scholarly journals Spatial simulation of population in Shandong Province based on night-time imagery and land cover data

2021 ◽  
Vol 293 ◽  
pp. 02015
Author(s):  
Keyi Yang ◽  
Yunling Li ◽  
Yang Liu
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Stepwise Regression ◽  
Shandong Province ◽  
Good Precision ◽  
Night Time ◽  
Actual Distribution ◽  
Land Cover Data ◽  
Public Datasets ◽  
Better Than

Population spatial data can more truly express the actual distribution characteristics of the population, and provide data support for the regional environment and population development. Use Shandong Province as the research area, township-level census data, revised DMSP/OLS night-time data, and Globaland30 land cover data as data sources, partitions based on population agglomeration, and uses a stepwise regression method to build a population data spatial model. Use the model to simulate population density with a resolution of 100m. The experimental results show: Stepwise regression model good precision, the average relative error was 23.56%, and Root Mean Square Error, Mean Absolute Error are better than the other two public datasets. The simulation results are better than the two public datasets.

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.

Carbon Emissions and Sequestration Due to Land Conversion In The Padang Watershed, North Sumatera (incomplete)

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Suci Arisa Purba ◽  
Bejo Slamet ◽  
Abdul Rauf
Keyword(s):  
Carbon Sequestration ◽  
Land Cover ◽  
Spatial Data ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Land Conversion ◽  
Land Cover Data ◽  
Arc Gis ◽  
Reduce Emissions

Land conversion activities cause changes in the area of vegetation and carbon storage in the air. These changes can lead to emissions (reduction of carbon stocks) or sequestration (addition of carbon stocks). This study aims to calculate stored carbon in the Padang watershed in 2009 and 2019 and to determine the dynamics of emissions and carbon sequestration due to land conversion in the Padang watershed, North Sumatra Province from 2009 to 2019. The method used in this research is spatial data processing using software Arc Gis. Processing, interpretation and classification of land cover are obtained from land cover data for 2009 and 2019 from the Ministry of Environment and Forestry. Furthermore, the analysis of emissions and carbon sequestration in the Padang watershed was carried out using the REDD Abacus SP software. The results showed that the total carbon stored in 2009 was 5,168,464.09 tons. Meanwhile, the total carbon stored in 2019 was 5,150,784.81 tons. This means that there is a decrease or carbon emission during the 2009-2019 period of 17,679.28 tons. The total net emissions and sequestration that occurred in the Padang watershed due to changes in land use from 2009 - 2019 were 22,851,751.43 tonnes CO2-eq / year and  3,100,199.00 tonnes CO2-eq / year, respectively. Efforts to reduce emissions include planting and developing forests and community-based forest management.

scholarly journals The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland

2020 ◽  
Vol 9 (6) ◽  
pp. 358
Author(s):  
Iwona Cieślak ◽  
Andrzej Biłozor ◽  
Anna Źróbek-Sokolnik ◽  
Marek Zagroba
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Landscape Fragmentation ◽  
Water Bodies ◽  
Corine Land Cover ◽  
Land Cover Data ◽  
Land Use Types ◽  
Over Time

This article analyzes the applicability of spatial data for evaluating and monitoring changes in land use and their impact on the local landscape. The Coordination of Information on the Environment (CORINE) Land Cover database was used to develop a procedure and an indicator for analyzing changes in land cover, and the continuity of different land use types. Changes in land use types were evaluated based on land cover data. The results were analyzed over time to track changes in the evaluated region. The studied area was the Region of Warmia and Mazury in Poland. The preservation of homogeneous land cover plays a particularly important role in areas characterized by high natural value and an abundance of forests and water bodies. The study revealed considerable changes in land cover and landscape fragmentation in the analyzed region.

scholarly journals Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Wu ◽  
Jinyan Zhan ◽  
Haiming Yan ◽  
Chenchen Shi ◽  
Juan Huang
Keyword(s):  
Data Mining ◽  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Vegetation Index ◽  
Dynamical Downscaling ◽  
Driving Forces ◽  
Spatial Data Mining ◽  
Climate Simulation ◽  
Land Cover Data

The land use and land cover change (LUCC) is one of the prime driving forces of climate change. Most attention has been paid to the influence of accuracy of the land cover data in numerous climate simulation projects. The accuracy of the temporal land use data from Chinese Academy of Sciences (CAS) is higher than 90%, but the high-precision land cover data is absent. We overlaid land cover maps from different sources, and the grids with consistent classification were selected as the sample grids. By comparing the results obtained with different decision tree classifiers with the WEKA toolkit for data mining, it was found that the C4.5 algorithm was more suitable for converting land use data of CAS classification to land cover data of IGBP classification. We reset the decision rules with Net Primary Productivity (NPP) and Normalized Difference Vegetation Index (NDVI) as the indicators. The accuracy of the reclassified land cover data was proven to reach 83.14% through comparing with the Terrestrial Ecosystem Monitoring Sites and high resolution images. Therefore, it is feasible to produce the temporal land cover data with this method, which can be used as the parameters of dynamical downscaling in the regional climate simulation.

scholarly journals SPATIAL DATA QUALITY EVALUATION FOR LAND COVER CLASSIFICATION APPROACHES

2020 ◽  
Vol V-3-2020 ◽  
pp. 681-687
Author(s):  
M. Salhab ◽  
A. Basiri
Keyword(s):  
Land Cover ◽  
Data Quality ◽  
Spatial Data ◽  
Land Cover Classification ◽  
Training Dataset ◽  
Global Land Cover ◽  
Spatial Data Quality ◽  
Land Cover Data ◽  
Data Gaps ◽  
Global Land

Abstract. Data gaps and poor data quality may lead to flawed conclusions and data-driven policies and decisions, such as the measurement of Sustainable Development Goals progress. This is particularly important for land cover data, as an essential source of data for a wide range of applications and real-world challenges including climate change mitigation, food security planning, resource allocation and mobilization. While global land cover datasets are available, their usability is limited by their coarse spatial and temporal resolutions. Furthermore, having a good understanding of the fitness for the purpose is imperative. This paper compares two datasets from a spatial data quality perspective: (1) a global land cover map, and (2) a fit-for-purpose training dataset that is generated using visual inspection of very high-resolution satellite data. The latter dataset is created using Google Earth Engine (GEE), a cloud-based computing platform and data repository. We systematically evaluate the two datasets from spatial data quality (SDQ) perspective using the Analytic Hierarchy Process (AHP) to prioritise the criteria, i.e. SDQ. To validate the results, land cover classifications are conducted using both datasets, also within GEE. Based on the results of the SDQ evaluation and land cover classification, we find that the second training dataset significantly outperformed the global land cover maps. Our study also shows that cloud-based computing platforms and publicly available data repositories can provide an effective approach to filling land cover data gaps in data-scarce regions.

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