“…Ali et al [139] Used LSWI+5 to analyze plant and soil water content in various watersheds in Ethiopia from 2006 to 2016 from Landsat-7 data. Cloud contamination affected results.…”
Section: Land Surface Water Index (Lswi+5)mentioning
Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.
“…Ali et al [139] Used LSWI+5 to analyze plant and soil water content in various watersheds in Ethiopia from 2006 to 2016 from Landsat-7 data. Cloud contamination affected results.…”
Section: Land Surface Water Index (Lswi+5)mentioning
Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.
“…The framework of this design idea is shown in Fig. 11 43 , and the design integrates the use of satellite remote sensing, unmanned aerial vehicles, video intelligent monitoring, the ground Internet of Things, and other methods to build an efficient and collaborative integrated monitoring system of the sky, space and earth, forming a point, line, and surface space with full-coverage sensing capability, and big data analysis monitoring and processing can be conducted 44 . Figure 11 Framework of the intelligent monitoring system for soil and water conservation 43 .…”
The construction of check dams is an important measure to prevent soil erosion on the Loess Plateau and reduce the amount of sediment entering the Yellow River. Based on an analysis of the current situation of soil and water conservation on the Loess Plateau and the three major problems faced by the traditional homogeneous soil check dam construction, the study of anti-scouring materials, hydrological calculation methods, dam design and construction technology and soil and water conservation monitoring are carried out in this paper. The results showed that the current soil and water conservation measures on the Loess Plateau have achieved remarkable outcomes. The new design and application concept of check dams with anti-burst and multi-sand interceptions is innovatively proposed in this paper. The new materials of solidified loess have good durability and anti-scouring characteristics and could meet the overflow and anti-scouring requirements of the new check dam. The small watershed high sand content of hydrological calculation can establish the upper limit of the flood sediment boundary for the anti-scouring protection layer of the check dam. The new technology of dam design and construction can achieve no collapse or slow collapse when encountering floods exceeding the standard. Intelligent monitoring systems can realize real-time dynamic monitoring for soil and water conservation on the Loess Plateau. The results will eventually contribute to the national strategy of the Ecological Protection and High Quality Development in the Yellow River basin.
“…This study was conducted in Langsa City, which is located on the east coast of Aceh province at positions 4 0 24'35"-4 0 33'47" N and 97 0 53'14" -98 0 04'42" E, with an area of 262.41 km 2 as shown in Figure 1. To identify the Langsa watershed, an analysis was performed through the Digital Elevation Model (DEM) extraction (Ali et al, 2020;Ardiansyah & Sumunar, 2020). Figures 2 and 3 show the delimitation procedures for the Langsa watershed and its result, respectively.…”
Watershed zoning is essential for regional development, specifically the conservation aspect. Langsa is a city in Aceh province that has no watershed zoning map as a basis for development. Therefore, this study aims to delimit the conservation area in the Langsa River Basin, Aceh Province, based on three factors, which include rainfall, slope, and vegetation density. The data used are Digital Elevation Models (DEM), Langsa City rainfall, and satellite imagery from Landsat 8 OLI TIRS. Furthermore, the data was obtained online from the website of Badan Informasi Geospasial also known as Geospatial Information Agency, the United States Geological Survey, and Badan Pusat Statistik or Central Bureau of Statistics. The data was processed using the Quantum GIS 3.16 application with scoring and weighted overlay. In this study, the Langsa watershed was divided into three areas, namely cultivation, buffer, and conservation. The cultivation area dominates the Langsa watershed with a sloping morphology, moderate rainfall, and moderate vegetation density. Also, the conservation area has a high morphology with high rainfall and vegetation density, while that of the buffer has characteristics that falls between the cultivation and conservation areas. Each zone namely cultivation, buffer, and conservation has respective areas of 63.75 km2, 4.84 km2, and 3.55 km2 with different land use priorities. For example, the cultivation zones are prioritized for agricultural and urban areas. Buffer is separated for perennials, while conservation zones are for protected forests or national parks.
Keywords : Langsa Watershed; Conservation Area; Buffer Area; Cultivation Area
Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember
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