Cover Data Products Research Articles

A dataset of land use/land cover fusion in Guangxi, China in 2000, 2010 and 2020

This study is based on six major land use/land cover remote sensing data products that are widely used in China and the world, namely ESA Global Land Cover Product (ESAGC), ESRI Global 10-meter Land Use/Land Cover Data (ESRI-LULC), Tsinghua University high-resolution global land cover product (FROM-GLC), Wuhan University China Land Cover Dataset (CLCD), global 30-meter high-resolution system land cover product (GLC_FCS30) and GlobeLand30 (GLC30). By reclassifying multiple data products, we developed a multi-source data product fusion method based on consistency assessment, and compiled a new Guangxi region land use/land cover data product in 2020 with a correlation coefficient greater than 0.94 with local statistical yearbook data. The study further used three sets of long sequence data (CLCD, GLC_FCS30 and GLC30) to create three land use/land cover fusion data products in Guangxi in 2000, 2010 and 2020. This study provides a methodological basis for the application of regional land use/land cover data products. The fusion of data can better support related research on regional environmental protection, agricultural production planning, and urban development planning in Guangxi.

CONTENT PLANNING AND STRUCTURE DESIGN OF GLOBAL LAND COVER KNOWLEDGE ATLAS

Abstract. From 2000 to 2020, three versions of the 30-meter global land cover data products (GlobeLand30) have been made accessible to the public. The dataset with 30-meter spatial resolution and 10-year temporal resolution are widely used for monitoring environment changes and resource management at global, regional and local scales. By analysing and extracting information and knowledge from the GlobeLand30 data, we have planned and designed a global land cover knowledge atlas, which aims to expand the available knowledge base for scientific research, management decision-making, and knowledge dissemination.

Consistency Analysis and Accuracy Evaluation of Multi-Source Land Cover Data Products in the Eastern European Plain

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products.

Accuracy assessment of land cover products in China from 2000 to 2020

The accuracy assessment of land cover data is of significant value to accurately monitor and objectively reproduce spatio-temporal dynamic changes to land surface landscapes. In this study, the interpretation and applicability of CCI, MCD, and CGLS long time-series land cover data products for China were evaluated via consistency analysis and a confusion matrix system using NLUD-C periodic products as reference data. The results showed that CGLS had the highest overall accuracy, Kappa coefficient, and area consistency in the continuous time-series evaluation, followed by MCD, whereas CCI had the worst performance. For the accuracy assessment of subdivided land cover types, the three products could accurately describe the distribution of forest land in China with a high recognition level, but their recognition ability for water body and construction land was poor. Among the other types, CCI could better identify cropland, MCD for grassland, and CGLS for unused land. Based on these evaluation results and characteristics of the data products, we developed suitable selection schemes for users with different requirements.

A dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015

The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.

Data driven analysis of atmospheric methane concentrations as function of geographic, land cover type and season

Over the last decade we have witnessed a rapid, so far unexplained, increase in the emission of methane to the atmosphere and this increase could lead to an acceleration of the ongoing climate changes. The increase is likely to originate from agriculture, but oil and gas production as well as wetlands are also under suspicion. The best way to quantify the emission of methane and other greenhouse gasses to our atmosphere is by using space based remote sensing. Here, we analyse 3 years of measurements of the column-averaged dry-air mole fraction of methane from the Tropospheric Monitoring Instrument on Sentinel-5P obtained with two different retrieval methods in order to evaluate the dependency on geographic, land cover type and season. The land cover types were obtained from the Moderate Resolution Imaging Spectroradiometer aboard the Terra and Aqua satellites and from the World Cover data product using observations from the Copernicus Sentinel-1 and Sentinel-2 missions. The analysis reveals that while the highest methane concentrations are generally found over croplands, the lowest are generally found over shrublands, which is in agreement with expectations. It is more surprising that the analysis also reveals lower than average methane concentrations over wetlands as wetlands are generally thought to be a major source of methane emission. Until this discrepancy is resolved the methane concentration over wetlands from the Tropospheric Monitoring Instrument on Sentinel-5P should be handled with caution. It is also found that the annual methane cycle, as seen in the measured methane concentrations, for croplands, shrublands and savannas is delayed in Africa compared to Asia.

Evaluation of Global Land Use–Land Cover Data Products in Guangxi, China

Land use–land cover (LULC) is an important feature for ecological environment research, land resource management and evaluation. Although global high-resolution LULC data sets are booming, their regional performances were still evaluated in limited regions. To demonstrate the local applicability of global LULC data products, six emerging LULC data products were evaluated and compared in Guangxi, China. The six products used are European Space Agency GlobCover (ESAGC), ESRI Land Use–Land Cover (ESRI–LULC), Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC), the China Land Cover Dataset (CLCD), the Global Land Cover product with Fine Classification System at 30 m (GLC_FCS30) and GlobeLand30 (GLC30). Reference data were obtained from the local government statistical yearbook and high-resolution remote sensing images on Google Earth. The results showed that CLCD, ESRI–LULC and GLC30 were found to agree well with the forest reference data, with the highest correlation coefficient of 0.999. For the cropland areas, GLC30, CLCD and ESAGC agreed well with the reference data, and the highest correlation coefficient was 0.957. Combined with the comparison with the high-resolution images obtained by Google Earth, we finally concluded that ESAGC, CLCD and GLC30 can best represent the LULCs in Guangxi. Furthermore, the spatial consistency analysis showed that three or more products identified the same LULC type as high as 96.98% of the area. We suggest that majority voting might be applied to global LULC products to provide fused products with better performances on a regional or local scale to avoid the error caused by a single data product.

Risk assessment of city site landscapes in the central plains of China

Scientific cognition and assessment of threats to cultural heritage and surrounding landscapes are prerequisites for targeted management and protection of cultural heritage. This study took 530 city sites in the Central Plains of China as the research object. Based on the survey information of city sites in the third national cultural heritage survey in China, a combination of high resolution google images and land use/land cover data products and city sites survey data enables a comprehensive evaluation of landscape elements impacts on city sites. With spatial statistical method, we divided city site landscape types, and constructed a landscape element risk assessment model and an urbanization intensity index to analyze the threats of each landscape element to city site protection. The results indicated that 40.64% of the sites are in farmland landscapes, where agricultural cultivation, irrigation, and other production activities may affect the surface and subsurface remains of the sites. The sites (16.82%) in urban landscapes are affected by activities such as building houses and roads. Only six sites belong to the water landscape area, which may be affected by water erosion. Other sites (2.84%) are mainly affected by plant root growth in forest landscapes. The city sites distributed in urban areas such as Zhengzhou or the urban–rural junction were significantly affected by urban expansion and got the greatest protection pressure. City sites in agricultural landscapes have the second highest conservation risk. Plant erosion and flooding mainly affected the city sites in the northwest-southwest mountainous areas or near the water area of the study area, and the risks were relatively small. It can be seen that landscape types such as urban and farmland, which are closely related to human activities, bring the most pressure to the protection of city sites, and it is necessary to focus on the impact of human activities such as urban construction on city sites and the surrounding environment in the future, while paying attention to the protection and rational use of city sites with high assessment risk in urban planning and management for the protection of the city site proper and landscape scenery.

An Optimal Transport Based Global Similarity Index for Remote Sensing Products Comparison

Remote sensing products, such as land cover data products, are essential for a wide range of scientific studies and applications, and their quality evaluation and relative comparison have become a major issue that needs to be studied. Traditional methods, such as error matrices, are not effective in describing spatial distribution because they are based on a pixel-by-pixel comparison. In this paper, the relative quality comparison of two remote sensing products is turned into the difference measurement between the spatial distribution of pixels by proposing a max-sliced Wasserstein distance-based similarity index. According to optimal transport theory, the mathematical expression of the proposed similarity index is firstly clarified, and then its rationality is illustrated, and finally, experiments on three open land cover products (GLCFCS30, FROMGLC, CNLUCC) are conducted. Results show that based on this proposed similarity index-based relative quality comparison method, the spatial difference, including geometric shapes and spatial locations between two different remote sensing products in raster form, can be quantified. The method is particularly useful in cases where there exists misregistration between datasets, while pixel-based methods will lose their robustness.

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height > 2 meters . Here, we report a new forest cover data product that used the FAO forest definition ( tree cover > 10 % and tree height > 5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

Effects of using different urban parametrization schemes and land-cover datasets on the accuracy of WRF model over the City of Ottawa

In the face of rapid urbanization and global warming, it is important to acquire a better understanding of urban climate and land-atmosphere interactions operating therein. The Weather Research and Forecasting (WRF) model is a limited area model that has been used to study urban microclimate in many cities across the globe. However, such a study is lacking for the Canada's capital city: Ottawa. In this article, the WRF model is set-up at 1 km spatial resolution over the Ottawa region and its sensitivity towards the use of different urban parametrization schemes and land-cover datasets is investigated from 01 June to 31 August 2018 which includes an extreme heat weather event spanning from June 30 to July 6. WRF-simulations are performed using the Noah land surface model with two urban parametrization schemes of different complexity, i.e., a simple bulk urban parametrization and a more advanced multilayer urban canopy model (UCM). Both WRF-simulations used the default land use-land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) available in WRF. Between these two WRF-simulations, the simulation with the multilayer UCM is found to be more accurate than the bulk scheme simulation in terms of modeled near-surface wind-speed, relative humidity, and total precipitation compared to observations recorded at one urban weather gauging station located within the city limits. Finally, a third WRF-simulation is performed with the multilayer UCM but using a higher resolution 30 m land use-land cover data product for the urban domain. This third WRF-experiment improved further the near-surface wind speed, relative humidity, and total precipitation correspondence to observations within the city. It is worthy to mention that modeled total precipitation is found to be sensitive to both urban parametrization schemes and urban land use-land cover data sets.

EMISSIONS FROM BIOMASS OPEN BURNING ON THE PEAT SOIL IN INDONESIA: INTEGRATING MODIS IMAGERY AND GIS DATA

Biomass open burning emissions release large amounts of pollutant, which has significantly contributed to the atmospheric pollution. This phenomenon also generates many pollutants as major influence on climate change. Southeast Asia currently has vast areas committed to agriculture, which are a major contributor of biomass open burning emissions. Satellite imagery and remote sensing are used in this study to calculate the burned area and emissions in the spatial context of Southeast Asia. The aim of this research is to obtain the importance of peat soil burning in Indonesia affected the amount of biomass open burning emissions in Southeast Asia. The burning of peat soil in Indonesia also emits large of emissions, thereforethiscasehastakenasaspecifictopicinthis research. MODIS burned area and land cover data products were used to detect the burned areas and estimate the emission factor in Southeast Asia related to emissions calculation. Asaresultofthisresearch,on 2001-2007, on May to October, Indonesia contributes a significant amount; approximately80%ofthetotalburnedin Southeast Asia wasoccurredinIndonesia. Data and results from this research can be applied for regulatory consideration to countries that participate in the Southeast Asia governing body.

Climate change perceptions, data, and adaptation in the Garhwal Himalayas of India

ABSTRACT Himalayan communities that depend on rain-fed agriculture are disproportionately vulnerable to climate change. In this study, we compare local perceptions of climate change from a household survey (n = 251) to climate data obtained from the Global Land Data Assimilation System (GLDAS 2.1) and MODIS Terra Snow Cover data product datasets. The study is situated in and around the Kedarnath Wildlife Sanctuary, which is located within the Garhwal Himalayas in the Indian state of Uttarakhand. We found that a large majority of respondents perceive that rainfall is increasing and that snowfall is decreasing, while a smaller majority perceives an increase in summer temperatures and no change in winter temperatures. Agreeing with the perceptions of the majority, the climate data show an increase in summer temperature and winter rainfall. However, the climate data also show an increase in winter temperature, and no monotonic change in snowfall, findings which are contrary to the perception of the majority. Household perceptions of climate change were not associated with adaptation; while many households perceived change, very few reported that they were planning to adapt. To encourage adaptation, communities would benefit from locally appropriate climate data products, and collaboration on best practices with researchers, NGOs, and extension services.

Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017

Myanmar, abundant in natural resources, is one of the countries with high forest cover in Southeast Asia. Along with its rapid socio-economic development, however, the construction of large-scale infrastructure, expansion of agricultural land, and an increasing demand for timber products have posed serious threats to the forests and significantly affected regional sustainable development. However, the geographical environment in Myanmar is complex, resulting in the lack of long-term sequence of land cover data products. Based on 30 years’ Landsat satellite remote sensing imagery data and the land cover data extracted by a mixed classification method, this paper examined the spatial and temporal evolution characteristics of forest cover in Myanmar and investigated driving factors of the spatio-temporal evolution. Results show that the forest cover has decreased by 110,621 km2 in the past 30 years with the annual deforestation rate of 0.87%. Cropland expansion is the main reason for the deforestation throughout the study period. The study can provide basic information of the forest cover data to the Myanmar government for ecological environment protection. At the same time, it can provide important support to the “Belt and Road” initiative to invest in the region’s economy.

Water-related land cover data products for geospatial analysis and modeling of infectious disease transmission risks

Event Abstract Back to Event Water-related land cover data products for geospatial analysis and modeling of infectious disease transmission risks Xiangming Xiao1*, Geli Zhang2, JInwei Dong3, Zhenhua Zou1, Xinxin Wang4 and Marius Gilbert5 1 University of Oklahoma, United States 2 China Agricultural University (CAU), China 3 Institute of Geographic Sciences and Natural Resources Research (CAS), China 4 Fudan University, China 5 Free University of Brussels, Belgium Transmission of many zoonotic infectious diseases is associated with the spatial-temporal changes in habitats, hosts, and vectors. One example is the highly pathogenic avian influenza viruses (HPAIV), and its transmission is closely related to the spatial distribution and movement of domestic ducks and wild water fowls which use water-related land cover types (e.g., open surface water body, natural wetland, and rice paddy) as their habitats. In the past decades, land use change and climate change have substantially and rapidly altered the spatial distribution and temporal dynamics of water-related land cover types, which calls for accurate and timely updated data, information, and knowledge of those water-related land cover types across the local, regional, national, and global scales. In this presentation, we will provide an update on water-related land cover data products from analyses of satellite images over years, including open surface water body, natural wetland, and rice paddy in China, USA, monsoon Asia, and the globe. We will also introduce the case studies that use these geospatial datasets in risk assessment and modeling of infectious diseases. We hope that this presentation will stimulate more discussion and collaboration between the geospatial science community, which focuses on the use of satellite images to track and monitor land use and land cover changes, and the animal and human health science community, which focuses on the epidemiology and ecology of infectious diseases that affect both animal and human health. Acknowledgements The research was supported by the National Institutes of Health (NIH) NIAID grant (1R01AI101028-01A2). Keywords: Surface water, aquaculture, wetlands, Rice paddies, remote sensing, avian influenza Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019. Presentation Type: Regular oral presentation Topic: Spatial data sources, open data, accessibility and information integration Citation: Xiao X, Zhang G, Dong J, Zou Z, Wang X and Gilbert M (2019). Water-related land cover data products for geospatial analysis and modeling of infectious disease transmission risks. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00052 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Jun 2019; Published Online: 27 Sep 2019. * Correspondence: Prof. Xiangming Xiao, University of Oklahoma, Norman, United States, xiangming.xiao@ou.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Xiangming Xiao Geli Zhang JInwei Dong Zhenhua Zou Xinxin Wang Marius Gilbert Google Xiangming Xiao Geli Zhang JInwei Dong Zhenhua Zou Xinxin Wang Marius Gilbert Google Scholar Xiangming Xiao Geli Zhang JInwei Dong Zhenhua Zou Xinxin Wang Marius Gilbert PubMed Xiangming Xiao Geli Zhang JInwei Dong Zhenhua Zou Xinxin Wang Marius Gilbert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Probability Distribution for the Visually Observed Fractional Cloud Cover over the Ocean

Abstract The authors suggest a three-parameter bounded distribution from the family of mixed gamma distributions for characterizing the probability density distributions of fractional total and low cloud cover over the global oceans. The authors derive both the continuous form of this distribution and its discrete counterpart, which can be directly applied to cloud cover reports. The distribution is applied to the cloud cover characteristics reported by voluntary observing ships (VOS) for the period from 1950 to 2011 after filtering nighttime observations with poor lunar illumination. The suggested distribution demonstrates a high goodness of fit to the data and good skill in capturing probability distributions with different shapes. The authors present seasonal climatologies of the parameters of the derived distribution for the chosen 60-yr period and demonstrate that applying the PDF-based concept to the analysis of cloud cover allows identification of areas where similar mean cloud amounts can be produced by probability distributions with very different shapes. The roles of the different parameters of the distribution in producing the observed cloud conditions in different regions of the World Ocean are discussed. The application of the derived probability distribution allows for accurate estimation of the percentiles of the distribution, which represent the probabilities of specific cloud conditions. These probabilities are presented for both total and low cloud cover, as well as for daytime and nighttime. The authors also discuss the applicability of the suggested distribution for the validation of different cloud cover data products over the globe and the prospects of additional applications.

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.

Continous updating and refinement of land cover data product

Continous updating and refinement of land cover data product

The Impact of Land Cover Change on Ecosystem Service Values in Urban Agglomerations along the Coast of the Bohai Rim, China

Local ecosystem services have been significantly affected by land cover changes associated with rapid urbanization in China. Based on the 2000 and 2010 land cover data products with 30-m resolution, we examined the similarities and differences in the impacts of land cover change on ecosystem service values (ESV) at three coastal urban agglomerations in China between 2000 and 2010 (Liaodong Peninsula (LP), Jing-Jin-Ji (JJJ) and Shandong Peninsula (SP)). A rapid evaluation method developed by Xie et al. (2008) was used to derive an ecosystem service value coefficient. The most significant change was an increase in artificial surfaces, due to urban expansion, which mainly occurred on cultivated land. The greatest loss in total ESV (2273 million Chinese Yuan) occurred in SP, due to the large decrease in wetland areas, because this service has the highest estimated coefficient. The second greatest loss in ESV (893 million Yuan) occurred in JJJ, due to the urban expansion of major cities. In contrast, ESV increased (72 million Yuan) in LP. This study demonstrates that urban expansion does not necessarily lead to a net decline in ESV. In conclusion, land use and land cover policymaking should consider the sustainability of ecosystem services in relation to economic growth.