Remote Sensing and Image Interpretation

Wright, John W.; Lillesand, T. M.; Kiefer, R. W.

doi:10.2307/634969

Cited by 1,289 publications

(138 citation statements)

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“…The maximum likelihood classification (MLC) algorithm (supervised classification) was performed to prepare the LULC map [85]. Confusion or error matrix was used to validate the classification result [86], and a good agreement was found (overall accuracy and Kappa coefficient of 89.38 and 0.8711, respectively). Figure 3 shows the LULC map of the study area.…”

Section: Spatial Data Processing For Groundwater Potential Zone Mappingmentioning

confidence: 99%

Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques

Mallick

Khan

Ahmed

et al. 2019

Water

163

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Saudi Arabia’s arid and semi-arid regions suffer from water scarcity because of climatic constraints and rapid growth of domestic and industrial water uses. The growing demand for high-quality water supplies and to reduce the dependency on desalination creates an urgent need to explore groundwater resources as an alternative. The weighted overlay analysis method using the fuzzy-analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) techniques combined with geoinformation technology was used in this study to explore the groundwater potential zones in the Itwad-Khamis watershed of Saudi Arabia. Twelve thematic layers were prepared and processed in a GIS setting to produce the groundwater potential zone map (GPZM). Subsequently, potential groundwater areas were delineated and drawn into five classes: very good potential, good potential, moderate potential, poor potential, and very poor potential. The estimated GWPZ (groundwater potential zones) was validated by analyzing the existing open wells distribution and the yield data of selected wells within the studied watershed. With this quality-based zoning, it was found that 82% of existing wells were located in a very good and good potential area. The statistical analysis showed that 14.6% and 28.8% of the total area were under very good and good, while 27.3% and 20.2% were accounted for the moderate and poor potential zone, respectively. To achieve sustainable groundwater management in the Aseer region, Saudi Arabia, this research provided a primary estimate and significant insights for local water managers and authorities by providing groundwater potential zone map.

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Section: Spatial Data Processing For Groundwater Potential Zone Mappingmentioning

confidence: 99%

Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques

Mallick

Khan

Ahmed

et al. 2019

Water

163

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“…Linear features on a satellite image regularly reflect the geological lineaments (faults or fractures) and hydrological structures (river or shoreline) [Lillesand et al, 2004] Analysis of the lineament network for different time periods images allows highlighting of the orientation of them ( fig. 3).…”

Section: Results Of Researches and Their Discussionmentioning

confidence: 99%

Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

Sichugova

Fazilova

2020

ICIGIS

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This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

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“…The maximum-likelihood classifier was adopted from a parametric classification algorithm [27][28][29][30] and divided into four classes: urban, vegetation, forest and waterbodies ( Table 2). The classes that were involved in the selection of the training sites were used as a reference in the user-guided approach [17,[31][32][33][34]. For each of the predetermined change detection types, training samples were selected by delimiting the polygons in the study area.…”

Section: Data Acquisition and Sourcesmentioning

confidence: 99%

Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

Nurda

Noguchi

Ahamed

2020

Forests

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The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

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Remote Sensing and Image Interpretation

Cited by 1,289 publications

References 0 publications

Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques

Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques

Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

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