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This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

Coastal wetland ecosystems support a wide range of native species; however, they are currently threatened by invasive plant species. The Point Calimere Ramsar Site, located in India, contains coastal tropical dry evergreen forests, coastal grasslands, and mangroves that are now threatened by the invasion of Prosopis species. Consequently, several birds, mammals, and amphibians that depend on these habitats are also at risk. Therefore, tracking and monitoring invasive species is required for restoring wetland ecosystems and preventing further invasions. The present study investigated multi-season Sentinel-2 Spectral Temporal Metrics (STM) for mapping coastal native and non-native vegetation communities using summer, monsoon, and post-monsoon season datasets with Support Vector Machine (SVM) classification on the Google Earth Engine (GEE) platform. The results show that a combination of summer and post-monsoon Sentinel-2 spectral-temporal metrics produced the best accuracy (Overall accuracy—94%) for mapping Prosopis , tropical dry evergreen forests, and coastal grasslands, while the monsoon dataset produced the best results for mapping mangroves. However, the entire season’s spectral temporal metrics produced the best average results for all land cover classes. We also analyzed the distribution and fragmentation of Prosopis in the various landscapes of the Ramsar site using Fragstats. Our findings showed that Prosopis is extensively distributed in the Point Calimere Wildlife Sanctuary, posing a significant threat to the wildlife that resides there. We anticipate that our map will be used for the ongoing Prosopis clearance in our study site, and our study provides a comprehensive application for monitoring Prosopis and native vegetation in coastal tropical wetland habitats using Sentinel-2 STM.

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

Global spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate‐dominated environment–diversity relationships and, where possible, at finer grain using remote sensing. However, for grasslands with their small plant sizes, the limited availability of vegetation plot data has caused large uncertainties in fine‐grained mapping of species diversity. Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. Combining all variables yielded a predictive accuracy of 69% compared with 64% using remotely sensed variables or 65% using abiotic variables alone. Among remotely sensed variables, functional traits showed the highest predictive power (55%) in species richness estimation, followed by productivity and phenology (48%), spectral diversity (48%) and habitat heterogeneity (48%). When considering spatial autocorrelation, remotely sensed variables explained 52% and abiotic variables explained 41%. Moreover, Remotely sensed variables provided better prediction at smaller grain size (<∼1,000 km), while water‐ and energy‐dominated macro‐environment variables were the most important drivers and dominated the effects of remotely sensed variables on diversity patterns at macro‐scale (>∼1,000 km). These findings indicate that while remotely sensed vegetation characteristics and climate‐dominated macro‐environment provide similar predictions for mapping grassland plant species richness, they offer complementary explanations across broad spatial scales. Plain Language Summary Mapping spatial patterns of plant distributions and diversity is critical for biogeography and macroecology. In this study, we map grassland plant species richness across the Mongolian Plateau using data from over 1,600 sample sites and remote sensing and investigate the synergistic relationship of multiple diversity‐related hypotheses and underlying drivers in model predictions. We show that remotely sensed vegetation characteristics are good predictors of fine‐grained and water‐ and energy‐related variables of coarse‐grained diversity patterns. This work demonstrates how newly available high‐resolution satellite data can improve the prediction and explanation of large‐scale plant diversity in grassland. These findings hold particular significance for advancing the development and application of essential biodiversity variables (EBVs) in large‐scale plant diversity assessments in the face of rapidly changing environments. Key Points Multiple diversity‐related hypotheses and underlying drivers are examined in the context of predicting grassland plant diversity Vegetation variables obtained through remote sensing help explain how macro‐environments affect grassland plant diversity Remotely sensed vegetation variables offer more accurate diversity predictions at smaller spatial scales (less than 1000 km)

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains, (2) to assess where mortality events have a greater probability of occurring, and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest- and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Invasive alien species threaten tropical grasslands and native biodiversity across the globe, including in the natural mosaic of native grasslands and forests in the Shola Sky Islands of the Western Ghats. Here, grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus, and Pines) since the 1950s, but differing invasion intensities between these species and intermixing with native species constitutes a major challenge for remotely sensed assessments. In this study, we assess the accuracy of three satellite and airborne remote sensing sensors (Sentinel-1 radar data, Sentinel-2 multispectral data and AVIRIS-NG hyperspectral data) and three machine learning classification algorithms to identify the spatial extent of native habitats and invasive tree species. We used the support vector machine (SVM), classification and regression trees (CART), and random forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM produced the highest classification accuracy (98.7%). Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy; but only with higher coverage of ground control points. The hyperspectral data (AVIRIS-NG) was the only sensor that permitted distinguishing recent invasions (young trees) with high precision. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration or to assess the success of restoration activities, for which a choice of sensors may have to be made based on the age of invasion being mapped, and the quantum of ground control data available.

Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles (UAVs) to derive such maps. However, pixel‐based species classification at high spatial resolution is highly affected by within‐canopy variation caused by shadows. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central‐south Chile using combinations of UAV‐based spectral (RGB and hyperspectral), 2D textural and 3D structural variables including and excluding shaded canopy pixels during model calibration. The model accuracies in terms of area under the curve (AUC), Cohen's Kappa, sensitivity (true positive rate) and specificity (true negative rate) were examined in sunlit and shaded canopies separately. Bootstrapping was used for validation and to assess statistical differences between models. Our results show that shadows significantly affect the accuracies obtained with all types of variables. The predictions in shaded areas were generally inaccurate, leading to misclassification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Spectral and 2D textural information showed generally higher performances and improvements when excluding shadows from the analysis. Shadows significantly affected the model results obtained with any of the variables used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products. Finally, a shadow simulation showed differences in the ideal acquisition window for each species, which is important to plan revisit campaigns. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics and as reference for regional scale remote sensing models. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central Chile using combinations of consumer‐grade RGB and hyperspectral data. The predictions in shaded areas were generally inaccurate, leading to overall miss‐classification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Shadows significantly affect the use of any of the predictors used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products.

Acquiring detailed information on wetland plant species is critical for monitoring wetland ecosystem restoration and management. The emerging technique of Unmanned Aircraft System (UAS) photogrammetry has immense potential for such applications. In this study, we assessed the capacity of UAS photogrammetric products for classifying and mapping a large number of wetland plant species using contemporary Object-Based Image Analysis (OBIA) and machine learning methods. Our testing results in a heterogeneous coastal wetland demonstrated the benefit of centimeter-level orthoimagery and vertical products from UAS photogrammetry for mapping 17 species compared with standard aerial photography products. We achieved an overall accuracy (OA) of 71.3% and 84.8% for mapping 17 species and 10 major species, respectively. Our study suggests that UAS photogrammetry is a valuable tool for mapping wetland species composition and distribution.