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We examine spatial and temporal variability in normalized difference vegetation index (NDVI), snow cover and land surface temperature (LST) in Himachal Pradesh between 2001 and 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Mann-Kendall trend tests and Sen's slope estimates indicate increasing NDVI trends during the postmonsoon period. Increasing snow cover trend is observed during winter and premonsoon whereas decreasing annual LST trends are observed for Himachal Pradesh. Pearson's correlation coefficient (PCC) indicate a strong positive correlation between NDVI and LST (PCC = .808) and strong negative correlation between LST and snow cover (PCC = −.809) and NDVI and snow cover (PCC = −.838). Coefficient of determination greater than .90, between MODIS LST and snow cover observations and weather station records, indicate fair representation of ground conditions using the MODIS dataset. Low (2.4°C/1,000 m) and 2 of 26 | Natural Resource Modeling HAQ ET AL.

Conventional 3-channel color images have limited information and quality dependency on parametric conditions. Hence, spectral imaging and reproduction is desired in many color applications to record and reproduce the reflectance of objects. Likewise RGB images lack sufficient information to successfully analyze diabetic retinopathy. In this case, spectral imaging may be the alternative solution. In this article, we propose a new supervised technique to detect and classify the abnormal lesions in retinal spectral reflectance images affected by diabetes. The technique employs both stochastic and deterministic spectral similarity measures to match the desired reflectance pattern. At first, it classifies a pixel as normal or abnormal depending on the probabilistic behavior of training spectra. The final decision is made evaluating the geometric similarity. We assessed several multispectral object detection methods developed for other applications. They could not proof to be the solution. The results were interpreted using receiver operating characteristics (ROC) curves analysis.