In the North China Plain, coal mining subsided lakes are surface water bodies that form after the conclusion of coal mining. In China, mining subsided lakes are often transformed into artificial wetland parks for ecological restoration. While many studies have focused on evaluating coal mining subsidence lake ecosystem service value and water pollution, little attention has been paid to changes in ecological regimes and ecological spillover effects before and after the reclamation of mining areas. This paper examines the Pan'an Lake artificial wetland in Jiawang District, Xuzhou, as a case study. Changes in the ecological regime of the mining subsidence area before and after land reclamation and corresponding spatial spillover effect on land prices are assessed based on remote sensing, GIS raster calculations and geostatistical methods. The results show that the ecosystem service value and ecological storage capacity changed significantly after the mining subsided lake was transformed into an artificial wetland and the wetland ecosystem has been developing well with significantly positive spillover effects on surrounding land prices. From 2008 to 2017, service functions of the artificial wetland ecosystem of Pan'an Lake increased by 81.95%, and the system’s ecological storage capacity increased from RMB 6,754 yuan/hm² in 2008 to RMB 12,289 yuan/hm² in 2017. The average impact of the Pan'an Lake artificial wetland on the spillover effects of surrounding residential land prices was measured at RMB 195.18 yuan/m², and the total spillover value of planned residential land in the study area was measured at RMB 805,422,100 yuan. The present study can serve as a useful guide for evaluating the economic feasibility of land reclamation planning and ecological restoration in mining subsidence areas.

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel.

Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R² = 0.58) is higher than that of using only optical images (R² = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

Conventional Coordinate Rotation Digital Computer (CORDIC) algorithm is an effective implementation of trigonometric function, and it has been widely applied in digital signal processing. In this paper, we propose a novel CORDIC architecture based on Scaling Free (SF) theory, which adopts adaptive recoding method to reduce the iterations and eliminate the scaling factor. The proposed scheme has been synthesized using 65nm CMOS technology with standard cell library. Compared with SFB4C, our scheme saves 21.11% to 34.64% hardware-overhead with no performance penalty. © IEICE 2012.