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Seagrass beds play a vital ecological role by regulating climate, maintaining biodiversity, and sequestering carbon. Achieving accurate classification of seagrass beds across both time and space is of significant importance for efficient monitoring. This paper addresses the challenges of seagrass bed classification in remote sensing imagery and the difficulty of achieving temporal and spatial transferability of classification model. Leveraging a long-term Landsat satellite image time series spanning from 1976 to 2022 in the Yellow River Delta, we enhance the DeepLab V3+ network and propose a cross-temporal-spatial domain seagrass bed transfer learning classification approach. We utilize selectively chosen images from a few years in partial regions for training, introduce freeze-training and-unfreeze-training during the model training phase, and then transfer the model to achieve cross-temporal and cross-temporal-spatial domain classification on images from other years and regions. Additionally, we discuss the factors influencing the classification results. The research results indicate that: (1)The method we proposed has shown good results, achieving classification accuracy of 83.17% and 80.99% for seagrass beds in both cross-temporal and cross-temporal-spatial domains. (2) The classification results indicate that this method achieves an average classification accuracy higher by 16.18% compared to networks such as UNet, PSPNet, HRNet, UNet++, SegFormer, etc. under cross-temporal conditions and higher by 17.56% under cross-spatiotemporal conditions for seagrass bed classification. (3) Based on the classification results, it is observed that from 1973 to 1986, the overall area of seagrass beds in the Yellow River Delta had an initial growth trend, with the largest seagrass bed area recorded in 1986, reaching 4281.289 hectares. Subsequently, the seagrass beds showed an overall decreasing trend, and by 2019 they had almost completely disappeared. The cross-temporal-spatial domain seagrass bed transfer learning classification method proposed in this paper can provide valuable support for the monitoring, restoration, management, and utilization of seagrass ecosystems. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of training deep learning models for fire smoke scene detection from multi-sensor, multi-spectral satellite imagery, where spectral bands vary and training data is scarce, especially for new sensors. We introduce a novel cross-sensor transfer learning approach enhanced by spectral pattern extraction, notably aided by a deep learning (DL) module called Input Amplification (IA). Our contribution includes the creation of two datasets, Landsat_smk2770 and Sentinel2_smk, with varying spectral bands. We employ a model named IA_VIB_SD which incorporates IA and is designed specifically for fire smoke scene detection. We investigated the transferability of the Landsat_smk2770-trained IA_VIB_SD model to the Sentinel2_smk dataset using various transfer learning techniques, and compared the performance of the transferred model with the benchmark IA_VIB_SD model exclusively trained on pure Sentinel2_smk data. Our proposed transfer learning approach resulted in a transferred model with an average accuracy 5% higher than the benchmark model. Notably, Our proposed transfer learning approach outperformed existing transfer learning methods by more than 1% in terms of the accuracy, even when trained on a mere 10% of the Sentinel2_smk dataset. This research advances fire smoke scene detection from multi-sensor, multi-spectral satellite imagery. [ABSTRACT FROM AUTHOR]

With strong self-learning and data analysis capabilities, deep learning is essential in cloud detection. However, many high-quality samples are the key to deep learning cloud detection methods. Different satellite image cloud detection techniques need to select adequate representative and high-quality samples and make corresponding cloud masks, which not only requires professional knowledge but also consumes a lot of workforce and time. To improve the generalization ability of the deep learning cloud detection model and quickly apply it to the cloud detection of different satellite images, this paper proposes a deep learning cloud detection method based on spectral assimilation for multiple types of satellite images (SAUNetCD). Under the condition of using fewer deep learning data samples, the deep learning model is used to achieve automatic cloud detection of multiple satellite data. Taking Landsat 8 OLI, Landsat 9 OLI, GF-1 WFV, and Sentinel 2A as examples, this paper selects Landsat 8 OLI data as the source data of cloud detection. The experimental results show that the spectral assimilation method improves the generalization ability of the deep learning cloud detection model and improves the cloud detection accuracy by nearly 20%. It realizes the fast cloud detection application of different satellite images by deep learning. It provides an effective way for cloud detection of multiple types of satellite images. [ABSTRACT FROM AUTHOR]

Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth. [ABSTRACT FROM AUTHOR]

Atmospheric correction eliminates corruption in reflectance captured by satellite images due to atmospheric elements like gases, aerosols, and water vapours. Existing physics-based approaches employ radiative transfer models constructed using lookup tables computed for different atmospheric parameters. However, these approaches are computationally expensive and rely on estimates of parameters that are difficult to sense accurately. This paper proposes a deep learning model as an alternative to physics-based approaches. We present an end-to-end deep neural network trained on seasonally and spatially rich Landsat 8 satellite images without explicit atmospheric parameterization along with our analysis and its validation. We validate the model's effectiveness vis-a-vis Landsat 8's Land Surface Reflectance Code – LaSRC results in $RMSE \sim 0.042$ R M S E ∼ 0.042 , $SSIM \sim 0.97$ S S I M ∼ 0.97 , and correlation coefficient $r \sim 0.99$ r ∼ 0.99. For ground measurements by RadCalNet, the proposed model has an $RMSE \sim 0.053$ R M S E ∼ 0.053 , $SSIM \sim 0.90$ S S I M ∼ 0.90 , and $r \sim 0.88$ r ∼ 0.88. The results show that the model accurately predicts surface reflectance and correlates highly with reference data. [ABSTRACT FROM AUTHOR]

One of the limitations of remote sensing is the low spatial resolution of the open-access multispectral sensors, generating a mixture of spatial information. The mixed pixels can be modelled as the linear combination of fundamental components, called endmember, with a weighted contribution or abundance. The development of linear unmixing algorithms considering spatial and spectral information has recently increased. Some unmixing methods have relied on segmentation to integrate spatial data, and one of the most used is superpixel-based segmentation. However, previous work in superpixel-based unmixing focuses on using superpixels as uniform regions. Commonly, linear unmixing is used on hyperspectral imagery, and limited literature is found with multispectral images. This paper aims to propose a new preprocessing approach for multispectral linear unmixing called Superpixel Linear Independent Preprocessing. The proposed approach generates a set of candidates to endmembers based on spatial-spectral information; these are the input of traditional endmember extraction methods for multispectral unmixing. Experimental results show that the proposed preprocessing improves the performance of endmember extraction. [ABSTRACT FROM AUTHOR]

Block adjustment is a common way for achieving large-scale mapping from several ortho-rectified satellite images derived from wide-parallax pushbroom (WPP) imaging. However, block adjustment's accuracy can be affected by the amount of ground control points (GCPs), resolution of digital evaluation model (DEM) and unexpected and unpredictable imaging drift. In addition, block adjustment requires sensor model, which is hard to obtain and needs adjustment in real time. In view of these problems, this paper proposes a WPP blocks concatenation method for simplified regional mapping. First, considering distortions along different blocks, an adaptive-RPC model is proposed to ortho-rectified images. Then, a novel algorithm for stitching the ortho-rectified images is proposed, which consists of line-point consistency for matching neighbourhoods between two images, and a new energy function for suppressing artefact and distortions. Stitching experiments carried out on images from Orbita Satellite show that the stitching accuracy reaches 0.87 pixels, better than state-of-the-art methods, and absolute cumulative accuracy after stitching 10 orthoimages is only 3.99% higher than original one. Further simulation experiments with variable in satellite swing angles verify its robustness under complex scenes, while the estimation of cumulative error reiterates the necessity of performing orthorectification in prior. All results demonstrate that the proposed method can obtain accurate large regional mapping with distortions and artefact effectively suppressed. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inaccurate emissivity obtained by empirical or experimental measurements in traditional volcanic inversion methods, which leads to the discrepancy between the temperature inversion results and the actual temperature, this paper constructs a joint inversion model of volcanic temperature and emissivity, and solves the volcanic temperature and emissivity simultaneously from the image element scale.In order to obtain the two unknown parameters of volcanic temperature and emissivity simultaneously, the study image element is assumed to be a pure image element and Lambertian body, and based on the blackbody radiation law and energy conservation law, the volcanic radiation energy transfer equation under different irradiance conditions is established and solved jointly. In this paper, the study area is Halemaʻumaʻu crater in Hawaii, and the short-wave infrared band of Landsat 8 satellite day and night remote sensing images with an imaging interval of 36 hours are used as the study data, and the temperature and emissivity are calculated using the golden mean and parabolic interpolation method with the same emissivity of the same image element in both scenes and the temperature approximation as the constraints. The results are as follows: the temperature inversion of the study area has a banding phenomenon, the temperature of the lava lake is 800–1050 K and the emissivity is 0.5–0.7; the temperature of the crater surface outside the lava lake is 500–800 K and the emissivity is 0.7–1.0. The inversion results are consistent with the actual situation. The results show that the method can realize the simultaneous inversion of volcanic temperature and emissivity parameters on the image element scale, which provides a new method for the inversion of volcanic temperature and emissivity. [ABSTRACT FROM AUTHOR]

The early stages of forest fires are usually accompanied by large amounts of smoke, so smoke identification and concentration inversion can effectively warn and locate the fire point. To achieve smoke identification and concentration inversion, this paper first analysed the spectral differences between smoke and underlying background features based on the samples selected from Landsat 8/OLI fire data of Qiqian forest in Inner Mongolia, China, andNew South Wales, Australia. Then, the sensitive band of smoke identification was screened and the relative smoke concentration indices were established. Finally, a mathematical model between the indices and smoke concentration level was constructed and the best inversion model was established by accuracy test based on samples in each background. The results show that (1) the reflectance increases with increasing smoke concentration in B1-B4 bands under different backgrounds (vegetation, water, bare ground), and the reflectance was significantly different between different concentrations of smoke; (2) the smoke concentration inversion model form is y = kx + b with R2 > 0.8 (x denotes the smoke concentration level, y denotes the relative smoke concentration index), and the visible brightness index (VBI = B1 + B2 + B3 + B4) was the best relative smoke concentration index under different background; (3) the model's accuracy was tested using each background smoke sample with a known level of smoke concentration, and the inversion accuracy was above 90%. The results show that the VBI can effectively reflect the spectral variation between different smoke concentrations and background features; the variation is closely related to smoke concentration which determines that the mathematical correlation model between VBI and relative smoke concentration can be established; the model is accuracy and can be used for the relative smoke concentration inversion of forest fire cases in different study areas. [ABSTRACT FROM AUTHOR]

Himawari-8, a geostationary satellite, is equipped with the Advanced Himawari Imager (AHI) sensor, which offers significant advantages for forest fire monitoring. This study proposes a weighted contextual fire detection algorithm (AHI_WFDA) that can apply to the AHI sensor as a global fire detection algorithm. Unlike the traditional pass-by screening algorithms, the algorithm takes into account the characteristics of different bands and assigns different weights and corresponding thresholds to the test conditions based on the bands' sensitivity to fire. To validate the algorithm's performance, we tested it on fires in five target areas. We regard MODIS data as the ground truth data and it was used as the benchmark for comparison with the AHI_WFDA , the Himawari-8 official product WLF, and the traditional spatial contextual algorithm (the reproduced SEVIRI algorithm). The results show that the AHI_WFDA significantly reduces the commission error rate compared to the WLF product. While our algorithm's accuracy rate is not superior to the SEVIRI algorithm, it detects more fire incidents correctly. Compared with the MODIS active fire product, the AHI_WFDA's omission error rate is about 63%. In contrast, the relative commission error rate is about 12%, which is in line with the results of some previous studies. In addition, we conducted detailed verification of our algorithm's results with the support of the Landsat series and Sentinel-2 data. The results show that the algorithm in this paper can effectively exploit the fire detection capability of AHI sensors and provide a new idea for the subsequent algorithms. [ABSTRACT FROM AUTHOR]

Thermography is an efficient way of detecting the thermal problems of the roof as a major part of a building's energy dissipation. Thermal images have a low spatial resolution, making it a challenge to produce a three-dimensional thermal model using aerial images. This paper proposes a combination of thermal and visible point clouds to generate a higher-resolution thermal point cloud from roofs of buildings. For this purpose, after obtaining the internal orientation parameters through camera calibration, visible and thermal point clouds were generated and then registered to each other using ground control points. The roofs of buildings were then extracted from the visible point cloud in four steps. First ground points were removed using cloth simulation filter (CSF), and then vegetation points were eliminated by applying entropy and red-green-blue vegetation index (RGBVI). Geometric features and a segmentation step were considered to filter roofs from other parts. Finally, by combining visible and thermal point clouds, the generated point had a high spatial resolution along with thermal information. In the achieved results, the thermal camera calibration was performed with an accuracy of 0.31 pixels, and the thermal and visible point clouds were registered with an absolute distance of < 0.3 m. The experimental results showed an accuracy of 18 cm for automated extraction of building roofs and 0.6 pixel for production of a high-resolution thermal point cloud, which was five times the density of the primary thermal point cloud and free from distortions. [ABSTRACT FROM AUTHOR]

Land-cover classification especially global mapping has become a new trend in recent years. Traditional convolutional neural network (CNN) methods for land-cover classification are usually patch based and have problems of high computation cost and low efficiency, which hinder their wide applications when timely and accurately mapping land covers. Fortunately, methods based on the fully convolutional network (FCN) have achieved state-of-the-art performance on the semantic segmentation task, which provides a new possibility for efficient land-cover classification. Many works have been done for land-cover classification but are almost focused on very high-resolution remote-sensing images and few research works are implemented on medium-resolution images. In this paper, six representative state-of-the-art segmentation models including 'U'-shaped network (U-Net), fully convolutional DenseNet (FC-DenseNet), full-resolution residual network (FRRN), bilateral segmentation network (BiSeNet), DeepLab version 3 plus (DeepLabV3+), and pyramid scene parsing network (PSPNet) are selected to compare their performances on the land-cover classification of Land Remote-Sensing Satellite System)-5 satellite remote-sensing images. Based on the analysis of their performances, an improved model named atrous spatial pyramid pooling U-Net (ASPP-U-Net) is proposed for classification. Methods including support vector machine, patch-based CNN, and U-Net are also selected for comparison with the proposed model. Furthermore, to overcome the insufficiency of reference data when training deep models, an integration strategy based on two existing global land-cover products finer resolution observation and monitoring of global land cover of 2010 and global land-cover mapping at 30 m resolution is designed to produce reference data. Experimental results show that the encoder–decoder architecture especially U-Net is the most competitive network and is highly recommended for mapping land covers of medium-resolution images. The proposed ASPP-U-Net outperforms other compared methods not only in the classification accuracy but also in the inference time efficiency. In addition, it is advisable to use existing global land-cover products to produce reference data for segmentation models when the labelled datasets are insufficient. [ABSTRACT FROM AUTHOR]

Landsat images, which have fine spatial resolution, are an important data source for land-cover mapping. Multi-temporal Landsat classification has become popular because of the abundance of free-access Landsat images that are available. However, cloud cover is inevitable due to the relatively low temporal frequency of the data. In this paper, a novel approach for multi-temporal Landsat land-cover classification is proposed. The land cover for each Landsat acquisition date was first classified using a Support Vector Machine (SVM) and then the classification results were combined using different strategies, with missing observations allowed. Three strategies, including the majority vote (MultiSVM-MV), Expectation Maximisation (MultiSVM-EM) and joint SVM probability (JSVM), were used to merge the multi-temporal classification maps. The three algorithms were then applied to a region of the path/row 143/31 scene using 2010 Landsat-5 Thematic Mapper (TM) images. The results demonstrated that, for these three algorithms, the average overall accuracy (OA) improved with the increase in temporal depth; also, for a given temporal depth, the performance of JSVM was clearly better than that of MultiSVM-MV and MultiSVM-EM, and the performance of MultiSVM-EM was slightly better than that of MultiSVM-MV. The OA values for the three classification results, which use all epochs, were 70.28%, 72.40% and 74.80% for MultiSVM-MV, MultiSVM-EM and JSVM, respectively. In comparison, two other annual composite image-based classification methods, annual maximum Normalised Difference Vegetation Index (NDVI) composite image-based classification and annual best-available-pixel (BAP) composite image-based classification, gave OA values of 68.08% and 69.92%, respectively, meaning that our method produced a better performance. Therefore, the novel multi-temporal Landsat classification method presented in this paper can deal with the cloud-contamination problem and produce accurate annual land-cover mapping using multi-temporal cloud-contaminated images, which is of importance for regional and global land-cover mapping. [ABSTRACT FROM AUTHOR]

Globally, remote sensing is being used to monitor vegetation degradation in areas of concern. In recent years, drought and water shortages have caused significant degradation of the wetland vegetation in Zhalong Wetland of Heilongjiang province, China. This paper employed middle- and high-resolution Landsat images to construct a Linear Spectral Mixture Analysis of the wetland, with the end member extraction verified by feasibility analysis and with vegetation cover data extracted over nearly 30 years. By considering the problem of poor timing with middle- and high-resolution images, this paper proposes a phase-transform method that combines the time advantage of moderate-resolution spectroradiometer images with the spatial advantage of high-resolution Landsat imagery. Based on an intensity analysis model, the temporal and spatial characteristics of vegetation cover in the study area were analyzed using a time scale and the level of vegetation cover. The results show that (1) from 1985 to 2015, the vegetation cover showed an overall tendency to degrade, and (2) vegetation cover was extracted based on the phase transformation and linear spectral mixture models with an accuracy of 0.8628, which is higher than that of traditional remote sensing methods. Improving the prediction accuracy in vegetation transfer is of great theoretical value in relation to global climate change. [ABSTRACT FROM AUTHOR]

The extraction of water distribution is extremely useful in research and planning activities, including those associated with water resources, environments, disasters, local climates, and other factors. Remote-sensing images with moderate resolution have been the main data source due to the vast distribution of water and the high cost, access difficulty, and massive size of high-resolution images. Although some water indices and methods for water extraction have been proposed, there is still a lack of these resources to easily, accurately, efficiently, and automatically extract water. This paper focused on some improvements that mainly used the most traditional but also the newest Operational Land Imager (OLI) images in Landsat 8. This study first analysed the variation features of previous water indices. Secondly, taking the city of Beijing and its surrounding area as the experimental site, a spectral curve analysis was performed and a new water index was proposed. This index was compared to three typical indices. Thirdly, a new approach was proposed to accurately and easily extract water. It included four major steps: background partitioning, thresholding and preliminary segmentation, noise removal by patch size, and local region growth. Next, the stricter and more effective stratified random sampling method was used to test the accuracy. Then, we tested the generality of the proposed water index and extraction method using nine typical test sites from around the world and tried to simplify the workflow. Finally, this paper discusses threshold optimization issues, such as automatic selection and reduction of the number of thresholds. The results show that the normalized water index (NDWI), modified normalized water index (MNDWI), and normalized difference built-up index (NDBI) may fail in some situations due to the complex spectrum of the impervious surface class. Some shadow pixels were impossible to remove using only spectral analysis because both the digital number (DN) trends and values were similar to those of water. The proposed water index was easy and simple, but it corresponded better to water bodies. Additionally, it was more accurate and universal and showed greater potential for extracting water. This method relatively accurately and completely extracted various water bodies from plain city, plain country, and natural mountainous regions in many typical climate zones, eliminating interference caused by dark impervious surfaces, plants, sand, suspended sediments, snow, ice, bedrock, reservoir drawdown areas, shadows from mountains and buildings, mixed pixels, etc. The mean kappa coefficients were 0.988, 0.982, and 0.984 in plain city, plain country, and natural mountainous regions, respectively. This paper suggests that thresholds can be automatically determined by comparing the accuracy changes of different thresholds according to preselected sample and test points. Furthermore, the combined use of the maximum class square error method (also known as the Ostu algorithm) and the adaptive thresholding method exhibits great potential for automatic determination of thresholds in regions without many noises with higher water index values. In addition, water bodies could also be accurately extracted by setting these thresholds to fixed values based on the results at more test sites. [ABSTRACT FROM AUTHOR]

This paper presents a method to estimate the aboveground biomass (AGB) through the selection of different estimation methods based on numerous vegetation types (i.e., broadleaf forest, coniferous forest, shrub and grassland) at a regional scale. The proposed method is based on three models, namely, the stepwise regression, an improved back-propagation neural network (Improved BBPNN) model based on the Gaussian error function, and the support vector machine (SVM) technique, Meanwhile, Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) image data and geo-parameters are employed to select 68 feature variables and optimize 213 data samples. Our results reveal that, the stepwise regression method provides the best AGB estimation performance for broadleaf forests and coniferous forests, while the SVM technique shows the best performance for grasslands and shrubs. Different vegetation types should be selected for additional biomass estimation models that have been proven to enhance the biomass estimation. This study on the AGB not only promotes research on the net primary productivity (NPP), but also plays a key role in global carbon cycle research. [ABSTRACT FROM AUTHOR]

Mountain shadows in optical satellite images complicate the mapping of glacial lakes. Due to the rugged topography in periglacial alpine regions, many glacial lakes, especially smaller lakes, are partially shaded by mountain shadows in remotely sensed images. Shadows not only reduce the accuracy of lake mapping but also make changes in lake area hard to detect. In this paper, the characteristics of mountain shadows in remotely sensed imagery are explored, and their spatial relationships with regards to glacial lakes are modelled. Building on the previously developed Glacial Lakes Iterative Local Mapping (GLILM) method, a new water mapping approach is presented. The new method utilizes log-transformed spectral data and a normalized difference water index, NDWIblue, for delineating the boundaries of lakes within shadowed regions. The application of this approach is explored within the context of mapping lakes across space and time using Landsat images in the glacially dominated Tianshan mountainous of Central Asia. The results demonstrate that glacial lakes, both in sunlit and in shaded areas, can be mapped reliably, and that the results are useful for lake change analysis studies. [ABSTRACT FROM AUTHOR]

Recently, very high resolution (VHR) remote sensing (RS) images have become widely available. While a VHR RS image provides more detailed information of the imaged scene, inter-class variation is enhanced as well, which makes it a challenging task to accurately classify the image at pixel level. It has been found that spatial features of a VHR image derived from the spectral measurements can improve the accuracy of image classification. Recent studies have shown that deep convolutional neural networks (CNNs) are powerful to extract high-level spatial features for object recognition and image retrieval. However, few CNNs have been developed for the pixel-wise classification of RS image. Moreover, in order to obtain large-scale spatial features, CNNs can be unmanageably complicated. In this paper, we propose a novel deep CNNs based method for VHR image classification. Firstly, pixel level CNNs are applied to multiscale images generated from the original VHR RS image using the Gaussian pyramid method. The hierarchical multiscale spatial features are then resampled to the size of the raw image. Finally, feature fusion is conducted with the original spectral bands for spectral-spatial based classification, where a support vector machine (SVM) classifier can be employed. Experiments were conducted on the image of VHR aerial image in Vaihingen, Germany. Compared to using spectral features only or with extended morphological profiles (EMPs) for image classification, the proposed method performs better, and the best improvement of classification accuracy is as high as 13%. [ABSTRACT FROM AUTHOR]

Image segmentation is useful for mapping vegetation communities since it aggregates pixels into homogenous zones. Multiresolution segmentation, a bottom-up multi-scale segmentation algorithm, is one of the most widely used and successful algorithms. The algorithm requires image band weights, scale parameter, shape, and compactness values to be specified prior to segmentation. The persistent challenge is how to identify these values, which are often determined by trial and error experimentation. This paper aimed at determining pre-segmentation image analyses that can inform the specification of the multiresolution segmentation parameter values, in order to optimise mapping of savannah vegetation community boundaries on high spatial resolution images. The vegetation boundaries in the 56 land types of the Kruger National Park (KNP), South Africa, were targeted for segmentation. Sentinel 2 Multi-Spectral Instrument (MSI) imagery acquired during the peak vegetation vigour period was used. The high (10 m) spatial resolution green, red, and near-infrared bands were selected for use. The KNP’s large size required a mosaic of successively acquired image frames. Multiresolution segmentation was performed using eCognition Developer 9. Pre-segmentation principal component analysis (PCA) revealed that none of the bands had superior data dimensionality. Therefore, equal band weights of 1 were specified. The original 16-bit top-of-atmosphere reflectance data showed that the vegetation communities had high within-community variance. Texture enhancement of the reflectance data using 3 × 3 kernel variance showed that the vegetation communities were more distinguishable by texture than the untransformed reflectance data. A heterogeneity-based scale parameter value of 388, and a high shape (‘texture’) value of 0.9 at the expense of the reflectance (‘colour’) value of 0.1, were specified. The scale parameter value was determined by averaging object coefficient of variation values on the texture image bands. Pre-segmentation fieldwork revealed transitional vegetation boundaries, necessitating the low compactness value of 0.1 (i.e. high smoothness value of 0.9). From the segmented objects, samples per respective vegetation community were specified for k-nearest neighbour (kNN) classification (k = 1). All of the 100 land type polygons were subsequently delineated, with overall mapping accuracy of 86.2%. Fuzzy membership of border objects successfully reproduced the transitional vegetation boundaries. The successful delineation of the savannah vegetation communities indicated that pre-segmentation PCA and analysis of potential objects’ variance-based texture can provide guidance on parameter values to specify for the inherently iterative multiresolution segmentation. [ABSTRACT FROM AUTHOR]

The inter-annual Normalized Difference Vegetation Index (NDVI) time series derived from Landsat (INSL) has been applied to many fields. However, INSL may contain noise from many factors. At present, most researchers focus on denoising for NDVI time series derived from other datasets with shorter time intervals. In this paper, we proposed a denoising method for INSL by combining the best index slope extraction algorithm (BISE) with wavelet transform (WT). This method consists of two steps: (a) INSL are processed by BISE under a limiting condition to remove the significant noise; and (b) INSL obtained by step (a) are filtered by WT. Commonly used methods, including Savitzky-Golay filter (S-G) and BISE, were used as references to evaluate the proposed method. Using qualitative and quantitative comparisons and a change detection analysis, the proposed method was demonstrated to be reliable. [ABSTRACT FROM AUTHOR]

Woodland Caribou Rangifer tarandus caribou habitat mapping in northern Alberta, Canada is incomplete and imprecise, as habitat relationships are not fully understood, and land cover mapping is neither consistent nor complete. Spectral information obtained through remote sensing observations makes possible the evaluation of Woodland Caribou habitat use over large areas. With the use of Global Positioning System (GPS) collars fitted on the animals, correlations between satellite observations and Woodland Caribou locations were studied. This study examines Landsat 5 Thematic Mapper imagery of the Wabasca region of northern Alberta in relation to a dataset containing nearly 100 000 locations acquired from GPS radio-collars and approximately 5000 locations acquired from Very High Frequency (VHF) radio-collars. The intent was to determine if spectral signatures measured by remote sensing satellites could be related to habitat use or avoidance by caribou, and to use this knowledge to predict caribou habitat selection in areas where there are no collared animals. The analysis presented in this paper provides a new perspective for the analysis of large and complex habitat selection datasets and opens new opportunities for the implementation of better and more comprehensive conservation policies in boreal environments. The methodology and habitat associations presented in this paper are being used in northern Alberta to advance caribou conservation. [ABSTRACT FROM AUTHOR]

Permafrost is an important ground thermal condition that has significant biophysical and socio-economic impacts. In order to better understand the distribution and dynamics of permafrost, there is a need to map permafrost at high spatial resolution. This study is part of a research project that aims to model and map permafrost using remote sensing images and the Northern Ecosystem Soil Temperature (NEST) model in the central part of the Hudson Bay Lowland in northern Ontario, Canada. The study area is near the southern margin of permafrost region where permafrost exists only in isolated patches. In this study, we ran the NEST model from 1932 to 2012 using a climate data set compiled from station observations and grid data sources. The model outputs were then compared to field observations acquired during 2009–2012 at seven peat monitoring stations and two flux towers, which represent three major types of peatland in the study area (bog, fen, and palsa). The simulated soil temperatures at various depths show good agreement with the observations, and the simulated latent and sensible heat fluxes and net radiation are similar to the observations at the two flux towers. The model accurately shows the existence of permafrost only at palsa sites. Based on the general range of climate and ground conditions in this area, sensitivity tests indicate that the modelled permafrost conditions are sensitive to leaf area index, air temperature, precipitation, and soil texture. Therefore, the NEST model is capable of simulating ground temperature and permafrost conditions in where permafrost occurs only sporadically. A companion paper (part 2) uses the model with Landsat and Radarsat imagery to map the distribution and dynamics of permafrost in this area. [ABSTRACT FROM PUBLISHER]

A paper by Gonzalez-Alonso et al . (1997) presents a regression estimator with excellent relative efficiency values to estimate the area of barley in an intensive agricultural area in Lleida (Catalonia, Spain) using 1994 ground survey data and a 1993 Landsat TM classified image. The paper concludes that the regression estimator can be efficiently applied with ground data from the current year and a classified satellite image from a previous year. If these conclusions are applicable to large areas, using the same classified image for several years would greatly increase the cost-efficiency of the regression estimator. This Letter evaluates the suitability of the method in other areas of Spain. [ABSTRACT FROM AUTHOR]

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level. The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation. First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover. A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale. [ABSTRACT FROM PUBLISHER]

The paper describes the analyses of moisture parameters and biomass of vegetation cover. These include a relative moisture classification, a relative biomass classification, and the actual aboveground biomass estimate. All analyses were carried out by applying spectral indices and fuzzy classification for assessment. The satellite imagery from the Thematic Mapper (TM) (Landsat 5) and the Enhanced Thematic Mapper Plus (ETM+) (Landsat 7) was used as input data. Biomass indices derived from the satellite imagery were correlated with table data of average crop yields in 2007. Since the imagery used was from three periods of time (May 2001, July 2007, August 2000), it could have been assessed in terms of both time dependence and phenological phases. The monitored variables during the year were the relative moisture and vegetation biomass. The territory of interest was the Trkmanka River basin in southeast Moravia. Time dependence was obtained with the results of classifications in terms of varying phenological phases as well as dependence between the two characteristics. The data from the comparison of May and June revealed primarily their increase (a decrease occurred only on arable land), whereas those from the comparison of July and August revealed mainly their decrease. The two characteristics show almost linear mutual dependence except for some forest land. We processed map outputs showing the spectral indices used, changes in relative moisture and relative biomass, the actual biomass estimate, and a deviation from a linear dependence of the two characteristics under examination. The selected indices were processed into Geoscientific Model Development (GMD) – models that can be repeatedly run in Model Maker in Earth Resources Data Analysis System (ERDAS) IMAGINE. Together with the models of spectral indices, these were optimized for the sensor TM and ETM+. [ABSTRACT FROM PUBLISHER]

This paper presents findings of a land-use and land-cover (LULC) change mapping exercise conducted in Freetown, Sierra Leone. Nine LULC classes were mapped from multi-temporal Landsat data of 1974, 1986 and 2000. Special attention was given to the growth or otherwise of agricultural land in relation to other LULC classes. Conversion of one land-use/-cover type to the other was identified, and its effects discussed. Major conversions occurred between agricultural lands, grasslands, evergreen forest, built-up areas and barren land. Built-up areas increased by at least 140% between 1974 and 2000, suggesting a high urbanization rate. About 882 ha (27%) of agricultural lands in 1986 were converted to residential purposes in 2000, especially at the urban fringes, in response to an increase in population. Some 14% of evergreen forest was found to have been converted to agricultural land. These major conversions suggest a strong linkage between urbanization, agriculture and deforestation. [ABSTRACT FROM AUTHOR]

Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM+) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R2 = 43%; root mean square error (RMSE) = 97.4 m3 ha-1) than other ETM+ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R2 = 73.4%; RMSE = 170.13 ha-1) than other combinations of ETM+ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM+ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETM+ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETM+ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions. [ABSTRACT FROM AUTHOR]

Achieving food security in particular in Africa continues to pose a major challenge to humankind. It is clear that the future agricultural potential of Africa plays a critical role in meeting this challenge. Although crop yield can be estimated with a degree of reliability using a limited sample of ground observations, the exact crop acreage and the spatial distribution are rarely available. Even though remote sensing offers the ability to produce a rapid and up-to-date land use and land cover database for agricultural monitoring, there are only a few countries in Africa where higher resolution satellite data such as Landsat have been used for land cover map production at the national level. However a number of global products have been produced which contain information on cropland extent. This paper will outline a comparison of four sources of land cover data to determine which product is the most suitable for agricultural monitoring and for the subsequent development of a crop mask. The land cover products used are the Global Land Cover Map (GLC-2000), the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MOD12V1), the SAGE (Center for Sustainability and the Global Environment) and the AFRICOVER dataset from the Food and Agriculture Organisation (FAO). Both the GLC-2000 and MODIS land cover products are at a resolution of 1 km2 while AFRICOVER, based on visual interpretation of 30-m resolution Landsat images, is available at a much finer resolution. The four land cover products are first aggregated to the same resolution so that they can be compared. The legend categories of the four land cover products in this study are reconciled using the method developed in Fritz and See [Fritz, S. and See, L., 2005, Comparison of land cover maps using fuzzy agreement. International Journal of Geographic Information Science, 19, pp. 787-807.] and See and Fritz [See, L.M. and Fritz, S., 2005, A user-defined fuzzy logic approach to comparing global land cover products. In 14th European Colloquium on Theoretical and Quantitative Geography, 9-12 September 2005, Lisbon, Portugal.] that allows overlap between legend definitions to be taken into account. Once the legend definitions between the different land cover products are reconciled, the maps are then compared with national and sub-national statistics. Analysis is undertaken at both continental and national scales as well as sub-national for Sudan and Eritrea. The study generally concludes that MODIS has the tendency to underestimate cropland cover when compared with FAO statistics or AFRICOVER data, whereas GLC-2000 tends to overestimate cropland cover in those countries that are located at the northern transition zone of subtropical shrubland and semi-desert areas. In this area MODIS and SAGE show a relatively similar cropland distribution. Even though the SAGE database has been calibrated with national statistics, it does not perform better than the other two datasets overall, and has highlighted the fact that the SAGE data show regional weaknesses and should be replaced in certain regions by more recent datasets such as GLC-2000 and MODIS, or ideally by a hybrid product that combines the best of the three products, depending upon the region and country. [ABSTRACT FROM AUTHOR]

In this paper we propose a method for characterizing UHIs by using a non-parametric model. Landsat 5 and 7 images obtained over the city of Indianapolis were used for analysis of UHI over space and time. The images were subjected to kernel convolution implementing Gaussian bi-variate function for non-parametric characterization of the UHI. The convoluted images were then analyzed, for pattern over space and over time. It was found that the spread of the UHI in Indianapolis increased from 13.90 km to 21.63 km in the west-east direction and from 10.98 km to 15.90 km in the north-south direction from 1985 to 2000. The changes in the UHI from 1995 to 2000 were evident in all cardinal directions. The model aided in successfully characterizing UHI in terms of its location, spread, and the rate of increase, facilitating a clearer image of UHI through space and time. [ABSTRACT FROM AUTHOR]

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10-100 t ha-1 for woodlands, 1-100 t ha-1 for shrub sites, and 0.5-10 t ha-1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one-step approach (i.e. using all points) and 0.78 when using a two-step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two-step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two-step approach over the one-step approach, however, is not significant for the validation dataset, suggesting that the one-step approach is as good as the two-step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report. [ABSTRACT FROM AUTHOR]

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the effectiveness of using Landsat ETM+ data to identify the extent and severity of locust damage. Two cloud-free Landsat ETM+ images of the study area, taken before and after peak locust plague, were compared to determine the extent and severity of the 2002 locust plague according to the decrease of the Normalized Difference Vegetation Index (NDVI) derived from the two images. The results showed that the locust plague could be classified into heavy, moderate and light damage degrees based on the NDVI value decrease calculated by each pixel, which further evaluated its accuracy by extensive ground survey data. Locust plague can be identified with 98% and 92% accuracy for determining geographic extent and severity respectively using Landsat ETM+ data. [ABSTRACT FROM AUTHOR]

Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the grey value differences caused by non-surface factors such as different illumination, atmospheric, or sensor conditions. Such differences make it difficult to compare images using the same colour metric system. Image normalization is required to reduce the radiometric influences caused by non-surface factors and to ensure that the grey value differences between temporal images reflect actual changes on the surface of the Earth.A variety of image normalization methods, such as pseudoinvariant features (PIF), dark and bright set (DB), simple regression (SR), no-change set determined from scattergrams (NC), and histogram matching (HM), have been published in scientific journals. These methods have been tested with either Landsat TM data, MSS data or both, and test results differ from author to author. However, whether or not existing methods could be adopted for normalizing high resolution multispectral satellite images, such as IKONOS and QuickBird, is still open for discussion because of the dramatic change in spatial resolution and the difference of available multispectral bands. In this research, the existing methods are introduced and employed to normalize the radiometric difference between IKONOS and QuickBird multispectral images taken in different years. Some improvements are introduced to the existing methods to overcome problems caused by band difference and to achieve more stable and better results. The normalized results are compared in terms of visual inspection and statistical analysis. This paper examined whether or not existing methods can be directly adopted for image normalization with high resolution satellite images, and showed how these methods can be modified for use with such images. [ABSTRACT FROM AUTHOR]

Land-cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8-6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land-cover classification accuracies. [ABSTRACT FROM AUTHOR]

The current paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub-basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 min, considering rainfall duration, peak discharge and flooding in the Yeongdeok basin. [ABSTRACT FROM AUTHOR]

This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor‐independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi‐band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR‐2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS‐II/GLI. The latter three sensors had negligibly small errors. [ABSTRACT FROM AUTHOR]

Fourteen vegetation indices (VIs) were quantitatively evaluated using a Landsat TM dataset spanning 17 years over the San Luis Valley, Colorado, USA to find the best VI for use in sparsely vegetated arid regions. The VIs were compared to the known linear promotional effect of antecedent precipitation in an area of stable water tables. The r2 value of the linear regression for these indices was used to determine performance. The following published VIs were evaluated: ARVI, DVI, EVI, IPVI, MSAVI, MSAVI2, NDVI, NDVI*, PVI, RVI, SAVI, TSAVI, WDVI, and a new index introduced in this paper, NDVIoffset. NDVI* and NDVIoffset performed best of all the tested VIs, both with r2 = 0.77. Standard NDVI was a relatively poor performer (r2 = 0.37). The high performance of NDVIoffset is promising for use in regions or multi‐temporal sequences that lack saturated NDVI pixels. [ABSTRACT FROM AUTHOR]

The present paper deals with the relevance of spectral and textural indices to surficial deposits identification and mapping. The study area is located in the Cochabamba valley in central Bolivia. Potential of SPOT‐4, Landsat‐7 and Radarsat‐1 data were compared for surficial deposits mapping. Different spectral indices including NDVI (normalized difference vegetation index) and TSAVI (transformed soil adjusted vegetation index) and textural features (mean, standard deviation, angular second moment, entropy, etc.) were extracted from these datasets and used in the mapping process. The results showed that indices exhibit different level of sensitivities according to surficial deposit types. A discriminant analysis was conducted to extract the most significant indices, which were then used in a three‐step linear combination mathematical model to map surficial deposits. We achieved an overall classification rate of 74% using spectral data of land use map in step 1. By adding information on vegetation and soils obtained from evaluation of spectral indices, this rate was improved to 82% during step 2. Finally, it was further slightly improved to 83% by adding textural data in the final step. [ABSTRACT FROM AUTHOR]

In this paper, Landsat images are used in the mapping of transverse fault lineaments in Zagros, Iran, and the origin of the fault lineaments as the result of basement reactivation faults is examined using geomagnetic maps. The area under investigation is located in the SE part of the Zagros Fold Belt (ZFB) and was affected by influence of concealed faults, or fault lineaments, during the late Alpine Zagros orogeny. Image interpretation of geological structures such as curvilinear geometry of fold hinges, en echelon pattern of surficial lineaments and younger folds, and breached salt plugs, has been used to map the fault lineaments. Two sets of strike‐lateral transverse fault lineaments have been detected. The first set, NW‐trending, was identified by right lateral curved geometry of the main fold hinges, generation of younger folds, and en echelon pattern of surficial lineaments. The second set, NE‐trending, was verified based on left lateral displacement of the structures and alignment of salt plugs pierced anticline hinges. The placement of Upper Proterozoic salt plugs on the surface along the fault lineaments, together with their correlation with the magnetic‐driven lineaments, implies that the fault lineaments have a basement origin. The trend of these fault lineaments can be correlated with the trend of basement faults in the northern margin of the Arabian plate. The key result of this study is that the fault lineaments identified by integration of remotely sensed data and geomagnetic maps are generated as first order wrench faults by reactivation of the N–S‐trending basement faults. The thick Hormoz Salt formations overlaying the basement do not allow the fault lineaments to emerge at the surface. Therefore, they can be mapped through regional remote sensing and their basement origin can only be recognized by their correlation with magnetic faults. It is also concluded that satellite imagery can greatly contribute to the structural mapping of the ZFB and is therefore a valuable aid for oil and gas exploration in analogous tectonic environments. [ABSTRACT FROM AUTHOR]

This paper proposes a method to support sugarcane yield forecast using vegetation spectral indices, principal component analysis and historic yield data. The study area is located in the State of São Paulo, Brazil, and is divided into 11 production plots (108.75 ha), where sugarcane of the RB85 5536 variety is cultivated on red latossol (oxissol‐type) soil and flat topography. The data employed in the study include radiometrically and geometrically corrected enhanced thermatic mapper Plus (ETM+)/Landsat‐7 and ASTER/Terra images, acquired in June and April 2001, respectively, and historic harvest data measured in 2000 and 2001. The method comprises several steps: (a) enhancement of specific spectral responses of vegetation constituents; (b) reduction of spectral dimensions with prioritization of information and weighing of parameters related to foliar area; the data processed through these steps are reduced to a single image (the synthesis image), from which the mean DN (digital number) per cultivated area is calculated; (c) the image DNs are subsequently transformed into ton of stalk per hectare (t ha-1) through normalization, which requires knowledge of the previous year's yield for the cultivated production plots under analysis. Yield estimates using the method showed greater precision in comparison to the ubiquitous visual methods employed by the sugarcane agro‐industry in Brazil. Using factual productivity data of the year 2000 harvest only, the method achieved estimate errors varying between 2.57% and 5.65%, compared with 9.06% expected by the sugar factory; whereas using data from the year 2001 harvest, error margins were remarkably lower, around 1%. [ABSTRACT FROM AUTHOR]

In the present paper we have looked into the excessive occurrence of 255 standard fill value retrievals in Collection 4 MODIS LAI product over soybean areas from crop year 2001/2002 to 2004/2005, in Southern Brazil. The 255 standard fill value indicates that no leaf area index (LAI) retrieval was possible for the considered pixel. Time series of eight‐day composite LAI images (MOD15A2) and 16‐day composite NDVI images (MOD13Q1) were both compared with a soybean reference map derived from multitemporal Landsat images. The Land Cover Type 3 product (MOD12Q1) was also analysed to verify if the occurrence of those retrievals was related to misclassification of the broadleaf crops biome. Results indicated that the 255 standard fill value retrievals in Collection 4 LAI product were mainly related to soybean areas during peak growing season and occurred in every crop year we have studied. Eventual misclassification in the biome map was not the cause of those retrievals in the Collection 4 MODIS LAI product. [ABSTRACT FROM AUTHOR]

Lateritic palaeosols cover roughly 33% of Earth's land surface. In spite of this, laterite facies remain largely unmapped. This paper illustrates how spectral properties of clays and iron minerals guide the mapping of three laterite facies using remote sensing. Remotely sensed data and image processing techniques prove ideal for effective mapping of laterites over large areas and difficult terrain so that their economic importance can be evaluated. Several types of remote sensing data are considered and the most appropriate selected. A 700 km 2 area in Eritrea is used as a test site. A laterite facies map derived from remotely sensed data is presented for the first time. Methods of mapping are given using Landsat Thematic Mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Land Imager (ALI) data. Field evidence supports observations. Remote sensing methodology developed for the test site is applied to unvisited areas in Eritrea and Saudi Arabia. The results indicate that laterite facies maps can be produced easily and used to assess: ground water quality; viable agricultural land; areas prone to landslide; building resources, and potential base metal and gold mineralisation sites. Mapping laterite distribution in relation to topography also sheds light on the tectonic evolution of NE Africa, whilst the presence of laterites gives clues to ancient terrestrial climate. [ABSTRACT FROM AUTHOR]

This paper compares and contrasts predictions of forest height in Sitka spruce ( Picea sitchensis ) plantations based on medium‐resolution Landsat Enhanced Thematic Mapper Plus (ETM+), high‐resolution IKONOS satellite imagery and airborne Light Detection and Ranging (LiDAR) data. The relationship between field‐measured height and LiDAR height is linear and highly significant (R 2 0.98). Despite the difference in spatial resolution and radiometry between Landsat ETM+ and IKONOS multi‐spectral data, the strength of the relationship between field height and predicted height using the green spectral band was very similar, with R 2 values of 0.84 and 0.85, respectively. The inclusion of additional observations taken from the LiDAR data improved the strength of the relationship slightly for the Landsat ETM+ data (R 2 = 0.87), but did not change the relationship for the IKONOS data (R 2 = 0.84). Comparison of the height models derived from the satellite and LiDAR data shows that the optical models provide accurate predictions up to the point of forest canopy closure (10 m) in densely stocked plantations (>2000 stems ha -1 ); beyond this point, only the LiDAR model is able to provide a reliable estimate of forest height. The results demonstrate that the retrieval of forest structure information over the lifetime of a plantation forest is best achieved by the integration of satellite, airborne and ground‐based measurements. It is possible to use optical satellite data to identify forest stands that display unexpected growth characteristics, such as areas of high natural regeneration, poor or incomplete stocking. [ABSTRACT FROM AUTHOR]

This paper presents the development and testing of different integration procedures aimed at producing Normalized Difference Vegetation Index (NDVI) datasets with high spatial and temporal resolutions. In particular, two methods were considered, which both used higher spatial resolution information to increase the spatial detail of more frequent low resolution NDVI data. The first method was based on the extraction of per-class NDVI end-member values from low spatial resolution images (NOAA-AVHRR), followed by a combination of these values with a reference land cover map. Starting from the same end-members, the second method introduced further high resolution information by means of regression analyses applied per-pixel to the multitemporal dataset. The performances of the two methods were evaluated by means of two experiments carried out in a Mediterranean study area (San Rossore, central Italy). First, the images produced were compared on a per-pixel basis to higher spatial resolution Landsat TM/ETM+ data. Next, the NDVI temporal profiles were transformed into Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) estimates, which were combined with standard meteorological (radiation) data to compute forest Gross Primary Productivity (GPP). The validation of these GPP estimates was performed by comparison with the outputs of a more complex model of bio-geochemical processes (FOREST-BGC), already calibrated in the study area. The results of both tests indicated the greater accuracy of the more advanced per-pixel integration method, which was capable of reproducing NDVI data with high spatial and temporal details. [ABSTRACT FROM AUTHOR]

In this paper, we consider the application of Landsat 7 ETM+ imagery and Digital Terrain Models (DTM) for mapping glacial lineaments in a formerly glaciated area of Wales. A series of landform interpretation experiments were conducted using different false colour composites (FCC) and a DTM, both individually and in combination. The experiments indicate that the optimal FCC for detection of glacial lineaments at this scale includes the thermal-infrared (TIR) band. However, by performing the interpretation both in the TIR composite and in a visible and near-infrared (VNIR, bands 4, 3, and 2) draped over the DTM, we were able to substantially increase the number of lineaments identified. The glacial lineaments consist of fractured bedrock and are defined as rock drumlins. The landform system formed by these rock drumlins is characterized by convergent flow patterns at its head, attenuated lineaments, and abrupt lateral margins, which may indicate formation by fast-flowing ice. [ABSTRACT FROM AUTHOR]

This paper addresses semantic accuracy in relation to images obtained with remote sensing. Semantic accuracy is defined in terms of map complexity. Complexity metrics are applied as a metric to measure complexity. The idea is that a homogeneous map of a low complexity is of a high semantic accuracy. In this study, complexity metrics like aggregation index, fragmentation index and patch size are applied on two images with different objectives, one from an agricultural area in the Netherlands, and one from a rural area in Kazakhstan. Images are segmented first using region merging segmentation. Effects on metrics and semantic accuracy are discussed. On the basis of well‐defined subsets, we conclude that the complexity metrics are suitable to quantify the semantic accuracy of the map. Segmentation is the most useful for an agricultural area including various agricultural fields. Metrics are mutually comparable being highly correlated, but showing some different aspects in quantifying map homogeneity and identifying objects of a high semantic accuracy. [ABSTRACT FROM AUTHOR]

Understanding spatial patterns is a critical and under-explored aspect of remote sensing. This paper describes how multifractal theory can be applied to characterize these heterogeneous patterns in remotely sensed data as well as to determine the operational scale. An example based on the characterization of ulexite distribution on the world's largest salt flat (10 000 km²), located in Bolivia, using a binarized Landsat Thematic Mapper (TM) 4/7 ratio image, is used to describe the step-by-step procedure. Distribution was well characterized by the multifractal parameters and expressed through the f-α, τ-q and D-q relationships. Moments from (q=-2 to 5 showed a linear trend in scales from approximately 0.007 to 10000km². This implies that the attribute analysed could be measured at different scales, within defined boundaries, and up- and down-scaled using the multifractal parameters. In addition, the asymmetry shown by the f-α a spectrum indicates the presence of clusters with high probability of finding ulexite. and large areas where the mineral might be found in small patches. The areas with a high probability of finding ulexite were mapped to guide any future field survey. Using the maximum entropy concept, the operational scale to determine the mineral was obtained at 1062 m. [ABSTRACT FROM AUTHOR]

In this paper, we use logit models to classify data from Landsat Thematic Mapper (TM) among 23 land-cover and land-cover change classes. The logit model is LI simple statistical technique that is designed to analyse categorical data. Diagnostic statistics indicate that the logil model can classify remotely sensed data in a statistically significant fashion. User accuracies for individual landcover classes range between 50 and 92%, with an overall accuracy of 79%. To assess these accuracies, we compare them to those generated by a Bayesian maximum likelihood classifier. While the overall accuracies are similar, the accuracies for individual land-cover categories differ. These differences may be associated with the size of the training data for each land-cover class. There is some evidence that the logit models generate higher accuracies for land-cover categories for which relatively few training pixels are available. Finally, a comparison of classification results using a 12-band composite of the six reflective TM bands and their change vectors versus a six-band composite of the three Tasselled Cap bands and their change vectors indicates that the latter reduces classification accuracies. [ABSTRACT FROM AUTHOR]

The land cover cartography from the optical remote sensing data can be carried out by using the multi-texture classification. In the multi-texture classification, the textural parameters, computed from local or global statistics of the image, are used as the quantitative descriptors to be classified. However, computation of textural parameters from grey-tone values is not relevant for the feature extracted data used in this study. So, textural analysis has to be performed on a binary image. In this paper, we present a method leading to the mapping of the spatial organization, so-called 'macro-texture', of a unique thematic set. The particularity of the proposed method resides in the use of the concept of granulometric analysis of a binary image for defining the macro-texture parameters. The local granulometry density is computed on a window that is centred at each pixel of the binary image. This computation generates grey-tone images that will be classified by using the K-means method. The final map obtained from the classification of the pixels described by macro-textural parameters can be considered as a map of the density of the set under study. The method has been tested and evaluated on Satellite pour l'Observation de la Terre (SPOT) and Landsat Thematic Mapper (TM) data. The data cover the two arid regions characterized by different stages of ligneous covers. [ABSTRACT FROM AUTHOR]

Due to the performance limit of remote sensing systems, multispectral images have limited spatial resolution. Their spatial resolution can be improved by merging them with higher resolution image data. A fundamental problem frequently occurring in existing fusion processes, however, is the distortion of spectral information. This paper presents a spatially adaptive image fusion algorithm which produces visually natural images and retains the quality of local spectral information as well. High frequency information of the high resolution image to be inserted to the resampled multispectral images is controlled by adaptive gains to incorporate the difference of local spectral characteristics between the high and the low resolution images into the fusion. Each gain is estimated to minimize the l 2 -norm of the error between the original and the estimated pixel values defined in a spatially adaptive window of which the weights are proportional to the spectral correlation measurements of the corresponding regions. This method is applied to a set of co-registered Landsat 7 Enhanced Thematic Mapper (ETM)+ panchromatic and multispectral image data. The experimental results show that high resolution images can be synthesized by the proposed method, which successfully preserves spectral content of the multispectral images. [ABSTRACT FROM AUTHOR]