scholarly journals Land Cover Change of Post-Tin Mining Land Conservation Area and Its Surroundings in Perimping Sub Watershed, Bangka Regency

2018 ◽  
Vol 73 ◽  
pp. 04021
Author(s):  
Meike Erthalia ◽  
Supriatna ◽  
Astrid Damayanti
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Cover Crops ◽  
Mining Area ◽  
Google Earth ◽  
Land Conservation ◽  
Local People ◽  
Degraded Land ◽  
Physical Damage ◽  
Conservation Area

Tin mining is one of the land uses that causes physical damage to the land. Degraded land due to the mining activity requires a conservation. Conservation of post-tin mining land in Perimping Sub Watershed consists of reclamation and revegetation by planting kinds of fast-growing plants and cover crops. As land management, conservation is conducted to establish the diversity of land cover and to recover the land quality to be more productive for the local people. This study aimed to analyze the land cover change in land conservation of post-tin mining area. Moreover, also to identify the condition of post-tin mining land which has been conserved. Land cover map of 2011, 2014, and 2017 were produced from Google Earth imagery. Field validation was conducted to determine the existence of cover types on conservation land and interviews were conducted to find other impact of post-tin mining land conservation for local people. The result shows that land cover change in post-tin mining land conservation area over 6 years dominated by escalation of land cover such as mining, plantations. Monitoring of land cover change in conservation area is important to measure the effectivity of land conservation in post-tin mining area.

scholarly journals Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

2020 ◽  
Vol 12 (18) ◽  
pp. 2883
Author(s):  
Theodomir Mugiraneza ◽  
Andrea Nascetti ◽  
Yifang Ban
Keyword(s):  
Growth Rate ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Land ◽  
Google Earth ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Urban Land Cover ◽  
Urban Land Cover Change ◽  
Land Cover Maps

Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

scholarly journals Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

2020 ◽  
Vol 12 (18) ◽  
pp. 3110
Author(s):  
Manjunatha Venkatappa ◽  
Nophea Sasaki ◽  
Sutee Anantsuksomsri ◽  
Benjamin Smith
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Carbon Stock ◽  
Forest Cover ◽  
Good Practice ◽  
Google Earth ◽  
Classification Method ◽  
Siem Reap ◽  
Google Earth Engine

Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.

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

scholarly journals Quantifying the Landscape’s Ecological Benefits—An Analysis of the Effect of Land Cover Change on Ecosystem Services

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
J. Carl Ureta ◽  
Lucas Clay ◽  
Marzieh Motallebi ◽  
Joan Ureta
Keyword(s):  
South Carolina ◽  
Ecosystem Services ◽  
Land Cover ◽  
Land Cover Change ◽  
Cover Crops ◽  
Soil Nutrient ◽  
Yield Potential ◽  
Water Yield ◽  
Sediment Retention ◽  
Retention Capacity

The increasing pressure from land cover change exacerbates the negative effect on ecosystems and ecosystem services (ES). One approach to inform holistic and sustainable management is to quantify the ES provided by the landscape. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, this study quantified the sediment retention capacity and water yield potential of different land cover in the Santee River Basin Network in South Carolina, USA. Results showed that vegetated areas provided the highest sediment retention capacity and lowest water yield potential. Also, the simulations demonstrated that keeping the offseason crop areas vegetated by planting cover crops improves the monthly ES provision of the landscape. Retaining the soil within the land area prevents possible contamination and siltation of rivers and streams. On the other hand, low water yield potential translates to low occurrence of surface runoff, which indicates better soil erosion control, regulated soil nutrient absorption and gradual infiltration. The results of this study can be used for landscape sustainability management to assess the possible tradeoffs between ecological conservation and economic development. Furthermore, the generated map of ES can be used to pinpoint the areas where ES are best provided within the landscape.

scholarly journals PEMANFAATAN CITRA LANDSAT 8 OPERATIONAL LAND IMAGER (OLI) UNTUK KLASIFIKASI TUTUPAN LAHAN DI TAMAN NASIONAL GUNUNG MERBABU

2020 ◽  
Vol 15 (2) ◽  
pp. 28-39
Author(s):  
Diyanti Isnani Siregar ◽  
Adnin Musadri Asbi
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Cost Effective ◽  
Land Cover Classification ◽  
Google Earth ◽  
Training Data ◽  
Landsat 8 ◽  
Conservation Area ◽  
Effective Manner

Gunung Merbabu National Park (TNGMb) is a conservation area with a high level of biodiversity. Information on land cover is very important in making ecological management policies in conservation areas. Proven Remote Sensing technology produces precise information on land cover in a time and cost-effective manner. This study uses Landsat 8 imagery in TNGMb land cover classification process. Maximum Likelihood approach is used because it uses a probability calculation basis. A configuration matrix table between training data and reference data is made to test the accuracy of land cover classification. Reference data refers to Google Earth Pro high-resolution imagery. Results showed that the most extensive land cover type was secondary dryland forest with total of 23393 pixels classified as equivalent to 2113.54 hectares (34.5% of the total classification area. The open area, built-up area, and rice field/vegetable garden each have an area of ​​12.08 Ha; 11.02 Ha; and 170.96 Ha, of which part of the area is in enclaved areas within the TNGMb area. The accuracy test shows the Kappa Coefficient of 86.25%, User's Accuracy Average, Ground Truth Average, and Overall Accuracy respectively 89.62%; 85.42%; and 88.33%. Overall Accuracy shows that 88.33% of the total pixels represent each classification correctly.

scholarly journals Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

2018 ◽  
Vol 10 (8) ◽  
pp. 1226 ◽  
Author(s):  
Kelsey E. Nyland ◽  
Grant E. Gunn ◽  
Nikolay I. Shiklomanov ◽  
Ryan N. Engstrom ◽  
Dmitry A. Streletskiy
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
Environmental Modeling ◽  
Google Earth ◽  
The Arctic ◽  
Permafrost Degradation ◽  
Yenisei River ◽  
River Region ◽  
Google Earth Engine

Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.

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