Land Cover and Land Use Monitoring Based on Satellite Data within World Bank Project

Author(s):  
Nataliia Kussul ◽  
Andrii Shelestov ◽  
Mykola Lavreniuk ◽  
Andrii Kolotii ◽  
Vladimir Vasiliev
2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.


Author(s):  
Ibrar ul Hassan Akhtar ◽  
Athar Hussain ◽  
Kashif Javed ◽  
Hammad Ghazanfar

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land. 


2021 ◽  
pp. 694-708
Author(s):  
Melisa Ljuša ◽  
Hamid Čustović ◽  
Jasmin Taletović ◽  
Mirza Ponjavić ◽  
Almir Karabegović

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Anup Kumar ◽  
Shishupal Singh ◽  
V.S. Arya

Landuse refers to the use of land by human beings while the land cover refers to the natural cover on land. Landuse and land cover mapping is important for better developmental planning purpose. In the present time remote sensing satellite data, geographical information system (GIS) and global positioning system (GPS) are widely used in mapping of land use and land cover. In the present study landuse and land cover change analysis of southeastern part of Panchkula city have been done using Google Earth satellite data of 2002 and 2018. Satellite data downloaded from Google Earth and geo-referenced in ArcGIS 10.4 software. Landuse and landcover classes had been interpreted and field visit was done at selected location to check the interpreted data. Final maps were prepared and area of landuse and land cover classes were calculated. The study shows that during the year 2002 to 2018 built-up land area increased 95.01Hect, agriculture land area increased 1.24Hect., river course area decreased 20.35 Hect., vacant land area decreased 119.43Hect., park area increased 14.64 Hect., open scrub area decreased 7.82 Hect., road area increased 7.21 Hect.,water body area increased 0.02 Hect. and forest area increased 30.48 Hect. The study can be used for monitoring land use and land cover for planning purpose in the study area.


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