Anping Liao

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.

Preliminary analysis of spatiotemporal pattern of global land surface water

Land surface water (LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land surface water and a continuous measuring of its dynamics can support to diagnose the global ecosystem and environment. Based on the Global Land 30-water 2000 and Global Land 30-water 2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water under scale-levels of global, latitude and longitude, continents, and climate zones. The Global Land 30-water products were corrected the temporal inconsistency of original remotely sensed data using MODIS time-series data, and then calculated the indices such as water area, water ration and coefficient of spatial variation for further analysis. Results show that total water area of land surface is about 3.68 million km2 (2010), and occupies 2.73% of land area. The spatial distribution of land surface water is extremely uneven and is gathered mainly in mid- to high-latitude area of the Northern Hemisphere and tropic area. The comparison of water ratio between 2000 and 2010 indicates the overall fluctuation is small but spatially differentiated. The Global Land 30-water products and the statistics provided the fundamental information for analyzing the spatial distribution pattern and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.

A comparative analysis of five global cropland datasets in China

Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including GlobalLand30, FROM-GLC, GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of GlobeLand30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using GlobeLand30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and GlobCover having the poorer accuracies. Regarding the spatial location of cropland, GlobeLand30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with GlobCover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of GlobCover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies.

Geospatial knowledge-based verification and improvement of GlobeLand30

Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been studied since the early 1980s, mature methods and efficient supporting tools are still lacking. This paper presents a geospatial knowledge-based verification and improvement approach for global land cover (GLC) mapping at 30-m resolution. A set of verification rules is derived from three types of land cover and its change knowledge (natural, cultural and temporal constraints). A group of web-based supporting tools is developed to facilitate the integration of and access to large amounts of ancillary data and to support online data manipulation and analysis as well as collaborative verification workflows. With this approach, two 30-m GLC datasets (GlobeLand-2000 and GlobeLand-2010) were verified and modified. The results indicate that the data quality of GlobeLand30 has been largely improved.

Global mapping of artificial surfaces at 30-m resolution

Urbanization is expected to accelerate with population growth and economic development at the global scale. The artificial surface is the main land cover form of urbanization. On the one hand, urbanization provides spaces for industry, economic activities and residence. On the other hand, artificial surfaces change the earth surface to a large extent, thus significantly affecting natural processes such as the heat exchange, hydrological processes and ecological balance. Therefore, the global mapping of artificial surfaces is valuable for both natural science and social science. This study produced the global artificial surface maps at 30-m resolution for two base-years using the satellite images acquired around 2000 and 2010. First, we proposed a new definition of “artificial surface” based on patch level with consideration of its geographic meaning and image features at 30-m resolution. Second, pixel-based and object-based image processing techniques were combined for the extraction of artificial surface patches. Finally, human editing and a quality control system were employed to guarantee the quality of global mapping. Independent accuracy assessments show that the user’s accuracy of this product is higher than 80%. It can be concluded that the product is the most reliable one among all the available global datasets of artificial surfaces (or related types). The data can significantly contribute to various research fields, such as urbanization and ecosystem assessment.

A Framework for Integrating Heterogeneous Spatial Information and Applications Adaptively Based on Multi-agent and Web Service

Spatial data is a main carrier and media of social information. But the structure and environment also have some interoperability problems. In this paper, we present a framework for integrating heterogeneous spatial information and applications based on multi-agent and web services. A Contract-First agent model is proposed to interact with the data interfaces of different spatial data providers, which can adaptively serializes the spatial data of multiple sources to be an OGC GI Service of the same protocols and interfaces. Then, we introduced a messages conversion agent model, which can adapt multiple requests and response comprehensible information according to individual application.

Check and modification of GlobeLand30 with MODIS NDVI

MODIS NDVI time-series data could indicate vegetation status in each season and have been widely used for land cover classification and studies in the fields of vegetation and land degradation monitoring. During global land cover mapping project at 30m resolution aiming at developing high quality product, there were mistakes of classification between bareland and vegetation in GlobeLand30 data in regions surrounding deserts because that the dates of some images are not in growing season. In this paper, we proposed a method to check GlobeLand30 data of 2010 in these areas. Max NDVI value of MODIS NDVI time-series data is chosen to represent growing conditions of vegetation. And then vegetation fraction (VF) calculated from the max NDVI value is divided into bareland and vegetation based on the definition of bareland that VF of bareland is lower than 10%. The dimidiated VF maps are employed to check GlobeLand30 with the help of high resolution images and other references. Finally, errors found out by steps above are modified with VF maps and segmentation objects of images at 30m resolution. 149 map sheets of GlobeLand30 were checked and 105 of them were modified. 13409 samples in 10 map sheets totally were selected to assess the effect of the approach. The result showed that the accuracy after modification of GlobeLand30 was higher than that before modification.

Global Land Surface Water Mapping and Analysis at 30 m Spatial Resolution for Years 2000 and 2010

Land surface water (LSW), one of the important components of land cover, is indispensable and important basic information for climate change studies, ecological environmental assessment, macro-control analysis, etc. In 2010 China launched a global land cover (GLC) mapping project, the aim of which was to produce a 30 m GLC data product (GlobeLand30) with 10 classes for years 2000 and 2010. This chapter describes an overall study on LSW in the project. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite imagery and other remotely sensed data, the program achieves an effective overlay of global multi-spectral images at 30 m resolution for two base years, namely, 2000 and 2010. The water information was extracted in an elaborate way by combining a simple operation of pixel-based classification with a comprehensive utilization of various rules and knowledge through object-oriented classification, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global LSW data results, including GlobeLand30-Water 2000 and GlobeLand30-Water 2010, are classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96 %. Based on the GlobeLand30-Water 2000/2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water at global scale. The GlobeLand30-Water products were corrected for the temporal inconsistency of the original remotely sensed data using MODIS time-series data, and then indices such as water area, water ration and coefficient of spatial variation were calculated for further analysis. Results show that the total water area of land surface is about 3.68 million km2 (2010), and occupies 2.73 % of land area. The GlobeLand30-Water products and their statistics provide fundamental information for analyzing the spatial distribution and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.