Future growth pattern projections under shared socioeconomic pathways: a municipal city bottom-up aggregated study based on a localised scenario and population projections for China

Precise multi-scenario projections of future economic outputs based on localised interpretations of global scenarios and major growth drivers are important for understanding long-term economic changes. However, few studies have focussed on localised interpretations, and many assume regional uniformity or use key parameters that are recursive or extrapolated by mathematical methods. This study provides a more intuitive and robust economic framework for projecting regional economic growth based on a neoclassical economic model and shared socioeconomic pathways (SSPs) scenarios. A non-uniform version of SSP2 (the middle-of-the-road scenario) was developed, and more detailed population projections for China were adopted using municipal-level data for 340 districts and parameter settings based on China’s recent development. The results show that China’s GDP will vary substantially across SSPs by 2050. Per capita GDP ranges from 19,300 USD under SSP3 (fragmentation) to 41,100 USD under SSP5 (conventional development). Per capita GDP under SSP1 (sustainability) is slightly higher than under SSP2, but lower on average than under SSP5. However, SSP1 is a better choice overall because environmental quality and equity are higher. Per capita GDP growth will generally be higher in relatively low-income regions by 2050, and the upper-middle-income provinces will become China’s new engine for economic growth

Regionalizing Aquatic Ecosystems Based on the River Subbasin Taxonomy Concept and Spatial Clustering Techniques

Aquatic ecoregions were increasingly used as spatial units for aquatic ecosystem management at the watershed scale. In this paper, the principle of including land area, comprehensiveness and dominance, conjugation and hierarchy were selected as regionalizing principles. Elevation and drainage density were selected as the regionalizing indicators for the delineation of level I aquatic ecoregions, and percent of construction land area, percent of cultivated land area, soil type and slope for the level II. Under the support of GIS technology, the spatial distribution maps of the two indicators for level I and the four indicators for level II aquatic ecoregion delineation were generated from the raster data based on the 1,107 subwatersheds. River subbasin taxonomy concept, two-step spatial clustering analysis approach and manual-assisted method were used to regionalize aquatic ecosystems in the Taihu Lake watershed. Then the Taihu Lake watershed was divided into two level I aquatic ecoregions, including Ecoregion I1 and Ecoregion I2, and five level II aquatic subecoregions, including Subecoregion II11, Subecoregion II12, Subecoregion II21, Subecoregion II22 and Subecoregion II23. Moreover, the characteristics of the two level I aquatic ecoregions and five level II aquatic subecoregions in the Taihu Lake watershed were summarized, showing that there were significant differences in topography, socio-economic development, water quality and aquatic ecology, etc. The results of quantitative comparison of aquatic life also indicated that the dominant species of fish, benthic density, biomass, dominant species, Shannon-Wiener diversity index, Margalef species richness index, Pielou evenness index and ecological dominance showed great spatial variability between the two level I aquatic ecoregions and five level II aquatic subecoregions. It reflected the spatial heterogeneities and the uneven natures of aquatic ecosystems in the Taihu Lake watershed

Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change

Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious ‘plaque’ phenomenon and image blurring in fused images, and developing relatively simple and easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes

Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios

Mapping burn severity of forest fires can contribute significantly to understanding, quantifying and monitoring of forest fire severity and its impacts on ecosystems. In recent years, several remote sensing-based methods for mapping burn severity have been reported in the literature, of which the implementations are mainly dependent on several field plots. Therefore, it is a challenge to develop alternative method of mapping burn severity using limited number of field plots. In this study, we proposed a support vector regression based method using multi-temporal satellite data to map the burn severity, evaluated its performance by calculating correlations between the predicted and the observed Composite Burn Index, and compared the performance with that of the regression analysis method (based on dNBR). The results show that the performance of support vector regression based mapping method is more accurate (RMSE = 0.46–0.57) than that of regression analysis method (RMSE = 0.53–0.68). Even with fewer training sets, it can map the detailed distribution of burn severity of forest fires and can achieve relatively better generalization, compared to regression analysis burn severity mapping methods. It could be concluded that the proposed support vector regression based mapping method is an alternative to the regression analysis method in small sample size scenarios. This method with excellent generalization performance should be recommended for future studies on burn severity of forest fires

Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user’s accuracies of sedge swamp and paddy respectively

An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis

High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is difficult to obtain remote sensing images with both high spatial and temporal resolution. The spatiotemporal fusion model is a crucial method to solve this problem. The spatial and temporal adaptive reflectivity fusion model (STARFM) and its improved models are the most widely used spatiotemporal adaptive fusion models. However, the existing spatiotemporal adaptive reflectivity fusion model and its improved models have great uncertainty in selecting neighboring similar pixels, especially in spatially heterogeneous areas. Therefore, it is difficult to effectively search and determine neighboring spectrally similar pixels in STARFM-like models, resulting in a decrease of imagery fusion accuracy. In this research, we modify the procedure of neighboring similar pixel selection of ESTARFM method and propose an improved ESTARFM method (I-ESTARFM). Based on the land cover endmember types and its fraction values obtained by spectral mixing analysis, the neighboring similar pixels can be effectively selected. The experimental results indicate that the I-ESTARFM method selects neighboring spectrally similar pixels more accurately than STARFM and ESTARFM models. Compared with the STARFM and ESTARFM, the correlation coefficients of the image fused by the I-ESTARFM with that of the actual image are increased and the mean square error is decreased, especially in spatially heterogeneous areas. The uncertainty of spectral similar neighborhood pixel selection is reduced and the precision of spatial-temporal fusion is improved