Search Results - "Mashimbye, Zama Eric"

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning by Kavhu, Blessing, Mashimbye, Zama Eric, Luvuno, Linda

“…Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a…”
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A Scoping Review of Landform Classification Using Geospatial Methods by Mashimbye, Zama Eric, Loggenberg, Kyle

“…Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to…”
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Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory by Kavhu, Blessing, Mashimbye, Zama Eric, Luvuno, Linda

“…Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However,…”
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Survey of Community Livelihoods and Landscape Change along the Nzhelele and Levuvhu River Catchments in Limpopo Province, South Africa by Musakwa, Walter, Wang, Shuai, Wei, Fangli, Malapane, Olgah Lerato, Thomas, Masala Makumule, Mavengahama, Sydney, Zeng, Hongwei, Wu, Bingfang, Zhao, Wenwu, Nyathi, Nesisa Analisa, Mashimbye, Zama Eric, Poona, Nitesh, Chakwizira, James, Gumbo, Trynos, Mokoena, Baleseng, Kaitano, Francis, Fundisi, Emmanuel, Yeni-Letsoko, Vuyiswa

“…Landscape-change studies have attracted increasing interest because of their importance to land management and the sustainable livelihoods of rural…”
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Characterising social-ecological drivers of landuse/cover change in a complex transboundary basin using singular or ensemble machine learning by Kavhu, Blessing, Eric Mashimbye, Zama, Luvuno, Linda

“…Studies have focused on understanding land use/cover (LULC) change through regression techniques. However, machine learning (ML) techniques and their ensembles…”
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Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning by Kavhu, Blessing, Mashimbye, Zama Eric, Luvuno, Linda

“…Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various…”
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A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa by Zeng, Hongwei, Wu, Bingfang, Wang, Shuai, Musakwa, Walter, Tian, Fuyou, Mashimbye, Zama Eric, Poona, Nitesh, Syndey, Mavengahama

“…This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and…”
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Assessing the influence of DEM source on derived streamline and catchment boundary accuracy by Van Niekerk, Adriaan, De Clercq, Willem Petrus, Mashimbye, Zama Eric

“…Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived…”
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Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning by Maponya, Mmamokoma Grace, van Niekerk, Adriaan, Mashimbye, Zama Eric

“…•S2 imagery and machine learning can map crops as early as eight weeks before harvest.•Hand-selecting images for inclusion did not significantly improve…”
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An evaluation of digital elevation models (DEMs) for delineating land components by Mashimbye, Zama Eric, de Clercq, Willem Petrus, Van Niekerk, Adriaan

“…Land component boundaries often coincide with transitions in environmental land properties such as soil, climate and biology. Image segmentation is an…”
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