Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS

Mohamed, Mohamed Ali

doi:10.4236/nr.2017.85022

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…The elevations in the study area increase from the center, where the basin of Lake Müggelsee is located, to the sides in the north and south. The major land use and land cover are built-up areas (residential and commercial settlements and roads) 3.91 km 2 , green and open areas 9.17 km 2 , forest 24.15 km 2 and water bodies 2.17 km 2 [8].…”

Section: Study Areamentioning

confidence: 99%

“…In order to meet the legal requirements to maintain soil functions in accordance with the laws and regulations to soil protection, the detailed knowledge of soil mapping in urban areas is necessary. In addition, the quality of soil information acquired in one way or another plays a crucial role in determining the quality of the assessment of the suitability of soil functions and the reliability of the decisions associated with their use [3][4][5][6][7][8]. Therefore, digital mapping of the soil based on remote sensing data is an important and major source of information for the protection of soil functions and the management of land uses in urban areas [4].…”

Section: Introductionmentioning

confidence: 99%

“…Classical soil mapping cannot represent continuous changes in soil forms or soil types and cannot provide any information on the spatial variation of their individual characteristics. In addition, soil mapping is costly and time consuming, as it relies on intensive field surveys of soil [3,[6][7][8][9]. Soil science, like many other fields within geography, can benefit from various remote sensing applications and improved geographic information system (GIS)-based analyzes for more efficient digital mapping of soil characteristics or soil types.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Mohamed

2020

Land

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In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Mohamed

2020

Land

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“…The repetitive satellite remote sensing over various spatial and temporal scales has been one of the cost-effective means to generate information about forest cover, vegetation type and land use changes [23][24][25]. The TIFF format of satellite images were exported to image format in ERDAS Imagine ® 2015 software using layer stack function.…”

Section: Classification and Accuracy Assessmentmentioning

confidence: 99%

Effects of Ecotourism on Land Use Land Cover Dynamics: A Study from Shivapuri Watershed

Mandal¹

2019

ARTOAJ

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The ecotourism activities have been affecting the land cover and land use of the Shivpuri watershed, but assessment was not done before to show the dynamics. Thus, this study was objectively done to assess status and Land Use and Land Cover Change between 1999 and 2016, identify to status of ecotourism, major ecotourism sites and activities and trend of tourists in Shivapuri watershed. The Landsat ETM+ satellite image of November 1999 and OLI 2016 were downloaded from USGS website. Shivapur watershed site was clipped using ERDAS. Next, the image was classified using supervised maximum likelihood algorithm to produce the thematic map. In addition, altogether 216 and 233 training samples collected from the field observation and were used for accuracy assessment of

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“…For tracking land cover changes, the combined use of GIS and remote sensing has been demonstrated to be a reliable and profitable technique [20][21][22][23]. The efficacy of space-borne imaging in mapping LULC changes has been demonstrated in many studies [24,25]. Several Landsat images of various time spans have been used by [26] to investigate the land use and land cover changes in Egypt.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Zeshan

Mustafa

Baig

2021

Water

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Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

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Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS

Cited by 14 publications

References 7 publications

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Effects of Ecotourism on Land Use Land Cover Dynamics: A Study from Shivapuri Watershed

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

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