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 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 DISTRICT-WISE CHANGE ANALYSIS OF LAND USE-LAND COVER IN DELHI TERRITORY USING REMOTE SENSING & GIS

2017 ◽  
Vol 10 (2) ◽  
pp. 201-213
Author(s):  
Surya Prakash Pattanayak ◽  
Sumant Kumar Diwakar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
High Growth ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Multi Temporal

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

scholarly journals Estimating the Volume of Sediments and Assessing the Water Balance of the Badra Basin, Eastern Iraq, Using Swat Model and Remote Sensing Data

2021 ◽  
Vol 54 (2C) ◽  
pp. 88-99
Author(s):  
Awad A. Sahar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Balance ◽  
Assessment Tool ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Sensing Data ◽  
Water Assessment

The primary objective of this study is to employ the remote sensing data and Soil & Water Assessment Tool model to estimate sediment volume and assess the water balance of the Badra Basin (2,615km2) in eastern Iraq. Remote sensing data was utilized as the main input with the Soil & Water Assessment Tool model. These data involved a land use-land cover map that was constructed by the classification of the Landsat-8 satellite imagery for the year 2020, STMR digital elevation model, soil map was acquired from the Food and Agriculture Organization and climatic data were sourced from the NASA-funded prediction of Worldwide Energy Resource The results discovered that about 40 % and 18% of the yearly rainfall are losing by evapotranspiration and filtration. The average amount of annual sediment transported was predicted at 120.47 tons /ha, 2018 recorded the highest value of transported sediment which is about 360 tons /ha. The volume of annual runoff was assessed at about 340.74 million m3. These results proved that the Soil & Water Assessment tool model has the ability to estimation the sediment and runoff volume. The climatic elements, especially rainfall, in addition to soil classes, topography, and land use-land cover had a significant impact on the amount of transported sediments and the volume of runoff.

scholarly journals Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Praveen Kumar Mallupattu ◽  
Jayarama Reddy Sreenivasula Reddy
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Area ◽  
Geographical Information Systems ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Land Use Land Cover ◽  
Rapid Urbanization ◽  
Sensing Data

Land use/land cover (LU/LC) changes were determined in an urban area, Tirupati, from 1976 to 2003 by using Geographical Information Systems (GISs) and remote sensing technology. These studies were employed by using the Survey of India topographic map 57 O/6 and the remote sensing data of LISS III and PAN of IRS ID of 2003. The study area was classified into eight categories on the basis of field study, geographical conditions, and remote sensing data. The comparison of LU/LC in 1976 and 2003 derived from toposheet and satellite imagery interpretation indicates that there is a significant increase in built-up area, open forest, plantation, and other lands. It is also noted that substantial amount of agriculture land, water spread area, and dense forest area vanished during the period of study which may be due to rapid urbanization of the study area. No mining activities were found in the study area in 1976, but a small addition of mining land was found in 2003.

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