Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China

Land degradation has a major impact on environmental and socio-economic sustainability. Scientific methods are necessary to monitor the risk of land degradation. In this study, the environmental sensitive area index(ESAI) was utilized to assess land degradation sensitivity and convergence analysis in Korla, a typical oasis city in Xinjiang of China, which is located on the northeast border of the Tarim Basin. A total of 18 indicators depicting soil, climate, vegetation, and management qualities were used to illustrate spatial-temporal patterns of land degradation sensitivity from 1994 to 2018. We investigated the causes of spatial convergence and divergence based on the Ordinary Least Squares(OLS) and Geographically Weighted Regression(GWR) models. The results show that the branch of the Tianshan Mountains and oasis plain had a low sensitivity to land degradation, while the Tarim Basin had a high risk of land degradation. More than two-thirds of the study area can be categorized as "critical" sensitivity classes. The largest percentage(32.6%) of fragile classes was observed for 2006. There was no significant change in insensitive or low-sensitivity areas, which accounted for less than 0.4% of the entire observation period. The ESAI of the four time periods(1994–1998, 1998–2006, 2006–2010, and 2010–2018) formed a series of convergence patterns. The convergence patterns of 1994–1998 and 1998–2006 can be explained by the government's efforts to "Returning Farmland to Forests" and other governance projects. In 2006–2010, the construction of afforested work intensified, but industrial development and human activities affected the convergence pattern. The pattern of convergence in most regions between 2010 and 2018 can be attributed to the government's implementation of a series of key ecological protection projects, which led to a decrease in sensitivity to land degradation. The results of this study altogether suggest that the ESAI convergence analysis is an effective early warning method for land degradation sensitivity.

Spatiotemporal Evolution of West Africa's Urban Landscape Characteristics Applying Harmonized DMSP-OLS and NPP-VIIRS Nighttime Light(NTL)Data

Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development.Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System(DMSP-OLS)and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite(NPP-VIIRS)Nighttime Light(NTL)data,this paper investigated the characteristics of urban landscape in West Africa.Using the harmonized NTL data,spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018.We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas.In addition,we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa.The accuracy of urban area extraction based on NTL data were R^(2)=0.8314 in 2000,R^(2)=0.8809 in 2006,R^(2)=0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R^(2)=0.8426 in 2018,by using Google Earth images as validation.The results indicated that there was a high rate and acceleration of urban landscapes in West Africa,with rates of 0.0160,0.0173,0.0189,and 0.0686,and accelerations of 0.31,0.42,0.54,and 0.90 for the periods of 1998–2003,2003–2008,2008–2013,and 2013–2018,respectively.The expansion direction of urban agglomeration in West Africa during 1993–2018 was mainly from the coast to inland.However,cities located in the Sahel Region of Africa and in the middle zone expanded from north to south.Finally,the results showed that the urban landscape of West Africa was mainly in a scattered and disordered’diffusion’process,whereas only a few cities located in coastal areas experiencing the process of’coalescence’according to urban growth phase theory.This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.

Developing a new red band- SEVI-blue band (RSB) enhancement method for recognition the extra-high-voltage transmission line corridor in green mountains

Monitoring the extra-high-voltage transmission line corridor(EHVTLC)in mountains is critical for safe smart-grid operation.However,the transmission lines are so narrow that they are difficult to recognize using multispectral satellite images with a spatial resolution of 10 m.In this study,we developed a new method using the red band–shadow-eliminated vegetation index(SEVI)–blue band(RSB)composite image to enhance the EHVTLC in green mountains(named RSB-enhancement method).Using this method,the EHVTLC becomes evident in the false-color synthesis of the RSB composite of the Sentinel-2 image.Then,we recognized and extracted approximately 342.45 km of the EHVTLC in a mountainous region of Fuzhou City,China,including a 46.73 km three-parallel-lane segment of 1000 kV and a 295.72 km two-parallel-lane segment of 500 kV.Spatial analysis shows that the SEVI mean difference between the EHVTLC and the buffer zone reaches approximately 10%,and three landslides and 2.66 km^(2) soil erosion reside in the buffer zone which area is approximately 73.67 km^(2).Finally,the RSB-enhancement method can be used in other satellite images with spatial resolutions of greater than 10 m for enhancement and recognition the transmission line corridors in green mountains.

A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data

Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.