Assessing the applicability of Fusion Landsat-MODIS data for mapping agricultural land use - A case study in An Giang Province

• Published in: IOP conference series. Earth and environmental science 2022-01, Vol.964 (1), p.12005
• Main Authors: Diem, P K, Diem, N K, Can, N T, Minh, V Q, Huong, H T T, Diep, N T H, Tao, P C
• Format: Article
• Language: English
• Subjects: Agricultural development; Agricultural land; Agricultural land use; An Giang province; Cereal crops; Classification; Climate change; Cropping systems; Crops; Environmental impact; Evaluation; fusion; Image analysis; Image classification; Image processing; Information management; Land cover; Land use; Landsat; Landsat-MODIS; Mapping; MODIS; Normalized difference vegetative index; Orchards; Perennial crops; Remote sensing; Rice; Rice fields; Satellite imagery; Spatial discrimination; Spatial resolution; STARFM; Sustainable agriculture; Sustainable development; Time series
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/964/1/012005

This study aimed to evaluate the applicability of using time-series data of spatiotemporal fusion Landsat-MODIS imagery for mapping agricultural land use in An Giang province, Vietnam. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was adopted for fusion techniques to integrate the relatively high spatial resolution of Landsat (30 meters) and frequently revisit time of MODIS (MOD09Q1, 8-days). The Maximum Likelihood Classifier (MLC) was then used to classify the land cover categories based on variations of NDVI (Normalized Difference Vegetation Index) time-series over the observation period. The overall accuracy is about 84.9%, and a kappa coefficient of K=0.7, which revealed the effectiveness of using Fusion Landsat-MODIS NDVI data in land cover classification at the provincial scale. The current of the agricultural land use was finally mapped, including seven categories, namely built-up areas (10.49%), double rice crops (4.8%), triple rice crops (68.24%), perennial tree/orchards (4.08%), annual crops (7%), water surfaces (3.07%), and forest (2.32%). The results indicate that the agricultural land use cover can be detected in detail using Fusion Landsat-MODIS imagery. The classification is dramatically higher compared to the map classified by a conventional method of solely Landsat 8 image analysis (overall accuracy of 67.3% and Kappa coefficient K=0.35). The research outcomes will support the detailed information for managers in evaluating the impact of climate change on the rice cropping system toward sustainable agriculture development.