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Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors' biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions. • This study mapped niches of B. utilis and R. campanulatum over Himalaya. • Investigated niche overlapping of study species over Himalaya. • Forty-nine NEOVs were developed to enhance SDMs in Himalaya. • NEOVs based models yielded realistic distributions compared to bioclimatic models. • Outputs of the study play a crucial role in conservation and management plans. [ABSTRACT FROM AUTHOR]

Monitoring and management of rare and economically important species in the highly complex terrain are challenging and thus need advanced technological development. In this study, the hyperspectral radiometer data of Taxus wallichiana were acquired at highly complex terrain of the Pindari region of the Himalaya and processed by using several sophisticated algorithms to deduce Taxol content in the plants. The spectroradiometer data were denoised through three different types of smoothing filters such as Average Mean, Savitzky Golay, and Fast Fourier Transform (FFT) followed by feature selection for allocation of best bands for Taxol content estimation. The results showed that the Average Mean filter in combination with feature selection performed best for Taxol spectral indices generation, model development, and Taxol content prediction. The best model showed a correlation of 0.719 with a relative root mean square error (RMSEr) value of 0.678 for Taxol content prediction. [ABSTRACT FROM AUTHOR]