Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

Abstract

Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using a Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agriculture, water, and forest classes had been subjected to fluctuations with barren land in the decreasing trend. This alteration in the area of the LC classes also resulted in varying ESVs for individual land cover and total values for the years. The accuracy for the RF classifier was under substantial agreement for such fragmented LCs. Using LC, CoG, and ESV, the paper discusses the need for spatiotemporal analysis studies in Nepal to overcome the current limitations and later expansion to other regions. Studies such as these help in implementing proper plans and strategies by district administration offices and local governmental bodies to stop the exploitation of resources.

Temporal Floodplain Change Assessment Using GIS: A Case of Tinau River, Butwal

“…There is an increasing trend of settlement areas whereas decrease in the area of Tianu river floodplain and agricultural land. Similar trends have been reported by Kc et al(2021) and Mandal (2014) in their study in case of agricultural land and settlement areas. The research work by Thapa and Poudel(2018) confirms the findings on vegetation.…”

“…Many studies have been conducted in Nepal to analyze the change in land use over a period of time. For example: Rimal et al (2019) in the south eastern plain, Shrestha and Acharya (2020) in Kathmandu valley (Kathmandu, Lalitpur and Bhaktapur district) Adhikari et al (2020) in Chitwan, Rimal et al (2020) in western Terai, Kc et al (2021) in Rupandehi, Gautam and Rai (2022) in Kathmandu district, Devkota et al(2023) There are two reasons behind selection of the Tinau River as study area. First of all, it has repeatedly witnessed big flooding events triggering scores of deaths and property damages worth millions (in Nepali Rupees) (Dhungana et al, 2016).…”

Correction: Miletić et al. Quantifying Forest Cover Loss as a Response to Drought and Dieback of Norway Spruce and Evaluating Sensitivity of Various Vegetation Indices Using Remote Sensing. Forests 2024, 15, 662

“…As reported by Matović et al [31] and Stojanović et al [33], in those years, there was a huge outbreak of I. typographus and P. chalcographus, which, at that moment, acted like a primary pest. However, it should be taken into account that bark beetle outbreaks in Norway spruce forests are a consequence of adverse climatic effects, such as drought, as their defensive mechanisms are weakened when affected by summer drought [112][113][114][115]. Spatial-temporal expansion of forest glades in 2015, 2016, and 2017, which previously emerged over small areas in 2014 (Figure 3 and Table 4), clearly indicate bark beetle activity.…”

Understanding the Built-Up Evolution and Complexity of Fast-Growing Built-Up Agglomeration Using the Centroid Shift Model and Fractal Dimension

“…Various global LC data have been produced over the years, such as GLC2000, MCD12, and GLCNMO, GlobCover, IGBP DISCover, MODIS Collection 5 global land cover, Global Land Surface Satellite Global Land Cover (GLASS-GLC), Global Human Settlement Layer, GLC30, GLC10, ESA CCI Land Cover time-series, ESRI 10 m, etc. (Aman et al, 2021).…”