The influence of satellite image spatial resolution on mapping land use/land cover: a case study of Ho Chi Minh City, Vietnam

Abstract

Different spatial resolutions of optical satellite imagery provide different details in land use/land cover (LULC) mapping. Therefore, it is essential to decide the appropriate classification system for specific spatial resolution. This study used the same classification system to extract land use/land cover for District 2 and District7 of Ho Chi Minh City, Vietnam in 2018 from two different spatial resolutions, Sentinel-2 (10 m) and VNREDSat-1 (2.5 m), images. Using the same set of ground truth data, the accuracy of the classified maps was assessed to select the right image for the study area corresponding to the given classification system. The outcomes revealed that both optical satellite images produced high overall accuracy (83% for Sentinel-2 images and 86% for VNREDSat-1 images) on the same classification system. It is easier to classify the cropland, grassland, roads, and industrial zones with VNREDSat-1 image whereas perennials, bare land, and aquaculture were easily classified with Sentinel-2 due to their homogeneity at a lower spatial resolution. This shows the importance of choosing the right spatial resolution following the requirements of the classification system.

Change of Agriculture Area Over the Last 20 Years: A Case Study of Nainital District, Uttarakhand, India

“…India is primarily an Agrarian economy with agricultural activities sustaining a major population. Therefore, it is imperative to utilize the available agricultural land such that maximum productivity can be achieved (Pal, 2005; Mallupattu and Sreenivasula, 2013; Maxwell et al, 2018; Nguyen et al, 2021; Tuvdendorj et al, 2022). The use of technology has been a boon for planned development models.…”

Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution

“…Visual interpretation is a classical method used to extract aquaculture areas from remote sensing images [7][8][9][10], but its operation is subjective, and the interpreter's prior knowledge has a significant influence on extraction accuracy, resulting in low efficiency. Using spectral features, some scholars have tried to automatically extract aquaculture areas according to spectral classification [11][12][13][14][15][16][17][18], or by constructing a spectral feature index [19][20][21][22][23]. Spectral classification, however, is susceptible to the phenomenon of "same object different spectrum" and "foreign object in the same spectrum", and the spectral characteristic index is constructed primarily for a certain region or a certain sensor, which has low portability and robustness.…”