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In aquatic remote sensing, algorithms commonly used to map environmental variables rely on assumptions regarding the optical environment. Specifically, some algorithms assume that the water is optically deep, i.e., that the influence of bottom reflectance on the measured signal is negligible. Other algorithms assume the opposite and are based on an estimation of the bottom-reflected part of the signal. These algorithms may suffer from reduced performance when the relevant assumptions are not met. To address this, we introduce a general-purpose tool that automates the delineation of optically deep and optically shallow waters in Sentinel-2 imagery. This allows the application of algorithms for satellite-derived bathymetry, bottom habitat identification, and water-quality mapping to be limited to the environments for which they are intended, and thus to enhance the accuracy of derived products. We sampled 440 Sentinel-2 images from a wide range of coastal locations, covering all continents and latitudes, and manually annotated 1000 points in each image as either optically deep or optically shallow by visual interpretation. This dataset was used to train six machine learning classification models - Maximum Likelihood, Random Forest, ExtraTrees, AdaBoost, XGBoost, and deep neural networks - utilizing both the original top-of-atmosphere reflectance and atmospherically corrected datasets. The models were trained on features including kernel means and standard deviations for each band, as well as geographical location. A deep neural network emerged as the best model, with an average accuracy of 82.3% across the two datasets and fast processing time. Higher accuracies can be achieved by removing pixels with intermediate probability scores from the predictions. We made this model publicly available as a Python package. This represents a substantial step toward automatic delineation of optically deep and shallow water in Sentinel-2 imagery, which allows the aquatic remote sensing community and downstream users to ensure that algorithms, such as those used in satellite-derived bathymetry or for mapping bottom habitat or water quality, are applied only to the environments for which they are intended. • Global model to delineate optically shallow/deep water in Sentinel-2 imagery. • A deep neural network outperformed other machine learning models. • Use of information from the local neighborhood improved model performance. • The model is made publicly available as a Python package. • Application of the model will benefit the aquatic remote sensing community. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions. • EnMAP spaceborne imaging spectroscopy data free and open access since November 2022. • World-wide users interest for various science applications topics. • In-flight calibrated data with high data quality performance analyses confirmed. • Initial EnMAP scientific results in various key environmental fields demonstrated. • EnMAP provides testbed data for upcoming global hyperspectral operational missions. [ABSTRACT FROM AUTHOR]

For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis Program (EnMAP), has bridged this gap initiating a new era in global hyperspectral mineral mapping. The EnMAP satellite, operational since November 2022, covers the spectral range of 420 and 2450 nm in 224 bands, offering a spatial resolution of 30 m and a mean spectral sampling distance of 8.1 and 12.5 nm in the visible-near infrared and shortwave infrared regions, respectively. In this paper, we demonstrate the enhanced mapping capabilities of EnMAP using datasets acquired over the Reko Diq mining district, a cluster of Miocene porphyries located in Pakistan's Chagai Belt hosting an undeveloped world-class porphyry Cu-Au ± Mo deposit. The EnMAP's Level 2A data product was processed using the polynomial fitting technique to characterize the diagnostic absorption features of the alteration minerals in the Reko Diq porphyry system. This involved retrieving the minimum wavelength, depth, width, and asymmetry parameters for key absorption features and employing them interactively for mineral characterization. A diverse array of minerals were successfully mapped over the study area and validated by ground spectroscopy. This includes abundance/composition maps for white micas, chlorite, epidote, calcite, kaolinite, gypsum, jarosite, and ferric and ferrous iron minerals. The minimum wavelength of white mica was found to vary between 2195 and 2210 nm, with the shorter wavelengths (Al-rich) white mica occurring proximal to the known mineralized zones. The potassic alteration cores and the outer propylitic zones were identified by the ferrous iron and chlorite-epidote-calcite mineral maps, respectively. This study demonstrated the superiority of EnMAP hyperspectral data in delineating the alteration mineralogy and zonation pattern of porphyry copper systems. This capability can potentially contribute to the exploration of new deposits in exposed terrains worldwide. • The Reko Diq porphyry Cu deposit in Pakistan was studied in detail via EnMAP data. • EnMAP data revealed the zonation and physicochemistry of alteration minerals. • Spectroscopic-based method facilitated the characterization of alteration minerals. • The Al-rich white mica was found proximal to the known mineralized centers. [ABSTRACT FROM AUTHOR]

The composition of the plant canopy is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. Terrestrial ecosystem and biosphere models, which are used to predict how ecosystems are expected to respond to changes in climate, atmospheric CO 2 , and land-use change, require accurate representations of plant canopy composition at large spatial scales. The ability to accurately specify plant canopy composition is important because it determines the physiological and ecological properties of plants (such as leaf photosynthetic capacity, patterns of plant carbon allocation and tissue turnover, and the resulting dynamics of plant demography) that govern the biophysical and biogeochemical functioning of ecosystems. Traditionally, plant canopy composition has been represented in a coarse-grained manner within terrestrial biosphere models, with ecosystems being comprised of a single plant functional type (PFT). However, models are increasingly seeking to represent fine-scale spatial variation in plant functional diversity. In this study, we show how imaging spectrometry measurements can provide spatially-comprehensive estimates of within-biome heterogeneity in PFT composition across a functionally diverse and topographically heterogeneous ~710 km2 area in the Southern Sierra Mountains of California. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data at 18 m resolution from the recent HyspIRI Preparatory Mission (Hyperspectral InfraRed Imager) were used to estimate the sub-pixel fractions of seven PFTs represented in the ED2 terrestrial biosphere model: Shrub, Oak, Western Hardwood, Western Pine, Cedar/Fir, and High-elevation Pine, plus a Grass/NPV (Non-Photosynthetic Vegetation) fraction using Multiple Endmember Spectral Mixture Analysis (MESMA). ED2 is an individual-based terrestrial biosphere model capable of representing fine-scale sub-pixel ecosystem heterogeneity. Our results show that this methodology captures important elevation-related shifts in canopy composition that occur within the study area that are not resolved by existing multi-spectral land-cover products. These estimates modestly improved when the putative PFT endmembers considered in the mixture analysis were constrained using available geospatial data about the presence and absence of the PFTs in particular areas: the average RMSEs (root-mean-square errors) with the geospatially-constrained versus conventional method were 11.3% and 11.9% respectively, with larger reductions in the bias (i.e. mean error) in the abundances of Oak, Cedar/Fir, and Western Hardwood PFTs (ranging from 2.0% to 7.8%). At the hectare scale around four flux towers in the Southern Sierra Mountains, the overall composition improved from an RMSE of 18.2% (5.0–24.2% for individual PFTs) to an RMSE of 9.5% (3.3–13.2% for individual PFTs). Downgrading AVIRIS to 30 m resolution resulted in a reduction in accuracy of the constrained method to an RMSE of 12.7% (0–23.7%) with <1% change in the bias for all tree and shrub PFTs. Our results demonstrate that imaging spectrometry measurements from planned satellite missions such as HyspIRI, EnMAP (Environmental Mapping and Analysis Program), and HISUI (Hyper-spectral Imager SUIte) can provide important and much-needed information about fine-scale heterogeneity in the composition of plant canopies for constraining and improving terrestrial ecosystem and biosphere model simulations of regional- and global-scale vegetation dynamics and function. • Imaging spectrometry used to estimate fine-scale terrestrial ecosystem composition • Analysis conducted over an ecologically diverse California landscape • Sub-pixel PFT abundances accurate to an average RMSE of 11.3% and Bias −2.4 to 3.1%. • Method provides a framework for improved terrestrial biosphere model simulations. [ABSTRACT FROM AUTHOR]

Understanding rapid global change requires land cover maps with broad spatial extent, but also fine spatial and temporal resolution. Developing such maps presents a unique challenge, as variability in relationships between spectral characteristics (i.e., predictors) and a response variable is likely to increase with the size of the region across which a model is built and applied. Although most mapping approaches apply the same predictor-response relationships globally across the entire modeling region, learned relationships from one local area may be invalid for another when predicting across broad extents. Here, we adapted a spatial ensemble approach borrowed from species distribution modeling to land cover mapping, and evaluated whether the approach could faithfully represent spatial variation in relationships between land cover and spectral data. The spatiotemporal exploratory model (STEM) uses an ensemble of regression trees defined within spatially overlapping support sets, producing a broad-extent map that reflects variability at the spatial scale of each constituent support set. As test cases for reference maps, we used 30-m-resolution forest canopy and impervious surface cover layers from the 2001 U.S. National Land Cover Database (NLCD) for the states of Washington, Oregon, and California. When testing strategies for support set size and sampling intensity, we found that predictor-response relationships were strongest when individual components of the spatial ensemble were small and when sampling intensity was high. Compared to aspatial bagged decision tree and random forest models, we found that the STEM approach successfully captured variation in our source maps, both globally and at scales smaller than the modeling region. Leveraging the spatial structure of a STEM, we also mapped per-pixel spatial variation in prediction confidence and the importance of different predictor variables. After testing appropriate spatial ensemble and sampling strategies, we extended the predictor-response relationships gleaned from the 2001 source maps into a yearly time series based on temporally-smoothed spectral data from the LandTrendr algorithm. The end products were yearly forest canopy and impervious surface cover time series representing 1990–2012. Formal evaluation showed that our temporally extended maps also closely resembled NLCD maps from 2011. The aim of this research was to cultivate the implicit relationships between spectral data and a given map, not improve them, but as the need for time series maps produced at both broad extents and fine resolutions increases, our results demonstrate that an ensemble of locally defined estimators is potentially more appropriate than conventional ensemble models for land cover mapping across broad extents. [ABSTRACT FROM AUTHOR]

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2 nm and 2445.5 nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30 m × 30 m at a swath width of 30 km , allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users. • EnMAP launched on April 1, 2022, and entered routine operations on November 2, 2022. • Satellite checked out and cycle of acquisition planning to product delivery confirmed. • In-flight spectral, radiometric and geometric calibrations performed and analyzed. • Fully-automatic image processing chain for three user product levels released. • Quality assessment and product validation of imaging spectroscopy data conducted. [ABSTRACT FROM AUTHOR]

Woody plant encroachment of eastern redcedar ( Juniperus virginiana L., hereafter referred to as “red cedar” ) into native grasslands in the U.S. Southern Great Plains has significantly affected the production of forage and livestock, wildlife habitats, as well as water, carbon, nutrient and biogeochemical cycles. However, time series of red cedar maps are still not available to document the continuously spatio-temporal dynamics of red cedar encroachment across landscape, watershed and regional scales. In this study, we developed a pixel and phenology-based mapping algorithm, and used it to analyze PALSAR mosaic data in 2010 and all the available Landsat 5/7 data during 1984–2010 with the Google Earth Engine (GEE) platform. This pilot study analyzed 4233 images covering > 10 counties in the central region of Oklahoma, and generated red cedar forest maps for 2010 and five historical time periods: the late 1980s (1984–1989), early 1990s (1990–1994), late 1990s (1995–1999), early 2000s (2000–2004), and late 2000s (2005–2010). The resultant maps for 2010, the late 2000s, early 2000s, and late 1990s were evaluated using validation samples collected from Google Earth's high-resolution images and geo-referenced field photos. The overall (producer and user) accuracy of these maps ranged from 88% to 96% (88%–93%, and 96%–99%). The resultant maps clearly illustrated an increase in red cedar encroachment within the study area at an annual rate of ~ 8% during 1984–2010. These maps can be used to support additional studies on the driving factors and consequences of red cedar encroachment. This study also demonstrated the potential to trace the historical encroachment of red cedar into grasslands using time series Landsat images and PALSAR data. [ABSTRACT FROM AUTHOR]

Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km 2 with a standard error of 23,000 km 2 . This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be applied to other regions and potentially other crops in an operational mode. [ABSTRACT FROM AUTHOR]

Emissions estimates of anthropogenic methane (CH 4 ) sources are highly uncertain and many sources related to energy production are localized yet difficult to quantify. Airborne imaging spectrometers like the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) are well suited for locating CH 4 point sources due to their ability to map concentrations over large regions with the high spatial resolution necessary to resolve localized emissions. AVIRIS-NG was deployed during a field campaign to measure controlled CH 4 releases at the Rocky Mountain Oilfield Testing Center (RMOTC) in Wyoming, U.S. for multiple flux rates and flight altitudes. Two algorithms were applied to AVIRIS-NG scenes, a matched filter detection algorithm and a hybrid retrieval approach using the Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm and Singular Value Decomposition. Plumes for releases as low as 14.16 m 3 /h (0.09 kt/year) were consistently observed by AVIRIS-NG at multiple flight altitudes and images of plumes were in agreement with wind directions measured at ground stations. In some cases plumes as low as 3.40 m 3 /h (0.02 kt/year) were detected, indicating that AVIRIS-NG has the capability of detecting a wide range of fugitive CH 4 source categories for natural gas fields. This controlled release experiment is the first of its kind using AVIRIS-NG and demonstrates the utility of imaging spectrometers for direct attribution of emissions to individual point source locations. This is particularly useful given the large uncertainties associated with anthropogenic CH 4 emissions, including those from industry, gas transmission lines, and the oil and gas sectors. [ABSTRACT FROM AUTHOR]

Programs which intend to maintain or enhance carbon (C) stocks in natural ecosystems are promising, but require detailed and spatially explicit C distribution models to monitor the effectiveness of management interventions. Savanna ecosystems are significant components of the global C cycle, covering about one fifth of the global land mass, but they have received less attention in C monitoring protocols. Our goal was to estimate C storage across a broad savanna ecosystem using field surveys and freely available satellite images. We first mapped tree canopies at 2.5 m resolution with a spatial subset of high resolution panchromatic images to then predict regional wall-to-wall tree percent cover using 30-m Landsat imagery and the Random Forests algorithms. We found that a model with summer and winter spectral indices from Landsat, climate and topography performed best. Using a linear relationship between C and % tree cover, we then predicted tree C stocks across the gradient of tree cover, explaining 87% of the variability. The spatially explicit validation of the tree C model with field-measured C-stocks revealed an RMSE of 8.2 tC/ha which represented ~ 30% of the mean C stock for areas with tree cover, comparable with studies based on more advanced remote sensing methods, such as LiDAR and RADAR. Sample spatial distribution highly affected the performance of the RF models in predicting tree cover, raising concerns regarding the predictive capabilities of the model in areas for which training data is not present. The 50,000 km 2 has ~ 41 Tg C, which could be released to the atmosphere if agricultural pressure intensifies in this semiarid savanna. In this study, we demonstrated the benefit of using high resolution imagery for regional tree cover and C analysis, increasing available training data when there is paucity of field data. [ABSTRACT FROM AUTHOR]

Agricultural field size is indicative of the degree of agricultural capital investment, mechanization and labor intensity, and it is ecologically important. A recently published automated computational methodology to extract agricultural crop fields from weekly 30 m Web Enabled Landsat data (WELD) time series was refined and applied to a year of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhance Thematic Mapper Plus (ETM +) acquisitions for all of the conterminous United States (CONUS). For the first time, spatially explicit CONUS field size maps and derived information are presented. A total of 4,182,777 fields were extracted with mean and median field sizes of 0.193 km 2 and 0.278 km 2 , respectively. The CONUS field size histogram was skewed; 50% of the extracted fields had sizes greater than or smaller than 0.361 km 2 , and there were four distinct peaks that corresponded closely to sizes equivalent to fields with 0.25 × 0.25 mile, 0.25 × 0.5 mile, 0.5 × 0.5 mile, and 0.5 × 1 mile side dimensions. There were discernible patterns between field size and the majority crop type as defined by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) cropland data layer (CDL) classification. In general, larger field sizes occurred where a greater proportion of the land was dedicated to agriculture, predominantly in the U.S. Wheat Belt and Corn Belt, and in regions of irrigated agriculture. The results were validated by comparison with field boundaries manually digitized from Landsat 5 and Google-Earth high resolution imagery. The validation was undertaken at 48 approximately 7.5 × 7.5 km sites selected across a gradient of field sizes in each of the top 16 harvested cropland areas in U.S. states that together cover 76% of harvested U.S. cropland. Conventional per-pixel confusion matrix based measures that assess pixel level thematic mapping accuracy, and object extraction accuracy measures, were derived. The overall per-pixel crop field classification accuracy was 92.7% and the overall crop field producer's and user's accuracies were 93.7% and 94.9%. Comparing all the reference and extracted field objects, 81.4% were correctly matched and the extracted field sizes were on average underestimated by 1.2% relative to the reference field objects. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy has been used successfully to map species across diverse ecosystems, and with several spaceborne imaging spectrometer missions underway (e.g., Hyperspectral Infrared Imager (HyspIRI), Environmental Mapping and Analysis Program (EnMAP)), these data may soon be available globally. Still, most studies have focused only on single ecosystems, and many different classification strategies have been used, making it difficult to assess the potential for mapping dominant species on a broader scale. Here we compare a number of classification approaches across five contrasting ecosystems containing an expansive diversity of species and plant functional types in an effort to find a robust strategy for discriminating among dominant plant species within and across ecosystems. We evaluated the performance of combinations of methods of training data selection (stratified random selection and iterative endmember selection (IES)), spectral dimension reduction methods (canonical discriminant analysis (CDA) and partial least squares regression (PLSR)) and classification algorithms (linear discriminant analysis (LDA) and Multiple Endmember Spectral Mixture Analysis (MESMA)). Accuracy was assessed using an independent validation data set. Mean kappa coefficients for all strategies ranged from 0.48 to 0.85 for each ecosystem. Maximum kappa values and overall accuracies within each ecosystem ranged from 0.56 to 0.90 and 61–92%, respectively. Our findings show that both LDA and MESMA are able to discriminate among species to a high degree of accuracy in most ecosystems, with LDA performing slightly better. Spectral dimension reduction generally improved these results, particularly in conjunction with MESMA. Within each ecosystem, both the number and identities of functional types present, as well as the spatial distribution of dominant species, played a strong role in classification accuracy. In a pooled ecosystem classification, using CDA and LDA, we discriminated among 65 classes with an overall accuracy of 70% for the validation library, using only a 6% training sample. Our results suggest that a spaceborne imaging spectrometer such as HyspIRI will be able to map dominant plant species on a broader scale. [ABSTRACT FROM AUTHOR]

The upcoming hyperspectral satellite mission Environmental Mapping and Analysis Program (EnMAP) will provide timely and globally sampled imaging spectrometer data on a frequent basis. This will create unprecedented opportunities for a variety of environmental research fields and lead to manifold novel applications. These opportunities specifically apply to challenging environments, including heterogeneous urban landscapes. In this paper, we explored the potential of EnMAP data for quantifying land cover along the urban–rural gradient of Berlin, Germany. Land cover fraction maps from a simulated EnMAP scene at 30 m spatial resolution were derived based on support vector regression (SVR) combined with synthetically mixed training data. Results demonstrate that EnMAP imagery will be well suited for mapping impervious , vegetation and soil surface types according to the VIS framework. Moreover, EnMAP data will allow extending the VIS framework by more detailed sub-categories such as roof and pavement , or low vegetation and tree . However, we advise caution that spaceborne imaging spectrometer data of improved quality will not completely help to overcome well known phenomena of spectral similarity between materials and spectral confusion caused by the presence of shaded areas. To identify possible benefits and limitations of EnMAP data, comparisons to fraction maps derived from a higher resolution Hyperspectral Mapper (HyMap) image at 9 m spatial resolution and a multispectral Landsat ETM +-like image at 30 m spatial resolution were drawn. First, we demonstrate that both VIS and extended VIS mapping reveal similar accuracies compared to maps from spatially higher resolution data. Second, we illustrate the superiority of the higher spectral information content for improved and extended urban land cover mapping compared to multispectral data. Overall, this study provides important insights into the potential of spaceborne imaging spectrometer and specifically future EnMAP data for urban remote sensing. [ABSTRACT FROM AUTHOR]

A fundamental measure to characterize water quality is the attenuation coefficient for downward planar irradiance in the water column. In this paper we present an analytical forward model and develop an inversion scheme (2SeaColor) to retrieve the downwelling attenuation coefficients from remote sensing data. The forward formulation of 2SeaColor provides an analytical and stable solution of the direct and diffuse components of the radiative transfer equation. The common high turbidity of inland waters is accounted for in 2SeaColor by projecting its effect on the inherent optical properties (IOPs) using the similarity transform. The inversion scheme of 2SeaColor uses a novel approach to parameterize the spectral dependency of IOPs. In this parameterization 2SeaColor uses the curvature information of observed remote sensing spectra to deduce the spectral-shape of the total absorption coefficient. The forward model is verified using simulated data from the International Ocean Colour Coordinating Group (IOCCG data) and is compared with two existing models. Statistical analysis results of model verification show that the forward model is able to derive K d values with 3% accuracy (relative mean absolute error). Our solution provides an improvement on the accuracy of the estimated K d when compared to other models, and especially at larger depths. The inversion scheme is validated against three sets of data: IOCCG, field measured and satellite observed matchups. Field measurements were collected in the turbid Naivasha Lake, Kenya. The mean absolute relative error in the derived K d in all data sets did not exceed 15%, with the satellite matchup set resulting in the largest discrepancy. The developed model, 2SeaColor, is stable to noise up to 15% and consistently produces accurate results which make it an ideal candidate model for the future Ocean and Land Colour Instrument (OLCI) on board the Sentinel-3 mission. [ABSTRACT FROM AUTHOR]

Since the beginning of the 1960s an escalating deterioration of reed beds in parts of Europe has been often observed. Hence, the ‘reed die-back’ as it was later named, has been a phenomenon of great scientific interest and concern to conservationists worldwide and intensively studied by field ecologists. Imaging spectroscopy has frequently been employed for vegetation mapping, but this paper is the first explicit analysis of the spectral information content for reed and an assessment of the potential for detecting the areas affected by the reed die-back syndrome using hyperspectral data at the near infrared and the chlorophyll absorption spectral regions. Leaf reflectance spectra and photophysiological information were acquired using a Hand-Held ASD spectroradiometer, a portable fluorometer and a chlorophyll metre in-situ concurrently from leaf samples along a transect perpendicular to the lake shore of Central Europe's largest inland lake in terms of area, Lake Balaton in Hungary. A strong correlation between narrowband spectral indices and chlorophyll fluorescence parameters indicates the potential of hyperspectral remote sensing in assessing plant stability. Canopy hyperspectral data were collected from an airborne AISA Eagle sensor (400–1000 nm). An application of the findings from the field data analysis to airborne hyperspectral imagery reveals important information about reed condition at the study area. Y(II) values, regarded as a proxy of photosystem activity, have been calculated from high R 2 combination of spectral ratio 612/516 representing Fs and 699/527 representing Fm′. ETR values are estimated based on the calculated Y(II) and the spectral ratio 463/488 for Photosynthetically Active Radiation. This research underpins the development of methods for the spectral discrimination of reed patches affected by stress caused by environmental conditions, and subsequently the reed die-back syndrome. A comparison with empirical vegetation indices from the literature shows significantly higher R 2 values of the proposed indices for the specific application. We recommend spectral indices at leaf level for evaluating reed ecological status based on spectroscopic data to support the identification of affected vegetation patches and present R 2 maps that can aid the selection of indices tailored to specifications of remote sensors. [ABSTRACT FROM AUTHOR]

Issues like monitoring lake water quality, studying the role of lakes in the global carbon cycle or the response of lakes to global change require data more frequently and/or over much larger areas than the in situ water quality monitoring networks can provide. The aim of our study was to investigate whether it is feasible to estimate different lake carbon fractions (CDOM, DOC, TOC, DIC, TIC and pCO 2 ) from space using sensors like OLCI on future Sentinel 3. In situ measurements were carried out in eight measuring stations in two Swedish lakes within 2 days of MERIS overpass. The results suggest that the MERIS CDOM product was not suitable for estimating CDOM in lakes Mälaren and Tämnaren and was not a good proxy for mapping lake DOC and TOC from space. However, a simple green to red band ratio and some other MERIS products like the total absorption coefficient, turbidity index, suspended matter and chlorophyll-a were correlated with different carbon fractions and could potentially be used as proxies to map these lake carbon fractions (CDOM, DOC, TOC, DIC, TIC and pCO 2 ) from space. [ABSTRACT FROM AUTHOR]

Abstract: The Pathfinder sea surface temperature (SST) data set, from the Advanced Very High Resolution Radiometer (AVHRR), is an important climate data record for SST due to the longevity of the sensor series of potentially 38years (from 1981 to 2020). However high amounts of aerosol in the atmosphere, such as after a major volcanic eruption, lead to a cool bias on the sea surface temperatures retrieved from single view sensors such as the AVHRR. The Along Track Scanning Radiometer (ATSR) series of instruments provide a better climate-quality SST data record but are only available from 1991. The purpose of the work presented in this paper was to develop and test a method of reducing the bias in AVHRR Pathfinder SST during periods of high stratospheric volcanic aerosol. In this paper we take the ATSR record to be the reference SST for calculating the bias on the AVHRR Pathfinder SSTs during the Pinatubo eruption aerosol period (1991–1992). The analysis gives a bias of approximately −0.1K for AVHRR Pathfinder SSTs in periods with low atmospheric aerosol and a much larger bias (globally −0.44K in September to November 1991) in periods affected by volcanic aerosol. This paper shows the bias can largely be corrected using a multi-linear regression with aerosol optical depths from the Total Ozone Mapping Spectrometer (TOMS) during periods of high volcanic aerosol. The AVHRR Pathfinder minus ATSR SST bias, for grid-boxes where the correction with TOMS can be applied, is reduced from −0.53K to −0.12K in a three month period heavily affected by the stratospheric aerosol. This bias corrected value is close to the bias during low atmospheric aerosol loading periods. A three-way error analysis technique was used to evaluate the random retrieval uncertainty as measured by standard deviation on the SST datasets before and after bias correction. AVHRR Pathfinder SST error in the months after the Pinatubo eruption fell from 0.53K uncorrected to 0.20K after correction which shows the correction technique has value. [Copyright &y& Elsevier]

Abstract: There are many sea surface temperature (SST) datasets derived from various satellite instruments with different characteristics in terms of orbit, coverage, retrieval, calibration, accuracy and measurement type. This paper presents the results of a project to combine SSTs from two instruments: the Along-Track Scanning Radiometer (ATSR) SSTs and the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SSTs. The dual-view nature of the (A)ATSR series of radiometers enables high-quality sea surface temperatures to be retrieved. These SSTs are ideal for using as a reference in order to bias-adjust the AVHRR series of instruments. In addition a method to correct for aerosol effects in the AVHRR SSTs is employed using aerosol optical depth from the Total Ozone Mapping Spectrometer. The method enables positive characteristics of both datasets to be combined to provide a dataset of potentially high-quality, long time-series, large data coverage satellite sea surface temperatures for climate monitoring purposes. An AVHRR SST dataset at ¼ degree spatial resolution with associated systematic biases and uncertainties is produced for climate research covering the period 1985 to 1998. The method is used to produce improved AVHRR SSTs for the period 1991 to the present (when the ATSRs are available) and additionally the method can be used to estimate biases on AVHRR SSTs prior to the first ATSR launch (1985 to 1991). The bias-correction has the effect of reducing an observed cool bias in the AVHRR SSTs, relative to the Hadley Centre Sea-Ice and Sea Surface Temperature dataset (HadISST), especially in cases of high volcanic and Saharan dust aerosol. The time-series indicates that the bias correction scheme is effective during the 1990s at decreasing the cool bias in the AVHRR SSTs. The scheme is especially effective in times of high atmospheric volcanic aerosol levels such as the period following the Mount Pinatubo eruption in 1991. [Copyright &y& Elsevier]

Abstract: Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT''s between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67–0.74, Kappa: 0.41–0.52) and increased by 0.09–0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%–20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes. [Copyright &y& Elsevier]

Abstract: The floodplain forests bordering the Amazon River have outstanding ecological, economic, and social importance for the region. However, the original distribution of these forests is not well known, since they have suffered severe degradation since the 16th century. The previously published vegetation map of the Amazon River floodplain (Hess et al., 2003), based on data acquired in 1996, shows enormous difference in vegetation cover classes between the regions upstream and downstream of the city of Manaus. The upper floodplain is mostly covered by forests, while the lower floodplain is predominantly occupied by grasses and shrubs. This study assesses deforestation in the Lower Amazon floodplain over a ~30 year period by producing and comparing a historical vegetation map based on MSS/Landsat images acquired in the late 1970s with a recent vegetation map produced from TM/Landsat images obtained in 2008. The maps were generated through the following steps: 1) normalization and mosaicking of images for each decade; 2) application of a linear mixing model transformation to produce vegetation, soil and shade fraction-images; and 3) object-oriented image analysis and classification. For both maps, the following classes were mapped: floodplain forest, non-forest floodplain vegetation, bare soil and open water. The two maps were combined using object-level Boolean operations to identify time transitions among the mapped classes, resulting in a map of the land cover change occurred over ~30 years. Ground information collected at 168 ground points was used to build confusion matrices and calculate Kappa indices of agreement. A survey strategy combining field observations and interviews allowed the collection of information about both recent and historical land cover for validation purposes. Kappa values (0.77, 0.75 and 0.75) indicated the good quality of the maps, and the error estimates were used to adjust the estimated deforested area to a value of 3457 km2 ± 1062 km2 (95% CI) of floodplain deforestation over the ~30 years. [Copyright &y& Elsevier]

Abstract: Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated algorithm that exploits the spectral–temporal properties of summer Landsat time series stacks (LTSSs) to generate spatially explicit maps of forest and recent forest disturbances. VCT performs well in contiguous forest landscapes with closed or nearly closed canopies, but often incorrectly classifies large patches of land as forest or forest disturbance in the complex and spatially heterogeneous environments that typify fragmented forest landscapes. We introduce an improved version of VCT (dubbed VCTw) that incorporates a nonforest mask derived from snow-covered winter Landsat time series stacks (LTSSw) and compare it with VCT across nearly 25million ha of land in the Lake Superior (Canada, USA) and Lake Michigan (USA) drainage basins. Accuracy assessments relying on 87 primary sampling units (PSUs) and 2640 secondary sampling units (SSUs) indicated that VCT performed with an overall accuracy of 86.3%. For persisting forest, the commission error was 14.7% and the omission error was 4.3%. Commission and omission errors for the two forest disturbance classes fluctuated around 50%. VCTw produced a statistically significant increase in overall accuracy to 91.2% and denoted about 1.115million ha less forest (−.371million ha disturbed and −0.744million ha persisting). For persisting forest, the commission error decreased to 9.3% and the omission error was relatively unchanged at 5.0%. Commission errors decreased considerably to near 22% and omission errors remained near 50% in both forest disturbance classes. Dividing the assessments into three geographic strata demonstrated that the most dramatic improvement occurred across the southern half of the Lake Michigan basin, which contains a highly fragmented agricultural landscape and relatively sparse deciduous forest, although substantial improvements occurred in other geographic strata containing little agricultural land, abundant wetlands, and extensive coniferous forest. Unlike VCT, VCTw also generally corresponded well with field-based estimates of forest cover in each stratum. Snow-covered winter imagery appears to be a valuable resource for improving automated disturbance mapping accuracy. About 34% of the world''s forests receive sufficient snowfall to cover the ground and are potentially suitable for VCTw; other season-based techniques may be worth pursuing for the remaining 66%. [Copyright &y& Elsevier]

Abstract: Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN. [Copyright &y& Elsevier]

Abstract: The California sage scrub (CSS) community type in California''s Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15m resolution scanned color infrared aerial photography to 10m resolution SPOT 5 multispectral imagery and three image processing models – per-pixel, object-based, and spectral mixture models – were tested. An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions. [Copyright &y& Elsevier]

Abstract: Pixels, blocks of pixels, and polygons are all potentially viable spatial assessment units for conducting an accuracy assessment. We develop a population-based statistical framework to examine how the spatial unit chosen affects the outcome of an accuracy assessment. The population is conceptualized as a difference map created by overlaying a complete coverage reference classification and the target map being evaluated. The per-class areas of agreement and disagreement derived from this population are summarized by a population error matrix and accuracy parameters (e.g., overall, user''s and producer''s accuracies). The population and values of the accuracy parameters are strongly affected by the protocols implemented for the response design which include the choice of spatial unit, how within-unit homogeneity is addressed when assigning class labels, and the definition of agreement between the reference and map classification. Several complete coverage populations are used to illustrate how accuracy results are affected by the spatial unit chosen for the assessment and also to evaluate how spatial misregistration of the map and reference locations impacts accuracy results for different spatial units. The sampling design implemented for accuracy assessment does not change the population or values of the accuracy parameters, but the choice of spatial unit will influence decisions regarding use of strata and clusters in the design. A universally best spatial assessment unit does not exist, so it is critical to recognize how the population, values of the accuracy parameters, and sampling design are impacted by the choice of spatial unit. [Copyright &y& Elsevier]

Abstract: In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions. [Copyright &y& Elsevier]

Abstract: Effective land cover mapping often requires the use of multiple data sources and data interpretation methods, particularly when no one data source or interpretation method provides sufficiently good results. Method-oriented approaches are often only effective for specific land cover class/data source combinations, and cannot be applied when different classification systems or data sources are required or available. Here we present a method, based on Endorsement Theory, of pooling evidence from multiple expert systems and spatial datasets to produce land cover maps. Individual ‘experts’ are trained to produce evidence for or against a class, with this evidence being categorised according to strength. An evidence integration rule set is applied to evidence lists to produce conclusions of different strength regarding individual classes, and the most likely class identified. The only expert system design implemented currently within the methodology is a neural network model, although the system has been designed to accept information from decision trees, fuzzy k-means and Bayesian statistics as well. We have used the technique to produce land cover maps of Scotland using three classification systems of varying complexity. Mapping accuracy varied between 52.6% for a map with 96 classes to 88.8% for a map with eight classes. The accuracy of the maps generated is higher than when individual datasets are used, showing that the evidence integration method applied is suitable for improving land cover mapping accuracy. We showed that imagery was not necessarily the most important data source for mapping where a large number of classes are used, and also showed that even data sources that produce low accuracy scores when used for mapping by themselves do improve the accuracy of maps produced using this integrative approach. Future work in developing the method is identified, including the inclusion of additional expert systems and improvement of the evidence integration, and evaluation carried out of the overall effectiveness of the approach. [Copyright &y& Elsevier]

Abstract: Geothermal resources exist on the Pyramid Lake Paiute Tribal Lands (PLPTL) in northwestern Nevada. We compiled numerous indicators of these resources into a geographic information system along with concurrent investigative results. This effort required acquisition and analysis of spaceborne multispectral and airborne hyperspectral remote sensing data for early-stage geothermal exploration. We identified minerals such as alunite, kaolinite, and montmorillonite through analysis of hyperspectral data indicating regions of hydrothermally altered rock associated with areas of geothermal potential. Tertiary volcanic and granitic rocks also contain these indicator minerals. Quaternary environments displayed gypsum-bearing evaporite crusts that we postulate were deposited by sulfate-rich thermal waters. Throughout the PLPTL, tufa towers and tufa shoreline deposits are extensively distributed as remnants of paleo-lake Lahontan. Based on measured spectra of calcium carbonate, we mapped tufa towers elucidating the strike direction of associated faults. Additionally, we correlated remotely-derived maps of shoreline tufa deposits with climate-related changes in lake level. Our mapping results helped guide detailed exploration efforts to areas with the most geothermal potential. [ABSTRACT FROM AUTHOR]

Abstract: Understory vegetation is an important component in forest ecosystems not only because of its contributions to forest structure, function and species composition, but also due to its essential role in supporting wildlife species and ecosystem services. Therefore, understanding the spatio-temporal dynamics of understory vegetation is essential for management and conservation. Nevertheless, detailed information on the distribution of understory vegetation across large spatial extents is usually unavailable, due to the interference of overstory canopy on the remote detection of understory vegetation. While many efforts have been made to overcome this challenge, mapping understory vegetation across large spatial extents is still limited due to a lack of generality of the developed methods and limited availability of required remotely sensed data. In this study, we used understory bamboo in Wolong Nature Reserve, China as a case study to develop and test an effective and practical remote sensing approach for mapping understory vegetation. Using phenology metrics generated from a time series of Moderate Resolution Imaging Spectroradiometer data, we characterized the phenological features of forests with understory bamboo. Using maximum entropy modeling together with these phenology metrics, we successfully mapped the spatial distribution of understory bamboo (kappa: 0.59; AUC: 0.85). In addition, by incorporating elevation information we further mapped the distribution of two individual bamboo species, Bashania faberi and Fargesia robusta (kappa: 0.68 and 0.70; AUC: 0.91 and 0.92, respectively). Due to its generality, flexibility and extensibility, this approach constitutes an improvement to the remote detection of understory vegetation, making it suitable for mapping different understory species in different geographic settings. Both biodiversity conservation and wildlife habitat management may benefit from the detailed information on understory vegetation across large areas through the applications of this approach. [Copyright &y& Elsevier]

Abstract: A multi-component satellite remote sensing program is required to track the response of the world''s oceans, lakes and rivers to climate change. Central to this endeavor is the ability to detect the motions of internal waves, swell waves and currents and hence follow energy transport and exchange. However, the present methods of monitoring the motions of water bodies from space, such as those based on altimetry or gravity measurements, are geared mostly toward applications on large spatial scales, whereas the capacity to map the fine details of hydrospheric flows is limited. This paper describes a satellite-based method of detecting wave motion and surface currents at high (in principle metric) resolution that can be applied under specific circumstances in the confined environs of narrow sea straits, lakes and rivers and that compliments the use of other high-spatial resolution techniques such as those based on Synthetic Aperture Radar (SAR). The Along-Track Stereo Sun Glitter (ATSSG) technique makes use of images of water bodies that are separated in time by roughly 1min and are gathered in the forward-, nadir- and backward-looking directions by space-borne optical sensors performing along-track observations. When sensor viewing geometries lead to the presence of Sun glitter in these images, surface slicks, internal waves, swell waves and other phenomena become highlighted through the surface roughness changes they induce, since these in turn modulate the reflected glitter radiance. Measurement of the differential displacements between congruent sections of the surface roughness signatures present within image pairs of the stereoscopic sequence then enables internal wave or swell wave motions to be determined, while surface currents can be deduced if “passive” tracers of the flow in the form of surface roughness structures (such as slicks) are present. The application of the ATSSG technique described herein makes use of data acquired at a spatial resolution of 2.5m by the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) to provide the first along-track fine-scale synoptic mapping of the surface motions of individual components within a large group of internal waves and to generate supporting surface-current measurements. The PRISM data we employ were acquired to the south of the Lombok Strait (Indonesia), where highly energetic internal-wave growth takes place and where through-flow surface-current data, of the type derived by the ATSSG technique, can be of value in climate studies. [Copyright &y& Elsevier]

Abstract: Repeatable approaches for mapping saltcedar (Tamarix spp.) at regional scales, with the ability to detect low density stands, is crucial for the species'' effective control and management, as well as for an improved understanding of its current and potential future dynamics. This study had the objective of testing subpixel classification techniques based on linear and nonlinear spectral mixture models in order to identify the best possible classification technique for repeatable mapping of saltcedar canopy cover along the Forgotten River reach of the Rio Grande. The suite of methods tested were meant to represent various levels of constraints imposed in the solution as well as varying levels of classification details (species level and landscape level), sources for endmembers (space-borne multispectral image, airborne hyperspectral image and in situ spectra measurements) and mixture modes (linear and nonlinear). A multiple scattering approximation (MSA) model was proposed as a means to represent canopy (image) reflectance spectra as a nonlinear combination of subcanopy (field) reflectance spectra. The accuracy of subpixel canopy cover was assessed through a 1-m spatial-resolution hyperspectral image and field measurements. Results indicated that: 1) When saltcedar was represented by one single image spectrum (endmember), the unconstrained linear spectral unmixing with post-classification normalization produced comparable accuracy (OA=72%) to those delivered by partially and fully constrained linear spectral unmixing (63–72%) and even by nonlinear spectral unmixing (73%). 2) The accuracy of the fully constrained linear spectral unmixing method increased (from 67% to 77%) when the classes were represented with several image spectra. 3) Saltcedar canopy reflectance showed the strongest nonlinear relationship with respect to subcanopy reflectance, as indicated through a range of estimated canopy recollision probabilities. 4) Despite the considerations of these effects on canopy reflectance, the inversion of the nonlinear spectral mixing model with subcanopy reflectance (field) measurements yielded slightly lower accuracy (73%) than the linear counterpart (77%). Implications of these results for region-wide monitoring of saltcedar invasion are also discussed. [Copyright &y& Elsevier]

Abstract: Although cities, towns and settlements cover only a tiny fraction (<1%) of the world''s surface, urban areas are the nexus of human activity with more than 50% of the population and 70–90% of economic activity. As such, material and energy consumption, air pollution, and expanding impervious surface are all concentrated in urban areas, with important environmental implications at local, regional and potentially global scales. New ways to measure and monitor the built environment over large areas are thus critical to answering a wide range of environmental research questions related to the role of urbanization in climate, biogeochemistry and hydrological cycles. This paper presents a new dataset depicting global urban land at 500-m spatial resolution based on MODIS data (available at http://sage.wisc.edu/urbanenvironment.html). The methodological approach exploits temporal and spectral information in one year of MODIS observations, classified using a global training database and an ensemble decision-tree classification algorithm. To overcome confusion between urban and built-up lands and other land cover types, a stratification based on climate, vegetation, and urban topology was developed that allowed region-specific processing. Using reference data from a sample of 140 cities stratified by region, population size, and level of economic development, results show a mean overall accuracy of 93% (k =0.65) at the pixel level and a high level of agreement at the city scale (R 2 =0.90). [Copyright &y& Elsevier]

Abstract: Lack of data often limits understanding and management of biodiversity in forested areas. Remote sensing imagery has considerable potential to aid in the monitoring and prediction of biodiversity across many spatial and temporal scales. In this paper, we explored the possibility of defining relationships between species diversity indices and Landsat ETM+ reflectance values for Hyrcanian forests in Golestan province of Iran. We used the COST model for atmospheric correction of the imagery. Linear regression models were implemented to predict measures of biodiversity (species richness and reciprocal of Simpson indices) using various combinations of Landsat spectral data. Species richness was modeled using the band set ETM5, ETM7, DVI, wetness and variances of ETM1, ETM2 and ETM5 (adjusted R 2 =0.59, RMSE=1.51). Reciprocal of Simpson index was modeled using the band set NDVI, brightness, greenness, variances of ETM2, ETM5 and ETM7 (adjusted R 2 =0.459 RMSE=1.15). The results demonstrated that spectral reflectance from Landsat can be used to effectively model tree species diversity. Predictive map derived from the presented methodology can help evaluate spatial aspects and monitor tree species diversity of the studied forest. The methodology also facilitates the evaluation of forest management and conservation strategies in northern Iran. [Copyright &y& Elsevier]

Abstract: Uncertainties in burning efficiency (BE) estimates can lead to large errors in fire emission quantification (from 23% to 46%). One of the main causes of these errors is the spatial variability of fuel consumption within burned areas. This paper studies whether burn severity (BS) maps can be used to improve BE assessment. A burn severity map of two large fires in California was obtained by inverting a simulation model constrained by post-fire observations from Landsat TM imagery. Model output values of BS were validated against field measurements, obtaining a high correlation (R 2 =0.85) and low errors (Root Mean Square Error, RMSE=0.14) throughout a wide range of BS levels. The BS map obtained was then used to adjust BE reference values per vegetation type found in the area before the fire. The adjusted burning efficiency (BEadj) was compared to the burned biomass, which was estimated by subtracting vegetation indices from pre- and post-fire images. Results showed a high correlation for conifers (R 2 =0.75) and hardwoods (R 2 =0.73), and a moderate correlation (R 2 ∼0.5) for shrubs and grasslands. In general, for all vegetation types BEadj performed better (R 2 =0.4–0.75) than literature-based BE (R 2 <0.0001). This study demonstrates: (i) the consistency of the simulation model inversion for BS estimation in temperate ecosystems, and (ii) the improvement of BE estimation when the spatial variability of the combustion was quantified in terms of BS. [Copyright &y& Elsevier]

Abstract: Mapping plant species composition of mixed vegetation stands with remote sensing is a complicated task. Uncertainties may arise from similar spectral signatures of different plant species as well as from variable influences of prevailing plant states (e.g., growth stages, vigor, or stress levels). Despite these uncertainties, empirical approaches may often be able to take up the challenge. However, their performance is likely to be affected by the temporal variability of empirical relations between reflectance and plant species composition. To assess some aspects of this temporal variability, we performed a greenhouse study. Three mixed stands of grassland species were planted with defined spatial variation in species proportions. The canopy reflectance of these mixed stands was measured with a field spectrometer over a period of three months. Confounding external influences on plant states apart from maturation were minimized. The suitability of canopy reflectance and derivative reflectance to draw conclusions on differences in qualitative species mixtures between the stands was tested with a classification approach (Spectral Angle Mapper, SAM). Procrustean randomization test (PROTEST), which is to our knowledge new to the field of remote sensing, was applied in combination with Isometric Feature Mapping to quantify the spectral variation caused by within-stand spatial variation in species proportions. Model fits in both analyses increased with progressing plant development; further, utilization of derivative reflectance improved the model fits. Regardless of the within-stand variation, SAM enabled a successful discrimination of the three stands with an average overall accuracy of 85% (reflectance) and 92% (derivative reflectance). In PROTEST analysis, spatial variation in reflectance was successfully related to within-stand variation in species proportions. However, observed influences of variable growth stages and health states on these relations were considerable. The temporal variation of these relations (r =0.27–0.73 for reflectance and 0.48–0.73 for derivative reflectance) was quantified for the first time under controlled conditions. [Copyright &y& Elsevier]

Abstract: Debris cover on glacier boundaries critically impedes the global inventorying of glaciers and confounds most of the techniques developed for semi-automated mapping of glaciers. Debris on the glacier (referred as supraglacial debris) and that occurring outside the glacier boundaries (referred as periglacial debris) being derived from a common source, i.e. the valley rock, tend to have a similar spectral response in the reflection region which renders them mutually indistinguishable. However, there exist temperature differences between them. This aspect has been considered in this remote sensing based study to distinguish between the supraglacial and periglacial debris in a test area in the Chenab basin, Himalayas, by inclusion of thermal infrared (TIR) bands in remote sensing data processing. A synergistic multisensor approach for the delineation of debris-covered glacier boundaries is used here which integrates the inputs from thermal (TERRA-ASTER sensor) and optical (IRS-P6-AWiFS sensor) remote sensing data, multispectral classification techniques and the DEM derived geomorphometric parameters. The results of this study corroborate earlier findings on utilization of temperature differences as one of the parameters in glacial studies. The proposed synergistic approach therefore appears useful in accurate mapping of debris-covered glaciers in the Himalayan region. [Copyright &y& Elsevier]

Abstract: Linking intertidal processes to their natural patterns within a framework of coastal erosion requires monitoring techniques providing high-resolution spatio-temporal data from the scale of processes to this of patterns. The Scanning Hydrographic Operational Airborne LiDAR Survey (SHOALS) consists of a ubiquitous topographic and bathymetric LiDAR (Light Detection And Ranging) system that has become an important technology for generating high-resolution Digital Terrain Models (DTM) and Digital Surface Models (DSM) over intertidal landscapes. The objectives of this project are i) to highlight the capacity of SHOALS Topography and intensity data (Red and Near-InfraRed) to detect intertidal vegetation, ii) to assess the salt-marsh zonation, and iii) to map intertidal habitats and its adjacent coastal areas (Gulf of St. Lawrence, Canada). The study area was selected based on the spectrum of land cover types, encompassing beach, salt-marsh, arable farm and urban coastal environments. Surfaces constructed from the LiDAR survey included DSM, DTM, Normalized Surface Model (NSM), Digital Intensity Model for InfraRed (DIMI), Digital Intensity Model for Red (DIMR), and Normalized Difference LiDAR Vegetation Index Model (NDLVIM), derived from the two previous models. The correlation between the so-called NDLVI and the amount of salt-marsh vegetation, measured in situ, was 0.87 (p <0.01). Then, LiDAR-assessed salt-marsh ecological zonation allowed finding out intermediate and strong relationships between NDLVI and Topography (r 2 =0.89, p <0.038) and Topographic heterogeneity (r 2 =0.54, p <0.1394), respectively. Finally, NDLVI and Topography surfaces were classified using maximum likelihood algorithm into 17 classes, whose overall accuracy and kappa coefficient were 91.89% and 0.9088, respectively. These results support that (1) intertidal vegetation can be discriminated by NDLVI, (2) salt-marsh ecological zonation pattern, and (3) accurate coastal land cover maps can be satisfactorily generated from a single LiDAR survey using the NDLVIM and DTM approach. [Copyright &y& Elsevier]

Abstract: This paper presents a technique for mapping erythemally-weighted solar ultraviolet (EUV) radiation from satellite data in a tropical environment. A satellite-based EUV radiation model was formulated for calculating the EUV daily dose from satellite-derived earth-atmospheric albedo, total column ozone and other ground-based ancillary data. The earth-atmospheric albedo was obtained from a geostationary satellite (GMS5) while the total column ozone was retrieved from a polar orbiting satellite (EP/TOMS). The model was validated against the monthly average EUV daily dose from the measurements at four solar radiation monitoring stations located in the tropical environment of Thailand. The monthly average EUV daily dose calculated from the model was in reasonable agreement with that obtained from the measurement, with root mean square difference (RMSD) and mean bias difference (MBD) of 12.3% and 0.7%, respectively. After the validation, the model was used to calculate the monthly average EUV daily dose over Thailand employing an 8-year period of data from GMS5, EP/TOMS and other ancillary surface data. Values of the monthly average of EUV daily dose were presented as monthly and yearly maps. These maps reveal that the tropical monsoons have a strong influence on the EUV in this region. [Copyright &y& Elsevier]

Abstract: Evapotranspiration (ET), the sum of evaporation from soil and transpiration from vegetation, is of vital importance in the hydrologic cycle and must be taken into consideration in assessments of the water resources of any region. The MODerate resolution Imaging Spectroradiometer (MODIS) sensor offers a promising opportunity for estimating daily ET with a 1km spatial resolution, but is hampered by frequent cloud contamination or data gaps from other factors. In this study, 1) a stand-alone ET model was applied and tested during clear or partial cloudy sky conditions using MODIS-based inputs of land surface and atmospheric data and 2) meteorological simulations by using Four-Dimensional Data Assimilation (FDDA) system between MODIS and the 5th Generation Meso-scale Meteorological Model (MM5) was used in cloudy conditions to facilitate continuous daily ET estimates. The MODIS ET algorithm modified from Mu et al. (2007) is based on the Penman–Monteith equation and was applied to predict ET at flux measurement sites. This algorithm considers both the effects of surface energy partitioning processes and environmental constraints on ET. We devised gap-filling approaches for MODIS aerosol and albedo data that were identified as bottlenecks to determine retrieval rates of insolation and ET. MODIS-derived input variables (i.e., meteorological variables and radiation components) for estimating ET showed a good agreement with flux tower observations at each site. The retrieval rate of MODIS ET doubled at four flux measurement sites after gap-filling with negligible compensation was undertaken for accuracy. In spite of the high accuracy of MODIS-derived input variables, MODIS ET showed meaningful errors at the four flux measurement sites. These errors were mainly associated with errors in the estimated canopy conductance. During clear sky conditions, MODIS was used to calculate ET, while the MODIS-MM5 FDDA system provided input variables for the calculation of ET under cloudy sky conditions. The performance of the MODIS-MM5 FDDA system was evaluated by comparing ET based on MODIS, which showed a good agreement with the MODIS ET for various land cover types. Our results indicate that MODIS can be applied to monitor the land surface energy budget and ET with reasonable accuracy and that MODIS-MM5 FDDA has the potential to provide reasonable input data of ET estimation under cloudy conditions. [Copyright &y& Elsevier]

Abstract: Methane (CH4) is an extremely important greenhouse gas that has increased significantly in pre and post-industrial times. Due to CH4''s strong absorptions in the shortwave infrared (SWIR), the potential exists to use imaging spectrometers, such as the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), to map CH4 emissions. Here, we present research evaluating the ability of AVIRIS to map CH4 emitted by one of the largest marine geologic CH4 sources in the world, the Coal Oil Point seep field in the Santa Barbara Channel, California. To develop algorithms for detecting CH4 and to establish detection limits, initial analysis focused on simulated radiance spectra, calculated using Modtran 5.2 radiative transfer code that was parameterized to match scene conditions for a 6 August 2007 AVIRIS flight over the area. Model simulations included a range of surface albedos, variable column water vapor, and CH4 concentrations ranging from 1.7ppm (background) up to the equivalent of 2500ppm in the lower 20m of the boundary layer. A multistep CH4 detection algorithm was developed using Modtran simulations. First, surface albedo was estimated from the radiance at 2139nm. Next, albedo-specific radiance for background CH4 was used to calculate spectral residuals for CH4 above background. A CH4 index, C, calculated as the average residual between 2248 and 2298nm, showed high sensitivity to CH4, with minimal sensitivity to water vapor or surface albedo. Detection limits in simulations depended on CH4 concentrations and albedo, ranging from 990ppm for a 0.5% albedo surface, to as low as 18ppm for albedos higher than 22%. Application of this approach to the AVIRIS data demonstrated considerable potential for mapping CH4. Due to specular reflectance off of the ocean surface, albedo in the scene varied significantly, from less than 0.5% to over 30%. Strong CH4 anomalies were observed in the data acquired over the seep field, which produced large C values with spectral residuals consistent with CH4 and estimated radiance spectra that matched measurements. All strong anomalies were located in close vicinity to and downwind from known CH4 sources. However, contrary to simulated data, C was overly sensitive to albedo, restricting high confidence anomalies only to the brightest surfaces, and showing high frequency spatial variation throughout the AVIRIS image. CH4 concentration was overestimated by C, potentially due to a spectral trend in sea surface reflectance and/or the impact of diffuse light on dark surfaces (<1%) leading to the over-expression of CH4 absorptions. [Copyright &y& Elsevier]

Abstract: Tallgrass prairie in North America has been largely converted to croplands and cool season grasslands. In Missouri, only 0.5% of the tallgrass prairie remains in a form of isolated prairie islands. This study sought to delineate prairie-native warm season grass (WSG) from cool season grass (CSG) using five ASTER images acquired on 03/11/2008, 05/12/2007, 07/12/2007, 08/16/2007 and 10/19/2007. Temporal trajectories of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were extracted to examine temporal variation of WSG and CSG grasslands in a growth cycle. It was found that the spring–summer period revealed maximal spectral differences between these two grass types. CSG reached peak NDVI in May while WSG tended to have peak NDMI in July. NDVI was more useful than NDMI in summer–fall. The NDVI trends in this period varied with both phenology and grassland treatments such as haying and grazing, which provided additional information in WSG/CSG delineation. A hierarchical decision tree was developed to delineate WSG and CSG grasslands in a 3-tier process. The WSG lands including those publically conserved prairies were identified in the lower Osage Plain in southwest Missouri. The accuracies were about 80% and could be improved with more frequent satellite observations. The trajectory-based decision tree in this study provides a robust approach of long-term WSG/CSG mapping. [Copyright &y& Elsevier]

Abstract: Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) – direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm''s fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires >4 km2, and 93% of all reference fires >10 km2. The burned area was highly correlated (R 2 =0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ±36% for fires 50 km2 in size, improving to ±31% at a fire size of 100 km2. Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires >50 km2, and 66% results from fires >100 km2). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km2 spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm''s DB implementation enables time sensitive burned area mapping to support operational air quality forecasting. [Copyright &y& Elsevier]

Abstract: In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAI e ) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAI e were acquired in a Norway spruce forest in SE Norway. We used LI-COR''s LAI-2000® Plant canopy analyzer (“LAI-2000”) and hemispherical images (“HI”) for field based estimates of LAI e . ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAI e . We fitted regression models of LAI e against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer–Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R 2 values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAI e based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data. [Copyright &y& Elsevier]

The Sarfartoq carbonatite complex occurs in the southern West Greenland in a transition zone between Archaean gneiss complex to the south and a Proterozoic mobile belt to the north. The Sarfartoq carbonatite complex consists of a core zone composed of dolomite carbonatite and minor søvite (calcite carbonatite) surrounded by a fenite zone and a marginal zone of gneisses frequently altered due to hydrothermal activity. High spatial and spectral resolution imaging spectrometer data recorded by the HyMap imaging system were used to map lithology of the Sarfartoq carbonatite complex. A careful analysis of the spectral reflectance properties of the carbonatite lithology preceded the HyMap data analysis stage. The spectral reflectance measurements showed that the various lithologic units including dolomite carbonatite, søvite, fenite and the marginal alteration zone have distinct spectral reflectance characteristics. The analysis of the HyMap data was based on an unsupervised clustering algorithm, the Self Organizing Maps (SOM), for the mapping of the main lithology and a hierarchical tree for the mapping of sparsely occurring søvite rocks. Spectral mixture analysis was applied to map fractional abundances and compare with the SOM results. The resulting lithological map shows the spatial distribution of dolomite carbonatite, søvite, fenite with abundant carbonatite dykes (representing the outer core of the carbonatite complex), fenite and hematized gneiss (marginal alteration zone). The results compare well with the field data collected for the assessment of the mapping accuracy and due to the spatially contiguous nature of the hyperspectral data could be used to better map the outcropping carbonatite lithology. The spectral reflectance measurements and the mapping results provide information of petrological importance for the carbonatite core zone. [Copyright &y& Elsevier]

Degradation of vegetation and soils in drylands, sometimes called desertification, is thought to be a serious threat to the sustainability of human habitation, but maps of the extent and severity of degradation at country and global scales do not exist. Degraded land, by definition, has suffered a change relative to its previous condition set by its climate, soil properties, topography and expectations of land managers. The local net production scaling (LNS) method, tested here in Zimbabwe, estimates potential production in homogeneous land capability classes and models the actual productivity using remotely-sensed observations. The difference between the potential and actual productivities provides a map of the location and severity of degradation. Six years of 250 m resolution MODIS data were used to estimate actual net production in Zimbabwe and calculate the LNS using three land capability classifications. The LNS maps agreed with known areas of degradation and with an independent degradation map. The principal source of error arose because of inhomogeneity of some land capability classes caused by, for example, the inclusion of local hot-spots of high production and differences in precipitation caused by local topography. Agriculture and other management can affect the degradation estimates and careful inspection of the LNS maps is essential to verify and identify the local causes of degradation. The Zimbabwe study found that approximately 16% of the country was at its potential production and the total loss in productivity due to degradation was estimated to be 17.6 Tg Cyr−1, that is 13% of the entire national potential. Since the locations of degraded land were unrelated to natural environmental factors such as rainfall and soils, it is clear that the degradation has been caused by human land use, concentrated in the heavily-utilized, communal areas. [Copyright &y& Elsevier]

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA''s Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene''s illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode. [Copyright &y& Elsevier]

There is an urgent need for techniques to rapidly and periodically measure biodiversity and ecosystem services over large landscapes. Conventional vegetation classification and mapping approaches are based on discrete arbitrary classes which do not capture gradual changes in forest type (and corresponding biodiversity and ecosystem services values) from site to site. We developed a simple multi-date NDVI based Mahalanobis distance measure (called eco-climatic distance) that quantifies forest type variability across a moisture gradient for complex tropical forested landscapes on a single ecologically interpretable, continuous scale. This Mahalanobis distance, unlike other distance measures takes into account the variability in the reference class and shared information amongst bands as it is based on the covariance matrix, and therefore is most useful to summarize ecological distance of a pixel to a reference class in multi-band remotely sensed space In this study we successfully apply this measure as a surrogate for tree biodiversity and ecosystem services at two nested scales for the Western Ghats Bio-diversity hotspot. Data from over 500 tree-plots and forest type maps was used to test the ability of this remotely sensed distance to be a surrogate for abundance based tree-species compositional turn-over and as a continuous measure of forest type and ecosystem services. Our results suggest a strong but scale dependant relationship between the remotely-sensed distance measure and floristic distance between plots. The multi-date NDVI distance measure emerges as very good quantitative surrogate for forest type and is a useful complement to existing forest classification systems. This surrogate quantifies forest type variability on a single, continuous quantitative scale and has important applications in conservation planning and mapping and monitoring of hydrologic and carbon storage and sequestration services. [Copyright &y& Elsevier]

High spatial resolution images allow us to assess the spatial structure and variability of natural vegetation characteristics. In order to optimize quantitative image analysis, we looked for the optimal spatial resolution to map natural vegetation in a Mediterranean environment. The optimal spatial resolution was then related to the spatial characteristics of the scene and the mapped parameters. Based on the relation between airborne hyperspectral imagery and field dataset consisting of 227 measured plots of Leaf Area Index (LAI) and aboveground biomass, we found an optimal pixel size of 55 and 95 m for the mapping of LAI and aboveground biomass, respectively. The optimal spatial resolution we found is determined by the spatial structure of the mapped parameters on the one hand, and by the effects of shading patterns and gaps in the canopy on the other. The spatial properties of scene elements influencing the optimal spatial resolution can be detected effectively using average local variance functions and variograms, provided that these are studied at a wide spatial range. The use of an optimally sized mapping unit instead of the original 5 m pixels resulted in an improvement of mapping accuracy of 7 to 17%. It is therefore recommended that the image support used is considered carefully in all quantitative mapping projects based on remotely sensed imagery. [Copyright &y& Elsevier]

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil–vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H R for crops is too low, and that variability of H R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial... [Copyright &y& Elsevier]

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha. [Copyright &y& Elsevier]

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation is the first geostationary satellite instrument with all visual and infrared channels that are important for snow mapping. In this paper, we present an algorithm for deriving snow cover maps from SEVIRI data that makes use of the unique combination of adequate spectral resolution and very high frequency. The short interval of 15 min between images makes it possible to extend traditional spectral classification with a detection of changes between images. This improves the detection of clouds and cloud shadows in instantaneous images, because these often display more variation in time than the surface. It therefore allows a more accurate mapping of surface snow cover, as is shown by a validation of the results with ground observations and other satellite data. The accurate classification of each single image allows the generation of temporal composite snow maps in near real-time, which is for example of interest for numerical weather prediction models. When compared to many in situ measurements from the winter of 2005/2006, the accuracy of the algorithm is 95%. [Copyright &y& Elsevier]