Urban expansion and agricultural land loss in China: A multiscale perspective

China’s rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China’s total urban area increased from 31,076 km2 in 2001 to 80,887 km2 in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km2 of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km2 or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai–Nanjing–Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km2) and 6.05% (2702 km2) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km2) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China’s rapid urbanization

From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale

Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales

The impacts of land cover spatial combination on nighttime light intensity in 2010 and 2020: a case study of Fuzhou, China

Abstract As human activities highly depend on the land resources and changed the land cover (LC) condition, the relationship between LC and nighttime light (NTL) intensity has been widely analyzed to support the foundation of NTL applications and help explain the drivers of urban economic development. However, previous studies always paid attention to the effect of each LC type on NTL intensity, with limited consideration of the joint effects of any two LC types. To fill this gap, this study measured the land cover spatial combination (LCSC) by using a spatial adjacency matrix, and then analyzed its impacts on NTL intensity based on an extreme gradient boosting (XGBoost) regression model with the assistant of sharpley additive explanations (SHAP) method. Our results presented that the LCSC can better (R2 of 82.4% and 98.1% in 2010 and 2020) explain the relationship between LC and NTL intensity with the traditional LC metrics (e.g., area and patch count), since the LCSC is much more sensitive to the diverse land functions. It is noteworthy that the impacts, as well as their dynamics, of LCSC between any two LC types on NTL intensity are various. LCSC associated with artificial surface contributed more to NTL intensity. In detail, the LCSC of water/wetland and artificial surface can increasingly promote the NTL intensity while the LCSC of grassland/forest and artificial surface has a decreasing or inverse U-shaped contribution to NTL intensity. Whereas LCSC associated with non-artificial surface were not conducive to the increase in NTL intensity due to high vegetation density. We also provided three implications to help further urbanization process and discussed the applications of LCSC

Investigating the Temporal and Spatial Variability of Total Ozone Column in the Yangtze River Delta Using Satellite Data: 1978–2013

The objective of this work is to analyze the temporal and spatial variability of the total ozone column (TOC) trends over the Yangtze River Delta, the most populated region in China, during the last 35 years (1978–2013) using remote sensing-derived TOC data. Due to the lack of continuous and well-covered ground-based TOC measurements, little is known about the Yangtze River Delta. TOC data derived from the Total Ozone Mapping Spectrometer (TOMS) for the period 1978–2005 and Ozone Monitoring Instrument (OMI) for the period 2004–2013 were used in this study. The spatial, long-term, seasonal, and short-term variations of TOC in this region were analyzed. For the spatial variability, the latitudinal variability has a large range between 3% and 13%, and also represents an annual cycle with maximum in February and minimum in August. In contrast, the longitudinal variability is not significant and just varies between 2% and 4%. The long-term variability represented a notable decline for the period 1978–2013. The ozone depletion was observed significantly during 1978–1999, with linear trend from (−3.2 ± 0.7) DU/decade to (−10.5 ± 0.9) DU/decade. As for seasonal variability, the trend of TOC shows a distinct seasonal pattern, with maximum in April or May and minimum in October or November. The short-term analysis demonstrates the day-to-day changes as well as the six-week system persistence of the TOC. The results can provide comprehensive descriptions of the TOC variations in the Yangtze River Delta and benefit climate change research in this region

Detecting annual anthropogenic encroachment on intertidal vegetation using full Landsat time-series in Fujian, China

Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Landsat time-series (FLTS) is proposed to detect the year of change (YOC) of intertidal vegetation converted to impervious surfaces (ISs) and artificial ponds (APs), and the condition of the remaining intertidal vegetation was also assessed by FLTS, in the Fujian province, a subtropical coastal area lying in southeast China. The accuracies of the YOC detection of intertidal vegetation converted to IS and AP were 91.84% and 72.73%, with mean absolute errors of 0.26 and 1.06, respectively. The total areas of intertidal vegetation encroached by IS and AP were 31.68 km2 and 23.85 km2, respectively. Most ISs were developed later than 2010, and most APs were developed earlier than 2005, which are highly related to the implementation of local policies for economic development. The remaining intertidal vegetation in growing, stable, and degraded conditions were 43.05%, 56.38%, and 0.57%, respectively. The results indicated that areas of intertidal vegetation were reclaimed for anthropogenic uses at a considerable rate, although the intertidal vegetation still increased owing to natural development after the establishment of natural reserves. The study demonstrates that the FLTS has capacities in monitoring the dynamics in coastal zones solely for its dense earth observations

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing

Antidiabetic Activity of Polysaccharides from Tuberous Root of Liriope spicata var. prolifera in KKAy Mice

Tuberous root of Liriope spicata var. prolifera has been widely used as a traditional Chinese medicine for the treatment of diabetes. The present study investigated the antidiabetic effect and the potential mechanisms of two new polysaccharides (LSP1, LSP2) and the total polysaccharides (TLSP), isolated from the tuberous roots. Upon the intragastric administration in obese insulin-resistant diabetic KKAy mice for 28 days, TLSP, LSP1, and LSP2 all caused a remarkable decrease of fasting blood glucose and significant improvement of insulin resistance and serum lipid metabolism in diabetic mice. In addition, liver histological analysis showed that TLSP, LSP1, and LSP2 significantly ameliorated the hepatocyte hypertrophy and decreased the lipid accumulation in the mice liver. Further experiments suggested that TLSP, LSP1, and LSP2 effectively inhibited hepatic gluconeogenesis and increased hepatic glycolysis and hepatic glycogen content. Furthermore, the mechanistic analysis showed the increased expression of insulin-receptor α subunit, insulin-receptor substrate-1, phosphatidylinositol 3-kinase, and peroxisome proliferators-activated receptors γ. These results suggested that TLSP, LSP1, and LSP2 manifest strong antidiabetic activity, therefore hold a great promise for therapeutic application in diabetic therapy and other related metabolic disorders

Flexible NAD(+)Binding in Deoxyhypusine Synthase Reflects the Dynamic Hypusine Modification of Translation Factor IF5A

The eukaryotic and archaeal translation factor IF5A requires a post-translational hypusine modification, which is catalyzed by deoxyhypusine synthase (DHS) at a single lysine residue of IF5A with NAD(+)and spermidine as cofactors, followed by hydroxylation to form hypusine. While human DHS catalyzed reactions have been well characterized, the mechanism of the hypusination of archaeal IF5A by DHS is not clear. Here we report a DHS structure fromPyrococcus horikoshii OT3(PhoDHS) at 2.2 angstrom resolution. The structure reveals two states in a single functional unit (tetramer): two NAD(+)-bound monomers with the NAD(+)and spermidine binding sites observed in multi-conformations (closed and open), and two NAD(+)-free monomers. The dynamic loop region V288-P299, in the vicinity of the active site, adopts different positions in the closed and open conformations and is disordered when NAD(+)is absent. Combined with NAD(+)binding analysis, it is clear thatPhoDHS can exist in three states: apo,PhoDHS-2 equiv NAD(+), andPhoDHS-4 equiv NAD(+), which are affected by the NAD(+)concentration. Our results demonstrate the dynamic structure ofPhoDHS at the NAD(+)and spermidine binding site, with conformational changes that may be the response to the local NAD(+)concentration, and thus fine-tune the regulation of the translation process via the hypusine modification of IF5A