Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

Abstract

Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m  250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three different techniques: error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coefficients (K). The average overall accuracies for rabi and kharif are 82.83% and 78.21%, respectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation between cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

Evaluation of the Impact of Water Management Technologies on Water Savings in the Lower Chenab Canal Command Area, Indus River Basin

“…The cultivation of wheat, cotton and vegetables increased a lot especially because of promotion of precision land levelling and bed planting and drip irrigation (Tables 10 and 11). According to [42], the acreage under wheat and sugarcane during the Rabi season 2007-2008 was 497,214 hectares and 126,883 hectares, respectively, which was almost the same as reported in Table 8 for wheat and sugarcane. The acreage reported in Table 9 for rice, cotton, and sugarcane is in accordance with the area reported by [42] for rice, cotton and sugarcane during the Kharif season 2007 i.e., Rice cultivated area was 252,756 hectares, cotton cultivated area was 76,740 hectares and the sugarcane cultivated area was 198,419 hectares.…”

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

“…NDVI is a commonly used vegetation index in remote sensing, and it has been used extensively in land cover mapping (e.g. Rodrigues, Marcal, Furlan, Ballester, & Cunha, 2013;Usman, Liedl, Shahid, & Abbas, 2015). Previous studies reported that land use classification can also generally benefit from the incorporation of information from the red-edge part of the electromagnetic spectrum (e.g.…”

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

“…For example, the Advanced Very High Resolution Radiometer (AVHRR) managed by the National Oceanic and Atmospheric Administration, possesses a coarse spatial resolution, while the Moderate Resolution Imaging Spectroradiometer (MODIS), Land Satellite (Landsat), and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) possess fine spatial resolutions. Specific advantages and disadvantages are associated with each type of them [34]. Due to the data volumes involved, the quality (i.e., season or cloud) as well as relatively low repeat times, the number of pre-collected medium spatial resolution images varies at different regions, thus provision of reliable and repeated urban information at regional to global scales remains a challenging task [35][36][37].…”