Subject
Now showing items 1-4 of 4
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Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning
(MDPI, 2021)Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within ... -
Comparing thresholding with machine learning classifiers for mapping complex water
(MDPI, 2019-06)Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable ... -
Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
(Wiley Open Access, 2017)Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time-consuming, thus, ... -
Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR
(Elsevier, 2016-05)This paper examines the value of very high-resolution multispectral satellite imagery and LiDAR-derived digital elevation information for classifying estuarine vegetation types. Satellite images used are fromtheWorldVie ...
