Spatial-temporal dynamics and driving factor analysis of urban ecological land in Zhuhai city, China

Cross-sensitivity analysis of land use transition and ecological service values in rare earth mining areas in southern China

Exploring the cross-sensitivity between land use transformation and ecological service values in rare earth mining areas is of great significance for the development of ecological protection and restoration in rare earth mining areas. To study the impact of land use changes on ecosystem service functions in rare earth mining areas, firstly, the land use change trends in the study area from 2009 to 2019 were analyzed using the land transfer matrix; then the distribution of ecosystem service values and the flow direction of ecosystem service values in the study area were measured based on the ecosystem service value equivalents; a spatial autocorrelation analysis was done on the ecosystem service values to explore their spatial distribution patterns; and finally, the cross-sensitivity coefficient was used to quantitatively assess the extent and direction of the impact of land use change on ecosystem service values. The results show that the land use types in the study area are mainly forest land and farmland, with woodland accounting for the highest proportion of the study area. The ESV changes in the study area are consistent with the trend of land use transformation, with the overall increase and decrease being comparable, and the decrease in ESV is mainly concentrated in the areas with a large increase in mining land and construction land; during the study period, the study area was significantly reduced with low-low cluster areas and the ecological environment was improved; from 2009 to 2014, the ecological sensitivity coefficient is more variable, and is more sensitive to the net conversion between water and desert, from 2014 to 2019, the ecological sensitivity coefficient is less variable, and the most sensitive is the net conversion between cultivated land and water. The study area should be reasonably developed for rare earth resources and the ecological environment around the mining area should be reasonably protected to build an ecological security pattern.

Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China

Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/km²), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R² of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao  (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (-3.48), number of patches (NP) (-3.91), and percentage of like adjacencies (PLAND) (-2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development.

Quantifying the human disturbance intensity of ecosystems and its natural and socioeconomic driving factors in urban agglomeration in South China

The impact of human activities on terrestrial ecosystems is becoming more intense than ever in history. Human disturbance analyses play important roles in appropriately managing the human-environment relationship. In this study, a human disturbance index (HDI) that uses land use and land cover data from 1980, 2000, 2010, and 2018 is proposed to assess the human disturbance of ecosystems in the Guangdong-Hong Kong-Macao Greater Bay Area. The HDI is first calculated by classifying the human disturbance intensity into seven levels and 13 categories from weak to strong in ecosystems. Then the driving factors of the HDI spatial pattern change are explored using a geographically weighted regression (GWR) model. The results showed that the spatial pattern of the HDI was high in the middle and low in the surrounding areas. The intensity of human disturbance increased, and the medium and high disturbance areas expanded during 1980-2018, especially in Guangzhou, Foshan, Shenzhen, and Dongguan. Human disturbance displayed an obvious spatial heterogeneity. The GWR model had a better explanation effect of the analysis of the HDI change drivers. The driving effect of the socioeconomic conditions was significantly stronger than that of the natural environmental. This study assists in understanding the distribution and change characteristics of the ecological environment in areas with strong human activities and provides a reference for related studies.

Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.