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• Seasonal LAI change can be mapped and assessed by multitemporal VHR satellite imagery. • Texture-based features are better than spectrum-based features in mapping forest LAI. • Late spring VHR Pleiades imagery resulted in higher accuracy in mapping forest LAI. • Red band of Pleiades imagery is better than other bands in estimating forest LAI. The forest canopy leaf area index (LAI) is an important structural variable directly affecting functions and structures of terrestrial plant ecosystems. Optical remote sensing techniques may provide an alternative in estimating and mapping plant LAI. However, existing studies on using very high resolution (VHR) multitemporal satellite imagery to map and investigate the seasonal effect on plant LAI at a landscape scale are rare. In this study, we proposed to map and analyze forest LAI using four seasonal Pléiades images and corresponding in situ seasonal LAI measurements collected over a natural forest area in the City of Tampa, Florida, USA. A subset of selected spectral/textural features was used to develop pixel-based seasonal LAI regression models through a two-step feature selection procedure and a canonical correlation analysis. Finally, seasonal changes of the mapped LAIs were analyzed and assessed. Several interesting experimental results were created through this study, including: (i) a set of optimal texture parameters for extracting the 1st- and 2nd-order grey level statistical textures from the Pléiades imagery was determined as a window size 5 × 5, a direction 90° and pixel displacement 4 pixels; (ii) textural features were more important than spectral features in estimating and mapping forest LAI, and red band has a higher power in mapping forest LAI than other three multispectral bands; (iii) the late spring Pleiades image resulted in the highest accuracy for estimating and mapping forest LAI; and (iv) there exists a significant seasonal change of forest LAI in the study area and the seasonal effect on forest LAI mapping can be assessed by using the multi-seasonal VHR satellite imagery at a landscape scale. A novel significance for this study is that it is the first time using both spectral and textural information extracted from the multi-seasonal VHR satellite images to assess the seasonal effect on forest LAI mapping at a landscape scale. Since the experimental results and findings were derived from a relatively small study area, further testing and validation work is needed over different forest ecosystems at a landscape scale. [ABSTRACT FROM AUTHOR]

• Leaf chlorophyll of spruce stands retrieved through inversion of INFORM utilizing Sentinel-2 and RapidEye data. • The retrieved and measured leaf chlorophyll content from the Sentinel-2 data had higher R2 and NRMSE values. • Using the mean of the ten best solutions the retrieval error for both Sentinel-2 and RapidEye data decreased. • The retrieval error of leaf chlorophyll decreased when Sentinel-2 red edge bands were used in the inversion process. Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. [ABSTRACT FROM AUTHOR]

Highlights • Unmanned Aerial System (UAS) multispectral imagery was acquired at an Australian rangeland site. • Fractional vegetation cover was derived from the UAS imagery using three methods. • Object based classification achieved the best results at the 100 m plot scale (RMSE = 12–13%). • No method was able to accurately determine all three cover fractions at ultra-high resolution. Abstract Vegetation cover is a key environmental variable often mapped from satellite and aerial imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) based on multispectral imagery acquired from an Unmanned Aerial System (UAS) has several applications, including the potential to revolutionise the collection of field data for calibration/validation of satellite products. In this study, abundance maps were derived using three methods, applied to data collected in a typical Australian rangeland environment. The first method used downscaling between Landsat FVC maps and UAS images with Random Forest regression to predict bare ground, photosynthetic vegetation and non-photosynthetic vegetation cover. The second method used spectral unmixing based on endmembers identified in the multispectral imagery. The third method used an object-based classification approach to label image segments. The accuracy of all UAS FVC and Landsat FVC products were assessed using 20 field plots (100 m diameter star transects), as well as from 138 ground photo plots. The classification method performed best for all cover fractions at the 100 m plot scale (12–13% RMSE), with the downscaling approach only able to accurately predict photosynthetic cover. The downscaling and unmixing generally over-predicted non-photosynthetic vegetation associated with Chenopod shrubs. When compared with the high-resolution photo plot data, the classification method performed the worst, while the downscaling and unmixing methods achieved reasonable accuracy for the photosynthetic component only (12–13% RMSE). Multispectral UAS imagery has great potential for mapping photosynthetic vegetation cover in rangelands at ultra-high resolution, though accurately separating non-photosynthetic vegetation and bare ground was only possible when the data was scaled-up to coarser resolutions. [ABSTRACT FROM AUTHOR]

Highlights • Regression-based fraction mapping requires mixed spectra for training. • Library-based training generates mixed training data efficiently and easily. • The proposed method outperforms map-based training approaches. • The method supports the development of generic fraction mapping workflows. • Application on Sentinel-2 image data for Brussels yields good accuracies. Abstract An improved trade-off between resolution, coverage and revisit time, makes Sentinel-2 multispectral imagery an interesting data source for mapping the composition and spatial-temporal dynamics of urban land cover. To fully realize the potential of Sentinel-2′s high amount of available data, efficient urban mapping workflows are required. Machine learning regression is a powerful approach to produce subpixel land cover fractions from remote sensing imagery, yet it requires mixed spectra for model training for which the fractions of the land cover classes present in the pixel are known. Typically, this data is obtained by sampling spectra from the image to be unmixed, and corresponding land-cover fractions from higher-resolution land cover reference data, i.e. map-based training. We propose synthetic mixing of library spectra as an alternative for producing land cover fraction training data for regression modelling, i.e. library-based training. The approach is applied to a Sentinel-2 image of the city of Brussels (Belgium) and part of its urban fringe for mapping Vegetation, Impervious, and Soil (VIS) fractions at 20 m resolution. VIS fraction maps obtained with library-based training have mean absolute errors below 0.1 for all three surface types. The composition of these three key surface categories and their spatial distribution is well represented for the entire area in resulting maps. As a proof of concept, library-based training is compared with the map-based training approach. The more flexible library-based training not only achieves similar mapping accuracies, but in most cases, outperforms the map-based training approach in terms of bias and magnitude of error. The outcome of the research suggests that use of spectral libraries and synthetic mixing may provide an efficient modelling framework for regression-based mapping from Sentinel-2 imagery in operational contexts. [ABSTRACT FROM AUTHOR]

Highlights • We propose a 2D-to-3D strategy for invasive plants mapping in mountain area, using UAV-derived 2D digital ortho map (DOM) and 3D mesh model. • The texture features overcome the shortcomings of RGB-only image, and helps to better distinguish invasive vegetation. • A 3D mesh model could better visualize the final results, and show the distribution of invasive plants in the mountains. Abstract Invasive plants constitute one of the major causes of biodiversity loss, and its monitoring plays an important role in the management of coastal ecological systems. This study aimed to apply high precision 3D mesh-model and digital orthophoto map (DOM) derived from unmanned aerial vehicle (UAV) multi view images to monitor the invasive plants over coastal mountain region in Shenzhen, China. To overcome the limitations of RGB images, the Gray-level Co-occurrence Matrix (GLCM) features of images were analyzed and combined with spectral features to obtain the 2-dimentional distribution of invasive plants first, using an object-based image analysis technique. A fine analysis was then introduced to obtain a more accurate 3-dimentional distribution of invasive plant by combining 2-dimentional distribution of invasive plant and 3D mesh model. the results have shown that: (1) Although the UAV RGB image has limited spectral information, the low-altitude makes the spatial resolution very high, which can effectively enhance the effectiveness of the texture in mapping invasive plants, and finally achieved an overall accuracy of 93.25%. (2) The use of 3D mesh model, on the one hand, could significantly alleviate the impact of undulatory terrain over mountain area and improve the classification result; on the other hand, it could better visualize the final results, helping us more intuitively understanding the distribution of invasive plants. This study demonstrated the great potential of UAV-derived 3D mesh model in accurate natural resource management over mountain areas. [ABSTRACT FROM AUTHOR]

Highlights • We combined the classification of Caatinga physiognomies with soil information from geostatistical analysis. • Multi-temporal Sentinel-2 and LiDAR data were used for Random Forest in the Brazilian semi-arid Caatinga. • Classification of multi-temporal over mono-temporal data improved, especially for EVI, NDII-1, OSAVI and NDVI. • Gains in accuracy were observed for arboreous Caatinga after adding LiDAR metrics. • Gravels/pebbles over the soils, CEC and soil depth were factors of physiognomic variability. Abstract The Caatinga is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of Caatinga physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multi-temporal data over the mono-temporal observations. Gains in classification of arboreous Caatinga were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standard deviation of vegetation height. Overall, the most important metrics for classification were the VIs, especially the Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII-1), Optimized Soil-Adjusted Vegetation Index (OSAVI) and the Normalized Difference Vegetation Index (NDVI). The most important MSI/Sentinel-2 bands were positioned in the red-edge spectral interval. From PCA, soil attributes responsible for most of the data variance were related to soil fertility, soil depth and rock fragments in the surface horizon. The amounts of gravels and pebbles were factors of physiognomic variability with shrub and sub-shrub Caatinga occurring preferentially over shallow and stony soils. By contrast, arboreous Caatinga occurred over soils with total profile depth greater than 1 m. Finally, areas of sub-shrub Caatinga had greater values of cation exchange capacity (CEC) and water retention at field capacity than areas of arboreous Caatinga. The differences were statistically significant at 95% confidence level, as indicated by Mann-Whitney U tests. [ABSTRACT FROM AUTHOR]

Highlights • We applied the LTSS-VCT approach to detect changes in the temperate forest of the SMO in the State of Durango (Mexico). • The extent of disturbance estimated using the VCT algorithm was compared with the INEGI’s land use and vegetation maps. • We development a fragmentation analysis to characterize forest disturbance. • We found that about 34% of the temperate forest cover was lost for the period 1986-2012. • Forest pests and forest fires are the principal disturbance events, and the climate greatly influenced the incidence of disturbances. Abstract The Sierra Madre Occidental (SMO) is the longest continuous mountain complex in Mexico and is characterised by high species diversity and a high proportion of endemism. The rate of deforestation is high in Mexico, as in other megadiverse countries, and protection of the country’s biodiversity is a top priority. Quantification of changes in vegetation cover is essential for this purpose. Temporal information is required to enable classification of vegetation cover and change processes. In this study, the disturbances that occurred in the temperate forest of the SMO in the State of Durango (Mexico) during the period 1986–2012 were quantified using Landsat Time Series Stacks (LTSS) and the Vegetation Change Tracker (VCT) algorithm. The results obtained confirmed that land cover changes were detected with high overall accuracy (97.6%). In order to analyze the forest losses corresponding to the only official data available in Mexico, we retrieved land use and vegetation mapping (USV) data from the Mexican National Institute of Statistics and Geography (INEGI). The aridity index was established and fragmentation analysis was carried out in the study area, showing that forest pests and forest fires were the principal disturbance events in the SMO of Durango, and that the climate greatly influenced the occurrence of disturbances. The LTSS-VCT analysis revealed that for the period 1986–2012, about 34% of the temperate forest cover in the SMO in Durango was lost due to different types of disturbance, representing an annual rate of loss of forest cover of 1.3% and affecting 32,840 ha of land per year. The trend analysis of USV data showed very similar changes to those indicated by the LTSS-VCT analysis in terms of loss of temperate forest. However, differences were observed in regards to the absolute values of forest cover and vegetation loss, with analysis of the USV data indicating forest losses of 28% due to disturbances and an annual disturbance rate of 1%, affecting 49,940 ha of land per year. The LTSS-VCT approach proved efficient for mapping data on forest disturbance acquired by a medium spatial resolution (Landsat) sensor in the SMO in the State of Durango, providing satisfactory results and at low cost. [ABSTRACT FROM AUTHOR]

Fine-resolution land cover mapping at the regional or global scale is usually time-consuming and involves a lot of manual participation. This article proposes a novel and automatic land cover mapping approach called the SP atial-t E mporal spe C tral Lib rary (SPECLib), which can produce a land cover map with a 30-m spatial resolution. First, the SPECLib is developed by extracting reference spectra for uniform, typical samples of each land cover type by combining the GlobCover2009 land cover product and the 8-day composite MODIS Surface Reflectance Product (MOD09 A1). SPECLib has a temporal resolution of 8 days and contains most of the land cover types in each 1.5 ° × 1.5 ° geographic cell. For an arbitrary temporal stage, for each geographical cell of the SPECLib, there are a total of about 5000 MODIS spectra. Secondly, an operational approach for automatic land cover mapping using Landsat data is developed based on SPECLib. For each Landsat scene to be classified, the reference spectra of the land cover types in the corresponding geographic cell were automatically extracted and normalized. The Landsat scene was then classified by using the normalized reference spectra from SPECLib in an integrated classification approach that combined MLC (Maximum Likelihood Classification) and SAM (Spectral Angle Mapper). Finally, the SPECLib-based approach is tested using 510 single-date Landsat 8 OLI scenes to produce a land cover map of China. The map was validated using 11,366 interpretation samples and was shown to have an overall accuracy of 77.34% and a kappa coefficient of 0.729. Therefore, the preliminary results indicate that the SPECLib-based operational classification approach is a promisingly automatic method for global land cover mapping at a resolution of 30 m. [ABSTRACT FROM AUTHOR]

This study evaluated the potential of five seasonal high resolution Pléiades satellite images for improving urban tree species classification in the City of Tampa, FL, USA. We assessed and compared the capabilities of individual and combined Pléiades images acquired during different seasons for classifying the urban tree species to understand the seasonal effect on tree species mapping accuracy. The seven species and groups included sand live oak ( Quercus geminata ), laurel oak ( Q. laurifolia ), live oak ( Q. virginiana ), pine (species group), palm (species group), camphor ( Cinnamomum camphora ), and magnolia ( Magnolia grandiflora ). A multi-level classification system was adopted to classify image objects of the tree species and groups. Species classification performance was compared between the five individual seasonal Pléiades images, between two combined dry-wet season images, and between the optimal single season and combined dry-wet season image data. Shade image objects were spectrally normalized to similar sunlit image objects. The tree species fraction features were extracted from the seasonal images using the Mixture Tuned Matching Filtering approach and used as additional features to a set of spectral and spatial/textural features. Random Forest, Support Vector Machine and Linear Discriminant Analysis classifiers were used to classify the seven tree species and groups with image objects features. The experimental results indicate significantly improved tree species mapping accuracies using late spring season (April) image compared to all other seasonal images (p < 0.01), and combined dry-wet season images performed even better. Results suggest a significant seasonal effect on tree species classification. The results also demonstrate that the Random Forest outperformed the Support Vector Machine and Linear Discriminant Analysis classifiers for tree species classification. Therefore, in practice, it is important to choose appropriate seasonal remote sensing data for mapping tree species. [ABSTRACT FROM AUTHOR]

High spatial resolution hyperspectral imagery has shown considerable potential for deriving accurate land cover maps in urban areas. In this paper, a new classification framework for mapping land cover in urban environments using high spatial resolution hyperspectral data was proposed. The proposed classification scheme was applied to map urban land cover using APEX data in the city of Baden, Switzerland. We first used the NDWI and NDVI indices to separate the land cover in the scene into three main classes: water, vegetation and non-vegetated surface. Then we partitioned the scene into many superpixels and applied classification using a SVM separately on the vegetation and non-vegetated surfaces. Soil was classified both in vegetation and non-vegetated surface, and these two soil results were merged in the final classification map. Shadows were initially classified in shaded vegetation surfaces and shaded non-vegetated surfaces, and then they were further classified into meaningful land cover categories. Our experimental results demonstrate that the proposed classification framework is well suited for mapping land cover in urban environments using high resolution hyperspectral data. Although the proposed method performs better than traditional methods in terms of soil classification accuracy, our findings emphasize that the soil class should be interpreted with caution in urban land cover maps derived from remote sensing data, even when high spatial resolution hyperspectral data are used. Results from this study also demonstrate that although shaded surfaces are generally classified as a single category in urban environments, in high resolution hyperspectral data, the shadows can be further classified into meaningful land cover classes with an acceptable accuracy. [ABSTRACT FROM AUTHOR]

Globally, there is paucity of accurate information on the spatial distribution and patch sizes of Invasive Alien Plants (IAPs) species. Such information is needed to aid optimisation of control mechanisms to prevent further spread of IAPs and minimize their impacts. Recent studies have shown the capability of very high spatial (<1 m) and spectral resolution (<10 nm) data for discriminating vegetation species. However, very high spatial resolution may introduce significant intra-species spectral variability and result in reduced mapping accuracy, while higher spectral resolution data are commonly limited to smaller areas, are costly and computationally expensive. Alternatively, medium and high spatial resolution data are available at low or no cost and have limitedly been evaluated for their potential in determining invasion patterns relevant for invasion ecology and aiding effective IAPs management. In this study medium and high resolution datasets from Landsat Operational Land Imager (OLI) and SPOT 6 sensors respectively, were evaluated for mapping the distribution and patch sizes of IAP, Parthenium hysterophorus in the savannah landscapes of KwaZulu-Natal, South Africa. Support Vector Machines (SVM) classifier was used for classification of both datasets. Results indicated that SPOT 6 had a higher overall accuracy (86%) than OLI (83%) in mapping P. hysterophorus . The study found larger distributions and patch sizes in OLI than in SPOT 6 as a result of possible P. hysterophorus expansion due to temporal differences between images and coarser pixels were insufficient to delineate gaps inside larger patches. On the other hand, SPOT 6 showed better capabilities of delineating gaps and boundaries of patches, hence had better estimates of distribution and patch sizes. Overall, the study showed that OLI may be suitable for mapping well-established patches for the purpose of large scale monitoring, while SPOT 6 can be used for mapping small patches and prioritising them for eradication to prevent further spread at a landscape scale. [ABSTRACT FROM AUTHOR]

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs. [ABSTRACT FROM AUTHOR]

Vertical forest structure can be mapped over large areas by combining samples of airborne laser scanning (ALS) data with wall-to-wall spatial data, such as Landsat imagery. Here, we use samples of ALS data and Landsat time-series metrics to produce estimates of top height, basal area, and net stem volume for two timber supply areas near Kamloops, British Columbia, Canada, using an imputation approach. Both single-year and time series metrics were calculated from annual, gap-free Landsat reflectance composites representing 1984–2014. Metrics included long-term means of vegetation indices, as well as measures of the variance and slope of the indices through time. Terrain metrics, generated from a 30 m digital elevation model, were also included as predictors. We found that imputation models improved with the inclusion of Landsat time series metrics when compared to single-year Landsat metrics (relative RMSE decreased from 22.8% to 16.5% for top height, from 32.1% to 23.3% for basal area, and from 45.6% to 34.1% for net stem volume). Landsat metrics that characterized 30-years of stand history resulted in more accurate models (for all three structural attributes) than Landsat metrics that characterized only the most recent 10 or 20 years of stand history. To test model transferability, we compared imputed attributes against ALS-based estimates in nearby forest blocks (>150,000 ha) that were not included in model training or testing. Landsat-imputed attributes correlated strongly to ALS-based estimates in these blocks (R 2 = 0.62 and relative RMSE = 13.1% for top height, R 2 = 0.75 and relative RMSE = 17.8% for basal area, and R 2 = 0.67 and relative RMSE = 26.5% for net stem volume), indicating model transferability. These findings suggest that in areas containing spatially-limited ALS data acquisitions, imputation models, and Landsat time series and terrain metrics can be effectively used to produce wall-to-wall estimates of key inventory attributes, providing an opportunity to update estimates of forest attributes in areas where inventory information is either out of date or non-existent. [ABSTRACT FROM AUTHOR]

The investigation of changes in spatial patterns of vegetation and identification of potential micro-refugia requires detailed topographic and terrain information. However, mapping alpine topography at very detailed scales is challenging due to limited accessibility of sites. Close-range sensing by photogrammetric dense matching approaches based on terrestrial images captured with hand-held cameras offers a light-weight and low-cost solution to retrieve high-resolution measurements even in steep terrain and at locations, which are difficult to access. We propose a novel approach for rapid capturing of terrestrial images and a highly automated processing chain for retrieving detailed dense point clouds for topographic modelling. For this study, we modelled 249 plot locations. For the analysis of vegetation distribution and location properties, topographic parameters, such as slope, aspect, and potential solar irradiation were derived by applying a multi-scale approach utilizing voxel grids and spherical neighbourhoods. The result is a micro-topography archive of 249 alpine locations that includes topographic parameters at multiple scales ready for biogeomorphological analysis. Compared with regional elevation models at larger scales and traditional 2D gridding approaches to create elevation models, we employ analyses in a fully 3D environment that yield much more detailed insights into interrelations between topographic parameters, such as potential solar irradiation, surface area, aspect and roughness. [ABSTRACT FROM AUTHOR]

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps. [ABSTRACT FROM AUTHOR]

The present study assessed the potential of varying spectral configuration of Landsat 8 Operational Land Imager (OLI), Sentinel 2 MultiSpectal Instrument (MSI) and Worldview 2 sensors in the seasonal discrimination of Festuca costata (C3) and Themeda Triandra (C4) grass species in the Drakensberg, South Africa. This was achieved by resampling hyperspectral measurements to the spectral windows corresponding to the three sensors at two distinct seasonal periods (summer peak and end of winter), using the Discriminant Analysis (DA) classification ensemble. In summer, standard bands of the Worldview 2 produced the highest overall classification accuracy (98.61%), followed by Sentinel 2 (97.52%), whereas the Landsat 8 spectral configuration was the least performer, using vegetation indices (95.83%). In winter, Sentinel 2 spectral bands produced the highest accuracy (96.18%) for the two species, followed by Worldview 2 (94.44%) and Landsat 8 yielded the least (91.67%) accuracy. Results also showed that maximum separability between C3 and C4 grasses was in summer, while at the end of winter considerable overlaps were noted, especially when using the spectral settings of the Landsat 8 OLI and Sentinel 2 shortwave infrared bands. Test of significance in species reflectance further confirmed that in summer, there were significant differences (P < 0.05), whereas in winter, most of the spectral windows of all sensors yielded insignificant differences (P > 0.05) between the two species. In this regard, the peak summer period presents a promising opportunity for the spectral discrimination of C3 and C4 grass species functional types, than the end of winter, when using multispectral sensors. Results from this study highlight the influence of seasonality on discrimination and therefore provide the basis for the successful discrimination and mapping of C3 and C4 grass species. [ABSTRACT FROM AUTHOR]

The spectral reflectance of most plant species is quite similar, and thus the feasibility of identifying most plant species based on single date multispectral data is very low. Seasonal phenological patterns of plant species may enable to face the challenge of using remote sensing for mapping plant species at the individual level. We used a consumer-grade digital camera with near infra-red capabilities in order to extract and quantify vegetation phenological information in four East Mediterranean sites. After illumination corrections and other noise reduction steps, the phenological patterns of 1839 individuals representing 12 common species were analyzed, including evergreen trees, winter deciduous trees, semi-deciduous summer shrubs and annual herbaceous patches. Five vegetation indices were used to describe the phenology: relative green and red (green\red chromatic coordinate), excess green (ExG), normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI). We found significant differences between the phenology of the various species, and defined the main phenological groups using agglomerative hierarchical clustering. Differences between species and sites regarding the start of season (SOS), maximum of season (MOS) and end of season (EOS) were displayed in detail, using ExG values, as this index was found to have the lowest percentage of outliers. An additional visible band spectral index (relative red) was found as useful for characterizing seasonal phenology, and had the lowest correlation with the other four vegetation indices, which are more sensitive to greenness. We used a linear mixed model in order to evaluate the influences of various factors on the phenology, and found that unlike the significant effect of species and individuals on SOS, MOS and EOS, the sites' location did not have a direct significant effect on the timing of phenological events. In conclusion, the relative advantage of the proposed methodology is the exploitation of representative temporal information that is collected with accessible and simple devices, for the subsequent determination of optimal temporal acquisition of images by overhead sensors, for vegetation mapping over larger areas. [ABSTRACT FROM AUTHOR]

Prosopis spp. is a fast and aggressive invader threatening many arid and semi-arid areas globally. The species is native to the American dry zones and was introduced in Somaliland for dune stabilization and fuel wood production in the 1970’s and 1980’s. Its deep rooting system is capable of tapping into the groundwater table thereby reducing its reliance on infrequent rainfalls and near-surface water. The competitive advantage of Prosopis is further fuelled by the hybridization of the many introduced subspecies that made the plant capable of adapting to the new environment and replacing endemic species. This study aimed to test the mapping accuracy achievable with Landsat 8 data acquired during the wet and the dry seasons within a Random Forest (RF) classifier, using both pixel- and object-based approaches. Maps are produced for the Hargeisa area (Somaliland), where reference data was collected during the dry season of 2015. Results were assessed through a 10-fold cross-validation procedure. In our study, the highest overall accuracy (74%) was achieved when applying a pixel-based classification using a combination of the wet and dry season Earth observation data. Object-based mapping were less reliable due to the limitations in spatial resolution of the Landsat data (15–30 m) and problems in finding an appropriate segmentation scale. [ABSTRACT FROM AUTHOR]

Global change has tremendous impacts on savanna systems around the world. Processes related to climate change or agricultural expansion threaten the ecosystem’s state, function and the services it provides. A prominent example is the Brazilian Cerrado that has an extent of around 2 million km 2 and features high biodiversity with many endemic species. It is characterized by landscape patterns from open grasslands to dense forests, defining a heterogeneous gradient in vegetation structure throughout the biome. While it is undisputed that the Cerrado provides a multitude of valuable ecosystem services, it is exposed to changes, e.g. through large scale land conversions or climatic changes. Monitoring of the Cerrado is thus urgently needed to assess the state of the system as well as to analyze and further understand ecosystem responses and adaptations to ongoing changes. Therefore we explored the potential of dense Landsat time series to derive phenological information for mapping vegetation gradients in the Cerrado. Frequent data gaps, e.g. due to cloud contamination, impose a serious challenge for such time series analyses. We synthetically filled data gaps based on Radial Basis Function convolution filters to derive continuous pixel-wise temporal profiles capable of representing Land Surface Phenology (LSP). Derived phenological parameters revealed differences in the seasonal cycle between the main Cerrado physiognomies and could thus be used to calibrate a Support Vector Classification model to map their spatial distribution. Our results show that it is possible to map the main spatial patterns of the observed physiognomies based on their phenological differences, whereat inaccuracies occurred especially between similar classes and data-scarce areas. The outcome emphasizes the need for remote sensing based time series analyses at fine scales. Mapping heterogeneous ecosystems such as savannas requires spatial detail, as well as the ability to derive important phenological parameters for monitoring habitats or ecosystem responses to climate change. The open Landsat and Sentinel-2 archives provide the satellite data needed for improved analyses of savanna ecosystems globally. [ABSTRACT FROM AUTHOR]

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed. [ABSTRACT FROM AUTHOR]

Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes. [ABSTRACT FROM AUTHOR]

We estimated surface displacements using persistent scatterer SAR interferometry (PS-InSAR) around the Hatchobaru geothermal field, Japan, from 18 ALOS/PALSAR images acquired from July 2007 to December 2010. Generally, geothermal fields, covered with natural targets such as rocky terrain and vegetation, have been one of the difficult targets for PS-InSAR analysis. However, we applied space adaptive filtering to increase the number of pixels for measuring surface displacement. The results of our analysis demonstrate ground subsidence with decaying velocity over the observation period around the geothermal field. The spatial pattern of ground subsidence includes sharp boundaries of subsidence that can be interpreted as fault traces. We demonstrated the usefulness of PS-InSAR analysis with the space adaptive filtering to estimate surface displacements with high spatial resolution and high spatial density around a geothermal field. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the contribution of very high spatial resolution (VHSR) Pléiades images to both early season crop identification and the mapping of bare soil surface characteristics due to cultural operations. The study region covering 21 km 2 is located west of the peri-urban territory of the Versailles plain and the Alluets plateau (Yvelines, France). About 100 cropped fields were observed on the ground synchronously with two Pléiades images of 3 and 24 April 2013 and one SPOT4 image of 2 April 2013. The GIS structuring of these field data along with vector information about field boundaries was used for delimitating both training and test zones for the support vector machine classifier with polynomial function kernel (pSVM). The pSVM was computed on the spectral bands and NDVI for both single-date Pléiades and the bi-temporal Pléiades pair. For the single-date classifications of crops, the overall per-pixel accuracy reached 87% for the SPOT4 image of 2 April (6 classes), 79% for the Pléiades image of 3 April (6 classes) and 82% for that of 24 April (7 classes). At the earlier date (2–3 April), the Pléiades image very well discriminated cultural operations (>77%, user’s or producer’s accuracies) as well as fallows and grasslands, while winter cereals and rapeseed were better discriminated by the SPOT4 image winter cereals (>70%, user’s or producer’s accuracies). As Pléiades images revealed within-field spatial variations of early phenological stages of winter cereals that could be critical for adjusting management of zones with delayed development during the growing season, they brought information complementary to multispectral images with high spatial resolution. For the bi-temporal Pléiades image, the overall per-pixel accuracy was about 80% (7 classes), winter crops, grasslands and fallows being very well detected while confusions occurred between spring barley at initial stages (2–3 leaves) and bare soils prepared for other spring crops. Using an additional validation field set covering ∼1/3 of the study area croplands, the crop map resulting from the bi-temporal Pléiades pair achieved correct crop prediction for about 89.7% of the validation fields when considering composite classes for winter cereals and for spring crops. Early-season Pléiades images therefore show a considerable potential for anticipating regional crop patterns and detecting soil tillage operations in spring. [ABSTRACT FROM AUTHOR]

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated. We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites. Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively. Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands . We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels — Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) — based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen’s kappa coefficient, κ ). The accuracies at Levels 2–4 and the classification based on the Natura 2000 habitat types were κ : 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy ( κ ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data. [ABSTRACT FROM AUTHOR]

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally ( i.e ., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal ( http://bis.iirs.gov.in ). [ABSTRACT FROM AUTHOR]

Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most patches < 1 km 2 ). The utilization of timber and non-timber resources by poor rural communities living around protected forest patches produce subtle changes in the forest canopy which can be hardly detected on a timely manner using traditional field surveys. The aims of this study were to assess: (i) the utility of very high resolution (VHR) remote sensing imagery (WorldView-2, 0.5–2 m spatial resolution) for mapping tree species and canopy gaps in one of the protected subtropical coastal forests in South Africa (the Dukuduku forest patch (ca.3200 ha) located in the province of KwaZulu-Natal) and (ii) the implications of the map products to forest conservation. Three dominant canopy tree species namely, Albizia adianthifolia , Strychnos spp . and Acacia spp ., and canopy gap types including bushes (grass/shrubby), bare soil and burnt patches were accurately mapped (overall accuracy = 89.3 ± 2.1%) using WorldView-2 image and support vector machine classifier. The maps revealed subtle forest disturbances such as bush encroachment and edge effects resulting from forest fragmentation by roads and a power-line. In two stakeholders’ workshops organised to assess the implications of the map products to conservation, participants generally agreed amongst others implications that the VHR maps provide valuable information that could be used for implementing and monitoring the effects of rehabilitation measures. The use of VHR imagery is recommended for timely inventorying and monitoring of the small and fragile patches of subtropical forests in Southern Africa. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats. [ABSTRACT FROM AUTHOR]

Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities. [ABSTRACT FROM AUTHOR]

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments. [ABSTRACT FROM AUTHOR]

Mangrove species compositions and distributions are essential for conservation and restoration efforts. In this study, hyperspectral data of EO-1 HYPERION sensor and high spatial resolution data of SPOT-5 sensor were used in Mai Po mangrove species mapping. Objected-oriented method was used in mangrove species classification processing. Firstly, mangrove objects were obtained via segmenting high spatial resolution data of SPOT-5. Then the objects were classified into different mangrove species based on the spectral differences of HYPERION image. The classification result showed that in the top canopy, Kandelia obovata and Avicennia marina dominated Mai Po Marshes Natural Reserve, with area of 196.8 ha and 110.8 ha, respectively, Acanthus ilicifolius and Aegiceras corniculatum were mixed together and living at the edge of channels with an area of 11.7 ha. Additionally, mangrove species shows clearly zonations and associations in the Mai Po Core Zone. The overall accuracy of our mangrove map was 88% and the Kappa confidence was 0.83, which indicated great potential of using hyperspectral and high-resolution data for distinguishing and mapping mangrove species. [ABSTRACT FROM AUTHOR]

Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility. [ABSTRACT FROM AUTHOR]

The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher–Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher–Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. [ABSTRACT FROM AUTHOR]

Since vegetation in urban areas delivers crucial ecological services as a support to human well-being and to the urban population in general, its monitoring is a major issue for urban planners. Mapping and monitoring the changes in urban green spaces are important tasks because of their functions such as the management of air, climate and water quality, the reduction of noise, the protection of species and the development of recreational activities. In this context, the objective of this work is to propose a methodology to inventory and map the urban tree spaces from a mono-temporal very high resolution (VHR) optical image using a Random Forest classifier in combination with object-oriented approaches. The methodology is developed and its performance is evaluated on a dataset of the city of Strasbourg (France) for different categories of built-up areas. The results indicate a good accuracy and a high robustness for the classification of the green elements in terms of user and producer accuracies. [ABSTRACT FROM AUTHOR]

Semiarid Mediterranean saline wetlands are unique ecosystems sheltering high biodiversity. In the last decades, the expansion of irrigated lands has led to hydrological imbalances in Mediterranean catchments, causing wetland degradation. Vegetation composition assessment is considered an important tool for evaluating wetland ecological condition and can be mapped using remote sensing. This study aims to develop a condition index based on plant community composition suitable for semiarid Mediterranean saline wetlands, as well as to test the applicability of airborne multispectral remote sensors for discriminating plant communities. Characteristic plant communities of 12 wetlands were identified by means of ordination and classification analysis of plant taxa cover percentages obtained through fieldwork sampling. An index for assessing wetland ecological condition was developed based on the relationship between wetland plant community composition and watershed hydrological condition. Selected wetland plant communities were then mapped by means of remote sensing techniques using random forest algorithm for supervised classification of airborne images. Following this methodology, remote sensing served as a tool for wetland condition assessment at a regional scale. [ABSTRACT FROM AUTHOR]

Knowledge of tree species distribution is important worldwide for sustainable forest management and resource evaluation. The accuracy and information content of species maps produced using remote sensing images vary with scale, sensor (optical, microwave, LiDAR), classification algorithm, verification design and natural conditions like tree age, forest structure and density. Imaging spectroscopy reduces the inaccuracies making use of the detailed spectral response. However, the scale effect still has a strong influence and cannot be neglected. This study aims to bridge the knowledge gap in understanding the scale effect in imaging spectroscopy when moving from 4 to 30 m pixel size for tree species mapping, keeping in mind that most current and future hyperspectral satellite based sensors work with spatial resolution around 30 m or more. Two airborne (HyMAP) and one spaceborne (Hyperion) imaging spectroscopy dataset with pixel sizes of 4, 8 and 30 m, respectively were available to examine the effect of scale over a central European forest. The forest under examination is a typical managed forest with relatively homogenous stands featuring mostly two canopy layers. Normalized digital surface model (nDSM) derived from LiDAR data was used additionally to examine the effect of height information in tree species mapping. Six different sets of predictor variables (reflectance value of all bands, selected components of a Minimum Noise Fraction (MNF), Vegetation Indices (VI) and each of these sets combined with LiDAR derived height) were explored at each scale. Supervised kernel based (Support Vector Machines) and ensemble based (Random Forest) machine learning algorithms were applied on the dataset to investigate the effect of the classifier. Iterative bootstrap-validation with 100 iterations was performed for classification model building and testing for all the trials. For scale, analysis of overall classification accuracy and kappa values indicated that 8 m spatial resolution (reaching kappa values of over 0.83) slightly outperformed the results obtained from 4 m for the study area and five tree species under examination. The 30 m resolution Hyperion image produced sound results (kappa values of over 0.70), which in some areas of the test site were comparable with the higher spatial resolution imagery when qualitatively assessing the map outputs. Considering input predictor sets, MNF bands performed best at 4 and 8 m resolution. Optical bands were found to be best for 30 m spatial resolution. Classification with MNF as input predictors produced better visual appearance of tree species patches when compared with reference maps. Based on the analysis, it was concluded that there is no significant effect of height information on tree species classification accuracies for the present framework and study area. Furthermore, in the examined cases there was no single best choice among the two classifiers across scales and predictors. It can be concluded that tree species mapping from imaging spectroscopy for forest sites comparable to the one under investigation is possible with reliable accuracies not only from airborne but also from spaceborne imaging spectroscopy datasets. [ABSTRACT FROM AUTHOR]

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R 2 = 0.96), shrub cover (adj. R 2 = 0.83), grass cover (adj. R 2 = 0.71), bare soil (adj. R 2 = 0.88), stone cover (adj. R 2 = 0.83) and litter cover (adj. R 2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies. [ABSTRACT FROM AUTHOR]

In the analysis of forest resources, the use of ALS (airborne laser scanning) enables detailed three dimensional (3D) descriptions of forests and their vegetation. Simultaneously, ecologists have recognized that 3D information on vegetation is highly important in analyzing the habitat suitability of a given site. Recently, animals’ habitat preferences have been analyzed, for example, with GPS-collared animals. This has resulted in detailed knowledge about the animals’ movements both spatially and temporally. This study combines 3D information on vegetation obtained from ALS data with information about animal locations from GPS data. The aim was to map and analyze the habitat preferences of moose. The study area was located on the west coast of Finland. The data consisted of 18 GPS-collared moose (monitored from 2009 to 2010) and ALS data collected in 2010. We investigated how habitat structure changes as a function of distance to observed moose locations and how observed moose locations differ from randomly selected locations in terms of 3D structure. We also created a model-based habitat suitability map and tested it against moose occurrences. The results suggested that there are clear differences between the areas occupied and not occupied by moose and that these differences can be detected from ALS data. More importantly, ALS proved its potential in linking 3D descriptions of vegetation directly to observed moose locations without any proxy variables. These observations strongly support future studies. [ABSTRACT FROM AUTHOR]

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion. [ABSTRACT FROM AUTHOR]

Abstract: This paper evaluates the usefulness of the hyperspectral imager (HSI) onboard Chinese HJ-1-A small satellite in vegetation mapping. Fractional vegetation cover (FVC) is an important surface microclimate parameter for characterizing land surface vegetation cover as well as the most effective indicator for assessing desertification and crop growth condition. The HJ-1/HSI data were used to calculate the narrow band vegetation index by using the in situ plot FVC data, which was then applied in sub-pixel de-composition model for the FVC estimation, namely the dimidiate pixel model. The FVC information in the Shihezi Area, Xinjiang, China was retrieved based on the dimidiate pixel model. Cross-checked with the in situ measured FVC data, a correlation coefficient square of 0.86, and the root mean square error of 10.9% is statistically achieved. The verification indicates that the FVC result retrieved from the HJ-1/HSI data is well correlated with the in situ measurements, demonstrating that the HJ-1/HSI data are promising for studying the potential impacts of global climate change on the arid and semi-arid landscapes. [Copyright &y& Elsevier]

Abstract: A new procedure for quantitatively assessing the geometric accuracy of thematic maps, obtained from classifying hyperspectral remote sensing data, is presented. More specifically, the methodology is aimed at the comparison between results from any of the currently popular contextual classification strategies. The proposed procedure characterises the shapes of all objects in a classified image by defining an appropriate reference and a new quality measure. The results from the proposed procedure are represented in an intuitive way, by means of an error matrix, analogous to the confusion matrix used in traditional thematic accuracy representation. A suitable application for the methodology is vegetation mapping, where lots of closely related and spatially connected land cover types are to be distinguished. Consequently, the procedure is tested on a heathland vegetation mapping problem, related to Natura 2000 habitat monitoring. Object-based mapping and Markov Random Field classification results are compared, showing that the selected Markov Random Fields approach is more suitable for the fine-scale problem at hand, which is confirmed by the proposed procedure. [Copyright &y& Elsevier]

Abstract: This study was the first to use high-resolution IKONOS imagery to classify vegetation communities on sub-Antarctic Heard Island. We focused on the use of texture measures, in addition to standard multispectral information, to improve the classification of sub-Antarctic vegetation communities. Heard Island’s pristine and rapidly changing environment makes it a relevant and exciting location to study the regional effects of climate change. This study uses IKONOS imagery to provide automated, up-to-date, and non-invasive means to map vegetation as an important indicator for environmental change. Three classification techniques were compared: multispectral classification, texture based classification, and a combination of both. Texture features were calculated using the Grey Level Co-occurrence Matrix (GLCM). We investigated the effect of the texture window size on classification accuracy. The combined approach produced a higher accuracy than using multispectral bands alone. It was also found that the selection of GLCM texture features is critical. The highest accuracy (85%) was produced using all original spectral bands and three uncorrelated texture features. Incorporating texture improved classification accuracy by 6%. [Copyright &y& Elsevier]

Abstract: Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies. [Copyright &y& Elsevier]

Abstract: The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarse-resolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover). [Copyright &y& Elsevier]

Abstract: The aim of the study was to elaborate a methodology for forest mapping based on high resolution satellite data, relevant for reporting on forest cover and spatial pattern changes in Europe. The Carpathians were selected as a case study area and mapped using 24 Landsat scenes, processed independently with a supervised approach combining image segmentation, knowledge-based rules to extract a training set and the maximum likelihood decision rule. Validation was done with available very high resolution imagery. Overall accuracies per scene ranged from 93 to 96%. The labelling disagreement in overlapping areas of adjacent scenes was 6.8% on average. The proposed methodology is easily applicable to other regions in Europe. It allows an accurate forest–non-forest map to be obtained in a quick and cost effective manner, spatially more detailed than the currently available European data sets. [Copyright &y& Elsevier]

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue. [Copyright &y& Elsevier]

Delineation of stands of Colophospermum mopane is carried out on aerial photographs for the area around Palapye, Botswana. The purpose of this study was to evaluate aerial photos as ground truth material for satellite image classification, and to assess the quality of an existing vegetation map around the study area. Colophospermum mopane can be accurately mapped using colour infrared (CIR) photographs. The study shows that CIR photography can be used for accurately mapping the distribution of mopane. This material can therefore, be used as a reliable tool for ground truthing vegetation classification of satellite images, as well as for monitoring the distribution of the species. The only pre-existing vegetation map over the study area is checked against a visually interpreted map for consistency of the level of detail. A close-up view of pictures of scanned CIR air photos are used to study tree shadow characteristics of Savannah vegetation. Results from this investigation are used to explain the concept of the darkening effect, prevalent on Savannah environments. A model is presented to show the effect of tree shadows on brightness values and on NDVI measured by satellites. [ABSTRACT FROM AUTHOR]

• Wetland complexities were mapped using a multi-level classification scheme. • Both wetland vegetation types and surface water dynamics were classified. • Combining Sentinel-1 and -2 provided the most accurate results. • High-vegetated wetlands could not be mapped accurately using Sentinel-1 and -2. Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1's C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively. [ABSTRACT FROM AUTHOR]

• Sentinel-2 imagery is effective at classifying compositional vegetation patterns. • Compositional patterns are finer than land use/land cover structures. • Multi-season Autumn/Spring feature set returns highest accuracy. • Highest overall accuracy from SVM (74%) and NN (72%) classifiers. • Sub-pixel ecological factors likely driving confusion. Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions. [ABSTRACT FROM AUTHOR]

• Peatland degradation due to the 2015 El Niño event has been identified. • The subsidence and land cover have been estimated using the Sentinel-1A SAR images. • Drainage networks have significant impacts on the subsidence patterns. • Vegetation cover degradation is correlated with the hotspot distribution. • The 2015 El Niño significantly increased subsidence and vegetation degradation. Indonesia has the world's largest tropical peatland, mostly located in the southern province of Sumatra, the south of Kalimantan, and Papua. The catastrophic fires between June and October 2015 induced by the El Niño event burnt most of these peatland areas. We analyzed spatio-temporal peat subsidence during pre- and post-fires in the peat hydrological unit of Sungai Sugihan – Sungai Saleh (KHGSS), South Sumatra using Sentinel-1 images by applying DInSAR-SBAS algorithm. Based on our analysis, the linear subsidence rate after the 2015 peat fires increased by a factor 6.4 compared to that of pre-fires. Generally, the estimated subsidence is temporally well-correlated with the precipitation variation. In addition, the subsidence patterns are spatially correlated with the hotspot distribution, peat thickness, and drainage networks. Furthermore, we mapped vegetation cover over the KHGSS by using the Sentinel-1 images as well. The results show that the vegetation degradation is correlated with the hotspot distribution and the highly-degraded vegetation associated with the 2015 peat fires. It demonstrated that the 2015 El Niño event has significant impacts on increasing the amount of the subsidence and the vegetation degradation in KHGSS area. [ABSTRACT FROM AUTHOR]