Abstract
Understanding ecological patterns and response to climate change requires unbiased data on species distribution. This can be challenging, especially in biodiverse but extreme environments like the Himalaya. This study presents the results of the first ever application of Unmanned Aerial Systems (UAS) imagery for species-level mapping of vegetation in the Himalaya following a hierarchical Geographic Object Based Image Analysis (GEOBIA) method. The first level of classification separated green vegetated objects from the rest with overall accuracy of 95%. At the second level, seven cover types were identified (including four woody vegetation species). For this, the suitability of various spectral, shape and textural features were tested for classifying them using an ensemble decision tree algorithm. Spectral features alone yielded ~70% accuracy (kappa 0.66) whereas adding textural and shape features marginally improved the accuracy (73%) but at the cost of a substantial increase in processing time. Contrast in plant morphological traits was the key to distinguishing nearby stands as different species. Hence, broad-leaved versus fine needle leaved vegetation were mapped more accurately than structurally similar classes such as Rhododendron anthopogon versus non-photosynthetic vegetation. Results highlight the potential and limitations of the suggested UAS-GEOBIA approach for detailed mapping of plant communities and suggests future research directions.
Highlights
Species composition of vegetation assemblages is a critical parameter for accessing biodiversity and ecosystem health [1,2]
The overall accuracy achieved for level 1 was 95.2% (Table 4), confirming that Normalized Difference Vegetation Index (NDVI) thresholding of objects by determining the optimal threshold value is simple yet an effective strategy for separating green vegetated objects from others (Figure 5a); this level of accuracy was observed by previous studies as well [54]
That is a challenging task for the alpine Himalayan communities because of (1) the dramatically heterogeneous topography, environment and community structure on the mountain slopes requiring adequate sampling of vegetation in space, and, (2) the formidable challenges of staying longer in the study site for adequate sampling
Summary
Species composition of vegetation assemblages is a critical parameter for accessing biodiversity and ecosystem health [1,2]. For the Himalayan region, space-borne coarse to medium spatial resolution imagery has been utilized for broad biodiversity characterization and assessment [6,7], for assessing forest degradation and changes in cover [8,9,10,11] and for land use/land cover mapping and monitoring [12,13,14] Whereas such studies provide understanding of ecological patterns and processes at the regional to landscape scale, characterizing the ecological dynamics at finer scales (e.g., individual tree or stand level) has been infeasible due to the lack of spatial detail in the data. Stand level mapping of tree-species can provide information about vegetation that would otherwise be impossible; this information possesses higher accuracy and precision in explaining diversity patterns in forests, quantifying carbon stocks, detecting post disturbance vegetation recovery, wildlife habitat assessment and understanding climate change impacts
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