scholarly journals Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

2019 ◽  
Vol 11 (13) ◽  
pp. 1514 ◽  
Author(s):  
Venkatappa ◽  
Sasaki ◽  
Shrestha ◽  
Tripathi ◽  
Ma
Keyword(s):  
Land Use ◽  
Cloud Computing ◽  
Land Cover ◽  
Land Use Changes ◽  
Forest Degradation ◽  
Google Earth ◽  
Climate Mitigation ◽  
Threshold Values ◽  
Simple Method ◽  
Google Earth Engine

As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.

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

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2493 ◽  
Author(s):  
Meena Kumari Kolli ◽  
Christian Opp ◽  
Daniel Karthe ◽  
Michael Groll
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
Google Earth ◽  
Nutrient Concentrations ◽  
Freshwater Ecosystem ◽  
Lake Ecosystem ◽  
Lake Basin ◽  
Lake Area ◽  
Google Earth Engine

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.

scholarly journals Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 173
Author(s):  
Changjun Gu ◽  
Yili Zhang ◽  
Linshan Liu ◽  
Lanhui Li ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Ecosystem Service ◽  
Forest Cover ◽  
Google Earth ◽  
Forest Cover Change ◽  
Sacred Landscape ◽  
Lulc Changes ◽  
Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals Land Use and Land Cover Mapping of Davangere using Google Earth Engine

2019 ◽  
Vol 8 (3) ◽  
pp. 474-477
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Error Matrix ◽  
Sensing Applications ◽  
Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

2021 ◽  
Author(s):  
Crismeire Isbaex ◽  
Ana Margarida Coelho
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Forest Biomass ◽  
Local Scale ◽  
Google Earth ◽  
Biomass Estimation ◽  
Promising Tool ◽  
Google Earth Engine ◽  
Sentinel 2

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

scholarly journals Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0184926 ◽  
Author(s):  
Alemayehu Midekisa ◽  
Felix Holl ◽  
David J. Savory ◽  
Ricardo Andrade-Pacheco ◽  
Peter W. Gething ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Land Cover Change ◽  
Google Earth ◽  
Google Earth Engine
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