scholarly journals Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽  
2018 ◽  
Author(s):  
Mohamed Mostafa Mohamed ◽  
Samy Elmahdy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Confusion Matrix ◽  
Land Development ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Spectral Angle Mapper ◽  
Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

scholarly journals Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 1222-1227
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Image Classification ◽  
Water Body ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Object Based

The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.

scholarly journals Effect of Land Use/Land Cover Change on Groundwater Recharge in Osun Drainage Basin, Nigeria

2019 ◽  
Vol 28 (3) ◽  
pp. 381-394
Author(s):  
E. D. Ashaolu ◽  
J. F. Olorunfemi ◽  
I. P. Ifabiyi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Groundwater Recharge ◽  
Land Cover Change ◽  
Drainage Basin ◽  
Groundwater Resources ◽  
Land Development ◽  
Land Use Land Cover ◽  
Rock Outcrops ◽  
Groundwater System

Osun drainage basin is one of the regions in Nigeria experiencing increasing population growth and rapid urbanization; and about 70% of the inhabitantsrely on shallow groundwater resources of the region. Change in land use/land cover is one of the significant factors controlling regional hydrology and groundwater resources, thus the continuous change in land use and land cover of the drainage basin will significantly affect the basin’s groundwater resources. There are 7 classified land use/land cover in the study area which are bare surfaces, built up area, crops/shrubs, forest, rock outcrops, water bodies and wetland. Applying WetSpass-M hydrological model, we predicted the effect of land use/land cover change on the groundwater recharge in Osun drainage basin, Nigeria between 1984-2015. The results revealed that the highest groundwater recharge of 48.56%, 33.64% and 37.29% occurred in forested area in 1984, 2000 and 2015, respectively. This result might be due to the influence of vegetation in slowing down the speed of running water across the forest area, that allows more infiltration and deep percolation into the water table to recharge the groundwater system. On the other hand, the least groundwater recharge of the total annual was on the rock outcrops, which are about 4% in 1984, 3% in 2000 and 2% in 2015. The least recharge found on rock outcrops is expected and may be attributed to the fact that infiltration can only occur around or on decomposed rock outcrop, which may result in minute recharge to the groundwater system. The mean annual groundwater recharge of the basin for the land use/land cover of 1984, 2000 and 2015 are476.54, 411.07 and 430.06 mm/y, respectively. Overall, for the 32 years period of investigation, change in land use/land cover accounts for only 10% reduction in mean groundwater recharge occurrence between 1984 and 2015. Also, there is a change in recharge pattern in the study area during this period because most often, change in land use/land cover is a transition from one land use/land cover class to another, and the recharge pattern is influenced based on the degree of transition that took place and the characteristics of the dominant land use/land cover at a particular area of the basin. Although, the 10% reduction in mean annual recharge appears minute, this might become pronounced if the current rate of deforestation in the drainage basin continues unabated. Therefore, proper land use allocation, regulated land development and afforestation in terms of planting of native trees that were lost through anthropogenic activities in the basin should be policy option for groundwater sustainability.

scholarly journals A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES FOR LAND USE/LAND COVER CLASSIFICATION USING LAPAN-A2 MICROSATELLITE DATA

2017 ◽  
Vol 14 (1) ◽  
pp. 27 ◽  
Author(s):  
Jalu Tejo Nugroho ◽  
. Zylshal ◽  
Nurwita Mustika Sari ◽  
Dony Kushardono
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Confusion Matrix ◽  
Digital Camera ◽  
Land Use Land Cover ◽  
Central Java ◽  
Object Based ◽  
Accuracy Result ◽  
Blurring Effect

In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in LAPAN-A2 dataprocessing (case study Semarang, Central Java).The LU/LC were classified into eleven classes, as follows: sea, river, fish pond, tree, grass, road, building 1, building 2, building 3, building 4 and rice field. The accuracy of classification outputs were assessed using confusion matrix. The object-based and pixel-based classification methods result for overall accuracy are 31.63% and 61.61%, respectively. According to accuracy result, it was thought that blurring effect on LAPAN-A2 data may be the main cause ofaccuracy decrease. Furthermore, the result is suggested to use pixel-based classification to be applied inLAPAN-A2 data processing.

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.

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.

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