Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Abstract

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PDF

Spatial-Temporal Variations of Drought-Flood Abrupt Alternation Events in Southeast China

“…Similar results can be found in [25]. Yang et al [26] revealed that the SWAP index, combined with the multi-threshold run theory enables a more accurate identification of DFAA events in the Yangtze River basin [27][28][29]. The study found that the SPI and SPEI have strong monitoring ability for drought in the southern part of northern China.…”

Monitoring of spatio-temporal glaciers dynamics in Bhagirathi Basin, Gharhwal Himalayas using remote sensing data

“…In this study, Landsat images of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Landsat 8-Operational Land Images (OLI) (Dibs et al 2023b) at 30 m spatial resolution have been used to estimate glacier area change between the year 2000 and 2015. The summary of the data set is listed in Table 1.…”

Categorization of Multiple Crops Using Geospatial Technology, Machine Learning and Google Earth Engine

“…This study refines the CD method for precise LU/LC pattern calculation (24). Study assesses optimal LU/LC mapping in Serdang, Selangor, Malaysia, comparing spatial resolutions (UAV, WorldView-2, Sentinel-2) with GS and Brovey algorithms for accuracy (25).…”