Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial due to natural phenomena and/or human-induced activities. Mango a major fruit crop in India, but mango crops remains challenging task because reason many perennial similar reflectance profiles. Therefore, potent change technique required for different applications such as rate deforestation, urban developments, damage evaluation, and resource monitoring. Compared annual seasonal crops, relatively few studies been conducted on crops. In this study, novel (i.e., LR-PCA) developed using fusion log-ratio (LR) principal component analysis (PCA) derived bi-temporal soil adjusted vegetation index (SAVI) extract meaningful information detect temporal areas with high accuracy. The proposed approach comprised two steps: (1) SAVI 2015 2019 were used retrieve (PC) images, respectively, both fused by applying pixel-by-pixel approach. (2) Fused classified into three classes: “positive change”, “no “negative change” threshold value. results show LR-PCA method yields accuracy 92% comparison other methods viz. image differencing, ratioing, PCA, log-ratio. To validate adaptability algorithm, experiments sets indices belonging Sitapur district Uttar Pradesh State determine performs well compared existing individual crop. expected be useful area future, an accurate efficient may helpful developing real-time monitoring system at national level.