scholarly journals Assessment of Change in the Built-Up Index of Uyo Metropolis and Its Environs Using Remote Sensing

2019 ◽  
Vol 4 (6) ◽  
pp. 84-89 ◽  
Author(s):  
Aniekan Effiong Eyoh ◽  
Akwaowo Ekpa
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Quantitative Analysis ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Positive Growth ◽  
Bare Land ◽  
Erdas Imagine

The research was aim at assessing the change in the Built-up Index of Uyo metropolis and its environs from 1986 to 2018, using remote sensing data. To achieve this, a quantitative analysis of changes in land use/land cover within the study area was undertaken using remote sensing dataset of Landsat TM, ETM+ and OLI sensor images of 1986, 2000 and 2018 respectively. Supervised classification, using the maximum likelihood algorithm, was used to classify the study area into four major land use/land cover types; built-up land, bare land/agricultural land, primary swamp vegetation and secondary vegetation. Image processing was carried out using ERDAS IMAGINE and ArcGIS software. The Normalised Difference Built-up Index (NDBI) was calculated to obtain the built-up index for the study area in 1986, 2000 and 2018 as -0.20 to +0.45, -0.13 to +0.55 and -0.19 to +0.63 respectively. The result of the quantitative analysis of changes in land use/land cover indicated that Built-up Land had been on a constant and steady positive growth from 6.76% in 1986 to 11.29% in 2000 and 44.04% in 2018.

Land Use/Land Cover Dynamic Monitoring and Analysis Using Remote Sensing and GIS Techniques: Case Study of Qingpu District, Shanghai

2012 ◽  
Vol 518-523 ◽  
pp. 5704-5709
Author(s):  
Yi Lin ◽  
Bing Liu ◽  
Feng Xie ◽  
Wen Wei Ren
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Land Use Land Cover ◽  
Ecological Value ◽  
Barren Land

This paper illustrates almost twenty years (1986～2007) of Land use/land cover change (LULCC) in Qingpu-one district of Shanghai. Qingpu District is an area of Upper Huangpu Catchment for fresh water supply with considerable ecological value, but it is also experiencing urban sprawl from development. To reveal the trends underlie LULCC, we propose a novel procedure to quantify different land use/land covers and implement it in the case study. In this procedure, we first collect historical remote-sensing data and co-registered or corrected them to the same spatial resolution and radioactive level. Based upon preliminary interpretation or investigation, land use/land cover types in study area can be included in 5 categories, i.e. Water, Agricultural Land, Urban or Built-up Land, Forest Land, and Barren Land or others. Moreover, data is clipped via boundary of study area for reducing computation load, followed by FPCR-ISODATA classification to divide the data into k groups (k>the number of land types). After postprocessing, e.g., merge the same connoted subgroups and correct misclassified units accompany with validation and verification, the detailed land use/land cover results can be achieved accurately. The quantitative and regression analysis indicate that during the past twenty years the area of agricultural land of Qingpu decreased coupled with urban or built-up area increased linearly. The water area had the minimum change during the decades. Forests had the smallest average proportion (9.6%) of the total area. It occupied so small proportion of land that we can only find points of it in the maps. Barren land can be an indicator for monitoring uncompleted redevelopment or transition of land.

scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Catchment Area ◽  
Environmental Conservation ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Plantation Forest ◽  
Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

scholarly journals Land Use/Land Cover Changes in a Disturbed River Watershed Kenya

2019 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
Zachary Gichuru Mainuri ◽  
John M. Mironga ◽  
Samuel M. Mwonga
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing Image ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Human Needs ◽  
Observation Systems ◽  
Erdas Imagine ◽  
Remote Sensing Image Processing

Drivers of land use change were captured by the use of DPSIR model where Drivers (D) represented human needs, Pressures (P), human activities, State (S), the ecosystem, Impact (I) services from the ecosystem and Response (R), the decisions taken by land users. Land sat MSS and Land sat ETM+ (path 185, row 31) were used in this study. The Land sat ETM+ image (June 1987, May, 2000 and July, 2014) was downloaded from USGS Earth Resources Observation Systems data website. Remote sensing image processing was performed by using ERDAS Imagine 9.1. Two land use/land cover (LULC) classes were established as forest and shrub land. Severe land cover changes was found to have occurred from 1987-2000, where shrub land reduced by -19%, and forestry reduced by -72%. In 2000 – 2014 shrub land reduced by-45%, and forestry reduced by -64%. Forestry and shrub land were found to be consistently reducing.

scholarly journals Land Use and Land Cover Change as an Indicator of Watershed Urban Development in the Kenyan Lake Victoria Basin

2021 ◽  
Vol 16 (2) ◽  
pp. 335-345
Author(s):  
Dancan Otieno Onyango ◽  
Christopher O. Ikporukpo ◽  
John O. Taiwo ◽  
Stephen B. Opiyo
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Lake Victoria ◽  
Water Bodies ◽  
Natural Environments ◽  
Land Use Land Cover ◽  
Lake Victoria Basin ◽  
Ecological Value ◽  
Bare Land

The socio-economic and ecological value of Lake Victoria is threatened by significant regional development and urbanization. This study analyzed spatial-temporal land use/land cover changes in the Kenyan Lake Victoria basin from 1978–2018 using Landsat 3, 4-5 and 8 imagery, with a view to identifying the extent and potential impacts of urbanization on the basin. Supervised image classification was undertaken following the Maximum Likelihood algorithm to generate land use/land cover maps at ten-year intervals. Results indicate that the basin is characterized by six main land use/land cover classes namely, agricultural land, water bodies, grasslands and vegetation, bare land, forests and built-up areas. Further, the results indicate that the basin has experienced net increases in built-up areas (+97.56%), forests (+17.30%) and agricultural land (+3.54%) over the last 40 years. During the same period, it experienced net losses in grassland and vegetation (-37.36%), bare land (-9.28%) and water bodies (-2.19%). Generally, the changing landscapes in the basin are characterized by conversion of natural environments to built-up environments and driven by human activities, urban populations and public policy decisions. The study therefore recommends the establishment of a land use system that creates a balance between the ecological realm and sustainable development.

scholarly journals A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

2020 ◽  
Vol 10 (8) ◽  
pp. 2928 ◽  
Author(s):  
Rui Zhang ◽  
Xinming Tang ◽  
Shucheng You ◽  
Kaifeng Duan ◽  
Haiyan Xiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Mountain Areas ◽  
Feature Level Fusion ◽  
Artificial Surfaces ◽  
Fusion Framework ◽  
Level Fusion

Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.

scholarly journals Impact of Land Use Land Cover Change on Coastal Tourism in Kundapura, Karnataka, Using Multi-temporal Remotely Sensed Data and GIS Techniques

2018 ◽  
Vol 13 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Shekar Naik ◽  
H Gangadhara Bhat ◽  
T N Sreedhara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Agricultural Land ◽  
Coastal Development ◽  
Land Use Land Cover ◽  
Coastal Tourism ◽  
Remotely Sensed Data ◽  
Erdas Imagine ◽  
Multi Temporal

The present study is an attempt to examine the Land Use Land Cover changes in parts of Kundapura Taluk in Karnataka for the period 2006 and 2016 and its impact on coastal tourism. IRS satellite images of 2006 and 2016 have been used and processed using ERDAS Imagine and ArcGIS. The result indicated tremendous changes, particularly in mixed urban and agricultural land and proved that RS/GIS has advantages over conventional techniques. The result obtained, based on the multi-dated satellite data study, will assist in decision making and help to take appropriate measures to monitor and regulate coastal development in order to achieve sustainable and integrated coastal development.

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