In September 2015, the 193 Member States of the United Nations (UN) unanimously adopted the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), aiming to transform the world over the next 15 years (ESDN, 2016). To meet the ambitions and demands of the 2030 Agenda, it is necessary for the global indicator framework to adequately and systematically address the issue of alternative data sources and methodologies, including geospatial information and Earth observations in the context of geographic location (UN-GGIM, 2016). For this purpose, the Inter-Agency and Expert Group on Sustainable Development Goals Indicator (IAEG-SDGs) created the Working Group on Geospatial Information (IAEG-SDGs: WGGI) to give full play to the role of geospatial data in SDGs measurement and monitoring. The Working Group reviewed global indicators through a ‘geographic location’ lens to pick out those which geospatial information can significantly support the production, and analyzed the methodological and measurements issues. This paper has discussed the progress in monitoring SDGs ever since the establishment of IAEG-SDGs: WGGI, as well as the existing problems, appropriate solutions and plans for the next stage of work.
ABSTRACT:A web-based validation is necessary for assessing the accuracy of Globalland30. As one of the obstacles in validation of global land cover data, it is a challenge to effectively select a reasonable sample dataset. Global land cover is heterogeneous and complex. However, some sampling plans based on probability and mathematical statistics don"t consider the landscape heterogeneity. To address this disadvantage of the two-rank acceptance sampling plan(TRASP) for validation of land cover data, landscape indices are used to improving this sampling plan. Landscape indicator is an typical method of quantifying landscape heterogeneity. The landscape indices based sampling combined landscape indicators and TRASP, with an innovation on the two side of computing samples" size and distributing samples. Firstly, landscape shape index(LSI) is introduced in the equation of TRASP for the sample size. Further, the validation area is divided into some small grids, and LSI is used to find more valuable hotspots from these grids to distributing samples.The theory and formulas are presented in this paper, and an example application is provided in which the sample of a land-cover map is chosen.
ABSTRACT:Land cover is one of the fundamental data sets on environment assessment, land management and biodiversity protection, etc. Hence, data quality control of land cover is extremely critical for geospatial analysis and decision making. Due to the similar remote-sensing reflectance for some land cover types, omission and commission errors occurred in preliminary classification could result to spatial inconsistency between land cover types. In the progress of post-classification, this error checking mainly depends on manual labour to assure data quality, by which it is time-consuming and labour intensive. So a method required for automatic detection in postclassification is still an open issue. From logical inconsistency point of view, an inconsistency detection method is designed. This method consist of a grids extended 4-intersection model (GE4IM) for topological representation in single-valued space, by which three different kinds of topological relations including disjoint, touch, contain or contained-by are described, and an algorithm of region overlay for the computation of spatial inconsistency. The rules are derived from universal law in nature between water body and wetland, cultivated land and artificial surface. Through experiment conducted in Shandong Linqu County, data inconsistency can be pointed out within 6 minutes through calculation of topological inconsistency between cultivated land and artificial surface, water body and wetland. The efficiency evaluation of the presented algorithm is demonstrated by Google Earth images. Through comparative analysis, the algorithm is proved to be promising for inconsistency detection in land cover data.
ABSTRACT:The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.
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