Mapping of Major Land-Use Changes in the Kolleru Lake Freshwater Ecosystem by Using Landsat Satellite Images in Google Earth Engine

Abstract

India’s largest freshwater ecosystem of the Kolleru Lake has experienced severe threats by land-use changes, including the construction of illegal fishponds around the lake area over the past five decades. Despite efforts to protect and restore the lake and its riparian zones, environmental pressures have increased over time. The present study provides a synthesis of human activities through major land-use changes around Kolleru Lake both before and after restoration measures. For this purpose, archives of all Landsat imageries from the last three decades were used to detect land cover changes. Using the Google Earth Engine cloud platform, three different land-use scenarios were classified for the year before restoration (1999), for 2008 immediately after the restoration, and for 2018, i.e., the current situation of the lake one decade afterward. Additionally, the NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) indices were used to identify land cover dynamics. The results show that the restoration was successful; consequently, after a decade, the lake was transformed into the previous state of restoration (i.e., 1999 situation). In 1999, 29.7% of the Kolleru Lake ecosystem was occupied by fishponds, and, after a decade of sustainable restoration, 27.7% of the area was fishponds, almost reaching the extent of the 1999 situation. On the one hand, aquaculture is one of the most promising sources of income, but there is also limited awareness of its negative environmental impacts among local residents. On the other hand, political commitment to protect the lake is weak, and integrated approaches considering all stakeholders are lacking. Nevertheless, alterations of land and water use, increasing nutrient concentrations, and sediment inputs from the lake basin have reached a level at which they threaten the biodiversity and functionality of India’s largest wetland ecosystem to the degree that immediate action is necessary to prevent irreversible degradation.

Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India

“…These requirements were addressed and such technology was made freely available to anyone with the launch of the Google Earth Engine (GEE). GEE is a cloud-based platform which combines vast amounts of remote sensing data from multiple sources with a high-performance computer service, allowing for quick and easy satellite imagery computing [23][24][25][26].…”

“…Midekisa et al (2017) [36] produced maps of locations all over the African continent for 15 years using the GEE platform. Kolli et al (2020) [26] mapped land use changes around Kolleru Lake, India, using an RF classifier and obtained an overall accuracy of 95.9% and a kappa coefficient of 0.94. Rahman et al (2020) [10] analyzed the performance, via accuracy levels, of RF and SVM on the classification of urban and rural areas in Bangladesh.…”

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

“…As GEE is technologically robust and is an open resource containing historical images, it has become increasingly popular in LULC mapping. In the Asian region as well, it has been adopted in the countries such as India [40], Bangladesh [41], Singapore [42], China [43][44][45], Cambodia [46], Nepal [47], etc. GEE has also been applied in understanding the changes in land cover at the regional level such as in Central Asia [48] and the Tigris-Euphrates basin of the Middle East [49].…”

UAV Thermal Images for Water Presence Detection in a Mediterranean Headwater Catchment

“…The identification of water pixels is then performed imposing a minimum threshold on the NDWI. The NDWI has been extensively used, especially in combination with satellite images (e.g., [27][28][29][30][31]), for mapping water bodies or quantifying surface water resources (e.g., [32][33][34][35]) and soil moisture (e.g., [36,37]). The NDWI underwent several modifications and revisions [27,[38][39][40][41] aimed at overcoming its major shortcomings.…”