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Terrestrial LiDAR has emerged as a promising technology for accurate forest assessment. LiDAR can provide a 3D image composed of a cloud of points using a rotary laser scanner. The point cloud data (PCD) contain information on the (x, y, z) coordinates of every single scanned point and a raw intensity parameter. This study introduces an algorithm for the automatic and accurate separation of the photosynthetic features of a PCD. It is shown that the recorded raw intensity is not a suitable parameter for the separation of photosynthetic features. Instead, for the first time, the absorption intensity is developed for every point based on its raw intensity and distance from the scanner, using proper scaling functions. Then, the absorption intensity is utilized as the only criterion for the classification of the points between photosynthetic and non-photosynthetic features. The proposed method is applied to the scans from a Canadian Boreal Forest and successfully extracted the photosynthetic features with minimal average type I and type II error rates of 5.7% and 4.8%. The extracted photosynthetic PCD can be readily used for calculating important forest parameters such as the leaf area index (LAI) and the green biomass. In addition, it can be used for estimating forest carbon storage and monitoring temporal changes in vegetation structure and ecosystem health. [ABSTRACT FROM AUTHOR]

Chronosequence changes among Tropical Dry Forests (TDFs) are essential for understanding this unique ecosystem, which is characterized by its seasonality (wet and dry) and a high diversity of deciduous trees and shrubs. From 2005 to 2021, we used two different airborne LiDAR systems to quantify structural changes in the forest at Santa Rosa National Park. Line- and shape-based waveform metrics were used to record the overall changes in the TDF structure. Based on a 16-year growth analysis, notable variations in height-related profiles were observed, particularly for RH50, RH100, and waveform-produced canopy heights. The results showed that Cy and RG have increased since the forests have been growing, whereas Cx has decreased. The decrease in Cx is because ground returns are lower when the canopy density i and canopy height increase. A positive relationship was observed between Cy and CH, RG, and RH100, particularly for the wet season data collected in 2021. These findings provide important insights into the growth dynamics of TDFs in Santa Rosa National Park and could inform future conservation efforts. [ABSTRACT FROM AUTHOR]

Secondary succession is defined as natural regeneration following complete forest clearance from anthropogenic or natural disturbances. Traditional strategies aimed to map and characterize secondary succession using remote sensing are usually based on deterministic approaches, where transitions between successional stages are not considered. These transitions represent rich environments between successional stages and play a key role in ecosystem regeneration. Here, we evaluate the use of the Full-waveform Airborne LiDAR to characterize changes in forest structure between the transition of early-to-intermediate and intermediate-to-late forest succession at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. The vertical forest structure was analyzed on twenty cross-sections selected between forest transitions previously mapped using machine learning; leaf area density (LAD) and waveform metrics were studied based on the waveform profile derived from twenty-seven plots distributed in different successional forest patches. Results suggest that LiDAR techniques can identify forest structure differences between successional stages and their transitions. The significance proves that transitions exist, highlights the unique transitional characteristics between intermediate and late successional stages and contributes to understanding the significance of inter-successional stages (transitions) in secondary dry forests. [ABSTRACT FROM AUTHOR]

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs. [ABSTRACT FROM AUTHOR]

Sensitivity of forest mortality to drought in carbon‐dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high‐frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site‐year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave‐one‐out cross‐validation R2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine‐scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high‐resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought. [ABSTRACT FROM AUTHOR]

Climate change will have considerable impact on the global economy. Estimates of the economic damages due to climate change have focused on the effect of average temperature, but not the effect of other important climate variables. Related research has not explored the sub-annual economic cycles which may be impacted by climate volatility. To address these deficits, we propose a flexible, non-linear framework which includes a wide range of climate variables to estimate changes in GDP and project sub-annual economic cycle adjustments (period, amplitude, trough depth). We find that the inclusion of a more robust set of climate variables improves model performance by over 20%. Importantly, the improved model predicts an increase in GDP rather than a decrease when only temperature is considered. We also find that climate influences the sub-annual economics of all but one province in Canada. Highest stressed were the Prairie and Atlantic regions. Least stressed was the Southeastern region. Our study advances understanding of the nuances in the relationship between climate change and economic output in Canada. It also provides a method that can be applied to related economies globally to target adaptation and resilience management. Climate change will impact the global economy. Here, the authors propose a framework to evaluate its effect on economies across multiple regional and temporal scales, and project decreased financial stability in a northern temperate economy. [ABSTRACT FROM AUTHOR]

Even though existing remote-sensing-based drought indices are widely used in many different types of ecosystems, their utility has not been widely assessed in tropical dry forests (TDFs). The aim of this study is to evaluate the performance of three remote-sensing-based drought indices, the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), for meteorological drought monitoring in TDFs using the moderate-resolution imaging spectroradiometer (MODIS) products. The correlation between the VCI, TCI, and VHI and multiple time scales of the Standardized Precipitation Index (SPI) (1, 3, 6, 9, 12, 15, 18, 21, 24 months) for each month (January to December) and each season (dry season, dry-to-wet season, wet season and wet-to-dry season) were conducted using the Pearson correlation analysis. We also correlated year-to-year changes of satellite-based drought indices with the changes of the in situ annual SPI (A_SPI) which provides annual information on the mean meteorological drought. The analysis reveals that the ability of these remote-sensing-based drought indices for meteorological drought monitoring varies with timing, and the TCI outperforms the VCI and VHI in terms of seasonal and annual scale. These remote-sensing indices performed well in monitoring meteorological drought in the dry season, poorly in the in the dry-to-wet season, and moderately in the wet season. The TCI performed best in monitoring meteorological drought in the wet-to-dry period, followed by VHI, whereas the VCI performed worst. All of these remote-sensing-based drought indices failed to detect drought in May during the green-up period and in September, October, and November when the water content in the root regions was abundant. Our results indicate that the evapotranspiration of TDFs is more sensitive than canopy greenness to detect meteorological drought. Results from this study increase the ability to provide real-time drought monitoring and early warnings of drought in TDFs. [ABSTRACT FROM AUTHOR]

The Guanacaste Province of Costa Rica is home to highly diverse forests which are under threat of degradation due to ongoing climatic changes. There is concern that increasing temperatures and changes in precipitation will force these forests outside of their optimal growth ranges leading to widespread degradation of forest productivity. The objectives of this study are, therefore, to project and assess climatic changes in Guanacaste and to build a relationship between these climatic changes and forest productivity with the goal of projecting productivity trends into the future. The ClimateSA model was used to project RCP4.5 and 8.5 scenarios from 2018 to 2080 and then assess these projections for the mean and extreme future conditions. Furthermore, the MODIS gross primary productivity (GPP) algorithm was used to build a relationship between forest productivity and the vapour pressure deficit scalar (VPD scalar) and project GPP alteration under future climatic scenarios both seasonally and annually. Results indicate that Guanacaste’s mean annual precipitation will stay within the historic levels for both the RCP4.5 and 8.5 scenarios. The monthly and annual temperatures increase in all projections. Results also indicate that the productivity-climate relationship follows a quadratic relationship between GPP and the VPD scalar. This quadratic relationship leads to areas with higher precipitation (high VPD scalar) experiencing an increase in GPP as they dry in the future. In drier areas (low VPD scalar), reduced precipitation will stabilize or decrease GPP. [ABSTRACT FROM AUTHOR]

Over the past 50 years, Costa Rica has experienced extensive landscape changes. It is, therefore, important to understand the historical changes that have occurred and how the forests will change in the future, which provides the objective of this study. To determine the historical deforestation trends and link them to the different biogeophysical and socioeconomic variables, forest maps from 1960 to 2013 were used in the Dinamica Environment for Geoprocessing Objects (Dinamica EGO) to create deforestation models for Costa Rica. The base model was built and analyzed for changes in landscape metrics such as patch size and distance between 1960 and 2013. After validation of the model's ability to replicate patterns, the landscape change was then projected to 2069. The historical model validated at 85% accuracy within 600 m for both the 2005-2013 and 1997-2005 iterations. All national future projections exhibited an increase in forest area, including the most extreme deforestation scenarios. Future projections are increasingly important given changes in the global socio-political structure, climatic change, and the ever-increasing globalization of capitalistic endeavors. The trajectory of the forest in Costa Rica can also serve as a way to track both these global pressures on the natural landscape, and as a proxy for how to manage deforestation in other similar political and geographic areas of the tropics. [ABSTRACT FROM AUTHOR]

The fields of tropical biology and conservation face significant transformations due to rapid technological developments in remote sensing. Other fields (e.g. Archeology) are experiencing this momentous change even more rapidly. In this article, we review some of the challenges that the fields of tropical biology and conservation face during the first quarter of the twenty-first century from the perspective of various remote sensing technologies, and discuss the transformations that they may bring to these disciplines. In addition, we review two emerging technologies driving paradigm changes in the nexus of ecology, remote sensing, and analytics: near-surface remote sensing and Wireless Sensor Networks. These two technologies, arising from the eScience paradigm, offer unique opportunities to integrate field observations at hyper-temporal and spatial resolutions that were not possible as recently as 5 years ago. [ABSTRACT FROM AUTHOR]

There is an increasing consensus on the importance of understanding ecosystem service (ES) provision in order to facilitate decision making and the sustainable management of Neotropical dry forests (NTDFs), yet research on the ESs provided by NTDFs is limited. We identified the main existing gaps and trends in the quantification of provisioning, regulating and supporting ESs in NTDFs. Systematic web-based searches showed that research has been increasing in recent decades in NTDFs, supporting greater ES knowledge and assessment. Carbon storage and biodiversity are the main subjects under study, while ESs relating to water and soil lack investigation. The most common approaches for assessing ES were fauna and plant inventories, carbon dynamics and ecological processes. [ABSTRACT FROM PUBLISHER]

Lianas are an important component of tropical forests, where they reduce tree growth, fecundity, and survival. Competition for light from lianas may be intense; however, the amount of light that lianas intercept is poorly understood. We used a large-scale liana-removal experiment to quantify light interception by lianas in a Panamanian secondary forest. We measured the change in plant area index ( PAI) and forest structure before and after cutting lianas (for 4 yr) in eight 80 m × 80 m plots and eight control plots (16 plots total). We used ground-based Li DAR to measure the 3-dimensional canopy structure before cutting lianas, and then annually for 2 yr afterwards. Six weeks after cutting lianas, mean plot PAI was 20% higher in control vs. liana removal plots. One yr after cutting lianas, mean plot PAI was ~17% higher in control plots. The differences between treatments diminished significantly 2 yr after liana cutting and, after 4 yr, trees had fully compensated for liana removal. Ground-based Li DAR revealed that lianas attenuated light in the upper- and middle-forest canopy layers, and not only in the upper canopy as was previously suspected. Thus, lianas compete with trees by intercepting light in the upper- and mid-canopy of this forest. [ABSTRACT FROM AUTHOR]

Agricultural expansion is causing deforestation in Minas Gerais, Brazil, converting savanna and tropical dry forest to farmland, and in 2012, Brazil’s Forest Code was revised with the government reducing deforestation restrictions. Understanding the effects of policy change on rates and locations of natural ecosystem loss is imperative. In this paper, deforestation in Minas Gerais was simulated annually until 2020 using Dinamica Environment for Geoprocessing Objects (Dinamica EGO). This system is a state-of-the-art land use and cover change (LUCC) model which incorporates government policy, landscape maps, and other biophysical and anthropogenic datasets. Three studied scenarios: (i) business as usual, (ii) increased deforestation, and (iii) decreased deforestation showed more transition to agriculture from shrubland compared to forests, and consistent locations for most deforestation. The probability of conversion to agriculture is strongly tied to areas with the smallest patches of original biome remaining. Increases in agricultural revenue are projected to continue with a loss of 25% of the remaining Cerrado land in the next decade if profit is maximized. The addition of biodiversity value as a tax on land sale prices, estimated at over $750,000,000 USD using the cost of extracting and maintaining current species ex-situ, can save more than 1 million hectares of shrubland with minimal effects on the economy of the State of Minas Gerais. With environmental policy determining rates of deforestation and economics driving the location of land clearing, site-specific protection or market accounting of externalities is needed to balance economic development and conservation. [ABSTRACT FROM AUTHOR]

Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling. [ABSTRACT FROM AUTHOR]

In this paper, we provide a comprehensive evaluation of the current regional literature associated with tropical dry forest (TDF) along three main axes: biodiversity, carbon and water conservation in the neotropics. Our analysis provides three key findings: (1) from the biodiversity point of view, we document that high degrees of endemism, diversity of plant life forms and ecophysiological types as key elements for their conservation across the Americas, (2) from the carbon storage point of view, we found that if the world's TDFs were restored they whole ecosystem would comprise 22 P of carbon in aboveground biomass. In the Americas alone, TDF restoration could potentially add 8 P of carbon to the potential total ecosystem carbon stock, (3) we found that at least 66 % of water reservoirs in the neotropics are located within dry forest ecoregions; therefore, the conservation of the quality of freshwater sources for human consumption in the neotropics is directly dependent on the sustainable management of TDF-dominated landscapes. In this paper, we stress that advocacy for conservation and sustainable management of TDF will benefit from integrating it's value in biophysical terms (e.g. carbon, biodiversity) with key ecosystem services and uses (e.g. its impact on hydrological dynamics and its potential for fostering ecotourism initiatives and entrepreneurship). By doing this, support and awareness could be wider and more effective in the long term, especially from national and local communities. [ABSTRACT FROM AUTHOR]

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest's biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (Hi) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB9 (Jenkins allometric equation) and HY9 (Lambert and Ung allometric equation) proved to be the best models (r2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r2 = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change: 1988, 1990, 2001, 2005, and 2011. Additionally, four pixel-based image comparisons were analyzed (i.e., 1988-1990, 1990-2005, 2005-2009, and 2009-2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance. [ABSTRACT FROM AUTHOR]

Aim: The general goal of this study is to investigate and analyze patterns of ecophysiological leaf traits and spectral response among life forms (trees, shrubs and lianas) in the Cerrado ecosystem. In this study, we first tested whether life forms are discriminated through leaf level functional traits. We then explored the correlation between leaf-level plant functional traits and spectral reflectance. Location: Serra do Cipo National Park, Minas Gerais, Brazil. Methods: Six ecophysiological leaf traits were selected to best characterize differences between life forms in the woody plant community of the Cerrado. Results were compared to spectral vegetation indices to determine if plant groups provide means to separate leaf spectral responses. Results: Values obtained from leaf traits were similar to results reported from other tropical dry sites. Trees and shrubs significantly differed from lianas in terms of the percentage of leaf water content and Specific Leaf Area. Spectral indices were insufficient to capture the differences of these key traits between groups, though indices were still adequately correlated to overall trait variation. Conclusion: The importance of life forms as biochemical and structurally distinctive groups is a significant finding for future remote sensing studies of vegetation, especially in arid and semi-arid environments. The traits we found as indicative of these groups (SLA and water content) are good candidates for spectral characterization. Future studies need to use the full wavelength (400 nm–2500 nm) in order to capture the potential response of these traits. The ecological linkage to water balance and life strategies encourages these traits as starting points for modeling plant communities using hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%–15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites. [ABSTRACT FROM AUTHOR]

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. [ABSTRACT FROM AUTHOR]

The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%-15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites. [ABSTRACT FROM AUTHOR]

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. [ABSTRACT FROM AUTHOR]

A single tropical plant species can harbour hundreds of endophyte species within its tissues. Beyond this, little is known about the relationship between endophyte colonization, leaf traits and spectral properties of leaves. We explore these relationships in Coccoloba cereifera, a plant well known for its symbiotic properties. Endophyte richness in C. cereifera was statistically correlated with leaf traits such as water content, the ratio of fresh weight/dry weight and polyphenol/leaf specific weight. Endophyte diversity was also related to spectral vegetation indices of chlorophyll content. The associations among endophyte diversity, leaf traits and spectral reflectance pose new questions and present new opportunities to better understand plant-fungal symbioses and related leaf optical properties. [ABSTRACT FROM AUTHOR]

There is increasing evidence that some natural disturbances are increasing in frequency and intensity with global change, but the effects of these changes on plant populations is poorly understood. It is estimated that for every 1°C increase in the summer mean minimum temperature, there is a 40% increase in hail damage. Severe hailstorms can cause large impacts on biological communities. In 2008, a strong hailstorm hit the speciose and endemic rupestrian vegetation in Serra do Cipó, Brazil. This event prompted us to record its effects on the narrowly distributed and threatened species Coccoloba cereifera (Polygonaceae). About 33 to 60% of the leaves on the 246 individuals surveyed were lost. The disturbance also influenced some of the physiological traits of C. cereifera, increasing the concentration of photosynthetic pigments (chlorophyll and carotenoid) and polyphenols in the leaves. The most pronounced increase of chlorophyll was in young leaves (ca. 60%). Carotenoid content increased by ca. 50% in all leaf ages, while polyphenols increased tenfold. Contrarily, the endophyte richness decreased drastically after the event (from 104 to 33 species), only 12% of similar species remain. The hail storm strongly influenced all variables evaluated in this study, i.e., structure, physiology, and associated fungi. These results show that hailstorm had a dramatic and immediate impact on C. cereifera and may also severely affect other endemic or threatened plant species. Therefore, it is imperative that we broaden our knowledge on global climate change impacts for the conservation of native species. [ABSTRACT FROM AUTHOR]

In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems. [ABSTRACT FROM AUTHOR]

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Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every end member can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping. [ABSTRACT FROM AUTHOR]

The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m3/hm2, 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns. [ABSTRACT FROM AUTHOR]

Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged. [ABSTRACT FROM AUTHOR]

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover. [ABSTRACT FROM AUTHOR]

In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images. [ABSTRACT FROM AUTHOR]

With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil's physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R2) of humidity (R2 = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R2 = 0.642, p < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R2 grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees' growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken. [ABSTRACT FROM AUTHOR]

We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season. [ABSTRACT FROM AUTHOR]

As the largest independent east–west-trending mountain in the world, Mt. Tianshan exerts crucial impacts on climate and pollutant distributions in central Asia. Here, the vertical structures of meteorological elements and black carbon (BC) were first derived at Mt. Tianshan using an unmanned aerial vehicle system (UAVS). Vertical changes in meteorological elements can directly affect the structure of the planet boundary layer (PBL). As such, the influences of topography and meteorological elements' vertical structure on aerosol distributions were explored from observations and model simulations. The mass concentrations of BC changed slightly with the increasing height below 2300 m above sea level (a.s.l.), which significantly increased with the height between 2300–3500 m a.s.l. and contrarily decreased with ascending altitude higher than 3500 m. Topography and mountain–valley winds were found to play important roles in the distributions of aerosols and BC. The prevailing valley winds in the daytime were conducive to pollutant transport from surrounding cities to Mt. Tianshan, where the aerosol number concentration and BC mass concentration increased rapidly, whereas the opposite transport pattern dominated during nighttime. [ABSTRACT FROM AUTHOR]

How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy of the UAV has become one of the key issues when we attempt to enable the UAV autonomy. In this paper, we propose a maneuver decision-making algorithm based on deep reinforcement learning, which generates efficient maneuvers for a UAV agent to execute the airdrop mission autonomously in an interactive environment. Particularly, the training set of the learning algorithm by the Prioritized Experience Replay is constructed, that can accelerate the convergence speed of decision network training in the algorithm. It is shown that a desirable and effective maneuver decision-making policy can be found by extensive experimental results. [ABSTRACT FROM AUTHOR]

Symptoms of root stress are hard to detect using non-invasive tools. This study reveals proof of concept for vegetation indices' ability, usually used to sense canopy status, to detect root stress, and performance status. Pepper plants were grown under controlled greenhouse conditions under different potassium and salinity treatments. The plants' spectral reflectance was measured on the last day of the experiment when more than half of the plants were already naturally infected by root disease. Vegetation indices were calculated for testing the capability to distinguish between healthy and root-damaged plants using spectral measurements. While no visible symptoms were observed in the leaves, the vegetation indices and red-edge position showed clear differences between the healthy and the root-infected plants. These results were achieved after a growth period of 32 days, indicating the ability to monitor root damage at an early growing stage using leaf spectral reflectance. [ABSTRACT FROM AUTHOR]

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups. [ABSTRACT FROM AUTHOR]

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement. [ABSTRACT FROM AUTHOR]

Background and Objectives: Increased frequency and intensity of drought events are predicted to occur throughout the world because of climate change. These extreme climate events result in higher tree mortality and fraction of dead woody components, phenomena that are currently being reported worldwide as critical indicators of the impacts of climate change on forest diversity and function. In this paper, we assess the accuracy and processing times of ten machine learning (ML) techniques, applied to multispectral unmanned aerial vehicle (UAV) data to detect dead canopy woody components. Materials and Methods: This work was conducted on five secondary dry forest plots located at the Santa Rosa National Park Environmental Monitoring Super Site, Costa Rica. Results: The coverage of dead woody components at the selected secondary dry forest plots was estimated to range from 4.8% to 16.1%, with no differences between the successional stages. Of the ten ML techniques, the support vector machine with radial kernel (SVMR) and random forests (RF) provided the highest accuracies (0.982 vs. 0.98, respectively). Of these two ML algorithms, the processing time of SVMR was longer than the processing time of RF (8735.64 s vs. 989 s). Conclusions: Our results demonstrate that it is feasible to detect and quantify dead woody components, such as dead stands and fallen trees, using a combination of high-resolution UAV data and ML algorithms. Using this technology, accuracy values higher than 95% were achieved. However, it is important to account for a series of factors, such as the optimization of the tuning parameters of the ML algorithms, the environmental conditions and the time of the UAV data acquisition. [ABSTRACT FROM AUTHOR]

Due to excessive human disturbances, as well as predicted changes in precipitation regimes, tropical dry forests (TDFs) are susceptible to meteorological droughts. Here, we explored the response of TDFs to meteorological drought by conducting temporal correlations between the MODIS-derived normalized difference vegetation index (NDVI) and land surface temperature (LST) to a standardized precipitation index (SPI) between March 2000 and March 2017 at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. We conducted this study using monthly and seasonal scales. Our results indicate that the NDVI and LST are largely influenced by seasonality, as well as the magnitude, duration, and timing of precipitation. We find that greenness and evapotranspiration are highly sensitive to precipitation when TDFs suffer from long-term water deficiency, and they tend to be slightly resistant to meteorological drought in the wet season. Greenness is more resistant to short-term rainfall deficiency than evapotranspiration, but greenness is more sensitive to precipitation after a period of rainfall deficiency. Precipitation can still strongly influence evapotranspiration on the canopy surface, but greenness is not controlled by the rainfall, but rather phenological characteristics when leaves begin to senesce. [ABSTRACT FROM AUTHOR]

In this paper, we address the retrieval of spatially distributed latent heat flux (λ E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring Super-Site, Costa Rica, in June 2016. The triangle method was used to derive λ E from the UAV imagery and the results were compared to λ E measurements of an eddy covariance system within the coincident eddy flux tower footprint. The tower footprint was derived using a two-dimensional parameterization model for flux footprint prediction. The comparisons with the flux tower measurements showed a mean relative difference of 10.98% with a slight overestimation of the UAV-based flux retrievals by nearly 7.7 Wm − 2 . The results are in good agreement with satellite-based retrievals, as provided by the literature, for which the triangle method was initially developed and mostly used so far. This study proved to be a promising approach for transferring the triangle method to UAV imagery in ecosystems such as tropical dry forests. With the presented approach, new details in spatially distributed latent heat flux estimates at ultra-high resolution are now possible, thereby potentially closing the gap in spatial resolution between satellites and flux towers. Even more, it allows tracing the latent heat flux from single trees at leaf level. Besides, this approach also opens new perspectives for the monitoring of latent heat fluxes in tropical dry forests. [ABSTRACT FROM AUTHOR]

Living walls are important vertical greening systems with modular prevegetated structures. Studies have suggested that living walls have many social benefits as an ecological engineering technique with notable potential for reconciliation ecology. Despite these benefits, there are currently no mature workflows or technologies for monitoring the health status and water stress of living wall systems. To partially fill the current knowledge gap related to water stress, we acquired thermal, multispectral, and hyperspectral remote sensing data from an indoor living wall in the Cloud Forest of the Gardens by the Bay, Singapore. The surface temperature (Ts) and a normalized difference vegetation index (NDVI) were obtained from these data to construct a Ts-NDVI space for applying the "triangle method". A simple and effective algorithm was proposed to determine the dry and wet edges, the key components of the said method. The pixels associated with the dry and wet edges were then selected and highlighted to directly display the areas under water-stress conditions. Our results suggest that the proposed algorithm can provide a reasonable overview of the water-stress information of the living wall; therefore, our method can be simple and effective to monitor the health status of a living wall. Furthermore, our work confirms that the triangle method can be transferred from the outdoors to an indoor environment. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) remote sensing platforms allow for normalized difference vegetation index (NDVI) values to be mapped with a relatively high resolution, therefore enabling an unforeseeable ability to evaluate the influence of the operation parameters on the quality of the thus acquired data. In order to better understand the effects of these parameters, we made a comprehensive evaluation on the effects of the solar zenith angle (SZA), the time of day (TOD), the flight altitude (FA) and the growth level of paddy rice at a pixel-scale on UAV-acquired NDVI values. Our results show that: (1) there was an inverse relationship between the FA (≤100 m) and the mean NDVI values, (2) TOD and SZA had a greater impact on UAV–NDVIs than the FA and the growth level; (3) Better growth levels of rice—measured using the NDVI—could reduce the effects of the FA, TOD and SZA. We expect that our results could be used to better plan flight campaigns that aim to collect NDVI values over paddy rice fields. [ABSTRACT FROM AUTHOR]

Central and South America tropical dry forest (TDF) is a water-limited biome with a high number of endemic species and numerous ecosystem services which has experienced a boom in research in the last decade. Although the number of case studies across these seasonal, water-limited, tropical forests has increased, there has not been a comprehensive review to assess the physiological variability of this biome across the continent and assess how these forests respond to climatic variables. Additionally, understanding forest change and resilience under climatic variability, currently and in the future, is essential for assessing the future extent and health of forests in the future. Therefore, the objective of this paper is to provide a literature review on the variability of TDF diversity and structure across a latitudinal gradient and to assess how these components respond to differences in climatic variables across this geographic area. We first assess the current state of understanding of the structure, biomass, phenological cycles, and successional stages across the latitudinal gradient. We subsequently review the response of these five areas to differences in precipitation, temperature, and extreme weather events, such as droughts and hurricanes. We find that there is a range of adaptability to precipitation, with many areas exhibiting drought tolerance except under the most extreme circumstances, while being susceptible to damage from increased extreme precipitation events. Finally, we use this climatic response to provide a commentary on the projected resilience of TDFs under climatic changes, finding a likelihood of resilience under drying scenarios, although model projections do not agree on the magnitude or direction of precipitation change. This review of quantitative studies will provide more concrete details on the current diversity that encompasses the TDF, the natural climatic ranges under which this ecosystem can survive and thrive, and can help inform future forest management practices under climate change scenarios. [ABSTRACT FROM AUTHOR]

The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future. [ABSTRACT FROM AUTHOR]

Commercially available autonomous photochemical reflectance index (PRI) sensors are a new development in the remote sensing field that offer novel opportunities for a deeper exploration of vegetation physiology dynamics. In this study, we evaluated the reliability of autonomous PRI sensors (SRS-PRI) developed by METER Group Inc. as proxies of light use efficiency (LUE) in an aspen (Populus tremuloides) forest stand. Before comparisons between PRI and LUE measurements were made, the optical SRS-PRI sensor pairs required calibrations to resolve diurnal and seasonal patterns properly. An offline diurnal calibration procedure was shown to account for variable sky conditions and diurnal illumination changes affecting sensor response. Eddy covariance measurements provided seasonal gross primary productivity (GPP) measures as well as apparent canopy quantum yield dynamics (α). LUE was derived from the ratio of GPP to absorbed photosynthetically active radiation (APAR). Corrected PRI values were derived after diurnal and midday cross-calibration of the sensor's 532 nm and 570 nm fore-optics, and closely related to both LUE (R2 = 0.62, p < 0.05) and α (R2 = 0.72, p < 0.05). A LUE model derived from corrected PRI values showed good correlation to measured GPP (R2 = 0.77, p < 0.05), with an accuracy comparable to results obtained from an α driven LUE model (R2 = 0.79, p < 0.05). The automated PRI sensors proved to be suitable proxies of light use efficiency. The onset of continuous PRI sensors signifies new opportunities for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. As such, this technology represents great value for the flux, remote sensing and modeling community. [ABSTRACT FROM AUTHOR]