Showing papers by "Dengsheng Lu published in 2021"
TL;DR: Wang et al. as discussed by the authors investigated the environmental impacts of cropland redistribution in China and suggested that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits.
Abstract: ABSTRACT Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. As a result of urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha–1), croplands expanded to marginal lands in northeast (∼4.5 t ha–1) and northwest China (∼2.9 t ha–1) during 1990–2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, for example increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%) and decreasing natural habitats (−3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified because of the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits.
53 citations
46 citations
TL;DR: Kuang et al. as mentioned in this paper proposed a new mapping strategy to acquire the multitemporal and fractional information of the essential urban land cover types at a national scale through synergizing the advantage of both big data processing and human interpretation with the aid of geoknowledge.
Abstract: . Accurate and timely maps of urban underlying land
properties at the national scale are of significance in improving habitat
environment and achieving sustainable development goals. Urban impervious
surface (UIS) and urban green space (UGS) are two core components for
characterizing urban underlying environments. However, the UIS and UGS are
often mosaicked in the urban landscape with complex structures and
composites. The “hard classification” or binary single type cannot be used
effectively to delineate spatially explicit urban land surface property.
Although six mainstream datasets on global or national urban land use and land cover
products with a 30 m spatial resolution have been developed, they only provide
the binary pattern or dynamic of a single urban land type, which cannot
effectively delineate the quantitative components or structure of
intra-urban land cover. Here we propose a new mapping strategy to acquire
the multitemporal and fractional information of the essential urban land
cover types at a national scale through synergizing the advantage of both big
data processing and human interpretation with the aid of geoknowledge. Firstly,
the vector polygons of urban boundaries in 2000, 2005, 2010, 2015 and 2018
were extracted from China's Land Use/cover Dataset (CLUD) derived from
Landsat images. Secondly, the national settlement and vegetation percentages
were retrieved using a sub-pixel decomposition method through a random forest
algorithm using the Google Earth Engine (GEE) platform. Finally, the products of
China's UIS and UGS fractions (CLUD-Urban) at a 30 m resolution were
developed in 2000, 2005, 2010, 2015 and 2018. We also compared our products
with six existing mainstream datasets in terms of quality and accuracy. The
assessment results showed that the CLUD-Urban product has higher accuracies
in urban-boundary and urban-expansion detection than other products and in
addition that the accurate UIS and UGS fractions were developed in each
period. The overall accuracy of urban boundaries in 2000–2018 are over
92.65 %; and the correlation coefficient ( R ) and root mean square errors
(RMSEs) of UIS and UGS fractions are 0.91 and 0.10 (UIS) and 0.89 and 0.11 (UGS),
respectively. Our result indicates that 71 % of pixels of urban land were
mosaicked by the UIS and UGS within cities in 2018; a single UIS
classification may highly increase the mapping uncertainty. The high spatial
heterogeneity of urban underlying covers was exhibited with average
fractions of 68.21 % for UIS and 22.30 % for UGS in 2018 at a national
scale. The UIS and UGS increased unprecedentedly with annual rates of
1605.56 and 627.78 km2 yr−1 in 2000–2018, driven by fast
urbanization. The CLUD-Urban mapping can fill the knowledge gap in
understanding impacts of the UIS and UGS patterns on ecosystem services and
habitat environments and is valuable for detecting the hotspots of waterlogging
and improving urban greening for planning and management practices. The
datasets can be downloaded from https://doi.org/10.5281/zenodo.4034161
(Kuang et al., 2020a).
40 citations
TL;DR: In this paper, the spatial patterns of vegetation coverage and its change were described by spatial clusters and outliers, and the framework was established to find the potential area for reduction of habitat fragmentation.
13 citations
TL;DR: In this paper, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban impervious surfaces area and green space (GS) fractions for the years 2015 and circa 2020, using random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine.
Abstract: Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability.
10 citations
TL;DR: Moso bamboo is an evergreen plant that extensively distributes in subtropical regions and has some unique characteristics: high growth rate, short h... as discussed by the authors The Moso bamboo forest is a type of evergreen evergreen tree.
Abstract: Moso bamboo is an evergreen plant that extensively distributes in subtropical regions. Comparing to other forest types, Moso bamboo forest has some unique characteristics: high growth rate, short h...
10 citations
TL;DR: In this article, a hierarchy-based classifier based on optimization of selected variables in each tree node is developed to conduct urban vegetation classification through incorporation of canopy height features into spectral and textural data.
4 citations
TL;DR: In this paper, the identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensi...
Abstract: Accurate identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensi...
4 citations
TL;DR: Wang et al. as discussed by the authors proposed a uniform procedure for mapping Eucalyptus plantations based on fused medium-high spatial resolution satellite datasets, where six key variables were determined by using the Z-statistic and random forest methods.
2 citations