scholarly journals Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015

2021 ◽  
Vol 13 (14) ◽  
pp. 2797
Author(s):  
Kehong Hu ◽  
Zhen Zhang ◽  
Hongliang Fang ◽  
Yijie Lu ◽  
Zhengnan Gu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Spatial Differentiation ◽  
Sanjiang Plain ◽  
Driving Factors ◽  
Anthropogenic Factors ◽  
Area Index ◽  
The Sanjiang Plain ◽  
Clumping Index ◽  
Vegetation Structures

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.

scholarly journals Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

2021 ◽  
Vol 13 (4) ◽  
pp. 684
Author(s):  
Shilun Zhou ◽  
Wanchang Zhang ◽  
Shuhang Wang ◽  
Bo Zhang ◽  
Qiang Xu
Keyword(s):  
Vegetation Dynamics ◽  
Sanjiang Plain ◽  
Anthropogenic Factors ◽  
Amur River Basin ◽  
Amur River ◽  
The Sanjiang Plain ◽  
Vegetation Indexes ◽  
The Amur River ◽  
Natural And Anthropogenic Factors ◽  
Hydrological Variables

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

scholarly journals Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial form: A Case Study of Jiangsu Province, China

2021 ◽  
Vol 13 (6) ◽  
pp. 3121
Author(s):  
Guoping Xiong ◽  
Xin Cao ◽  
Nicholas A. S. Hamm ◽  
Tao Lin ◽  
Guoqin Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Regional Development ◽  
Driving Factors ◽  
Spatial Expansion ◽  
Spatial Form ◽  
Coordinated Development ◽  
Urbanization Level ◽  
Driving Mechanisms ◽  
Urbanization Development ◽  
Southern Jiangsu

Unbalanced regional development is widespread, and the imbalance of regional development in developing countries with rapid urbanization is increasingly apparent. This threatens the sustainable development of the region. Promoting the coordinated development of the region has become a hot spot of scientific research and a major practical need. Taking 99 counties of Jiangsu Province China, a typical coastal plain region, as the basic research unit, this paper explores the unbalanced development characteristics of the regional urban spatial form using three indicators: urban spatial expansion size, development intensity, and distribution aggregation degree. Then, their driving mechanisms were evaluated using spatial autocorrelation analysis, Pearson correlation analysis, linear regression, and geographically weighted regression. Our results found that the areas with larger urban spatial expansion size and development intensity were mainly concentrated in southern Jiangsu, where there was a positive spatial correlation between them. We found no agglomeration phenomenon in urban spatial distribution aggregation degree. From the perspective of driving factors: economics was the main driving factor of urban spatial expansion size; urbanization level and urbanization quality were the main driving factors of urban spatial development intensity. Natural landform and urbanization level are the main driving factors of urban spatial distribution aggregation degree. Finally, we discussed the optimization strategy of regional coordinated development. The quality of urbanization development and regional integration should be promoted in Southern Jiangsu. The level of urbanization development should be improved relying on rapid transportation to develop along the axis in central Jiangsu. The economic size should be increased, focusing on the expansion of the urban agglomeration in northern Jiangsu. This study will enrich the perspective of research on the characteristics and mechanisms of regional urban spatial imbalance, and helps to optimize and regulate the imbalance of regional urban development from multiple perspectives.

scholarly journals Levels, Inventory, and Risk Assessment of Heavy Metals in Wetland Ecosystem, Northeast China: Implications for Snow Cover Monitoring

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2161
Author(s):  
Fuxiang Zhang ◽  
Bo Meng ◽  
Shang Gao ◽  
Rupert Hough ◽  
Peng Hu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Snow Cover ◽  
Environmental Quality ◽  
Regional Scale ◽  
Class Ii ◽  
Sanjiang Plain ◽  
Preliminary Evaluation ◽  
Monitoring Method ◽  
The Sanjiang Plain ◽  
Water Pollution Index

Snow cover is a unique environmental medium in cold regions that can cause potential risks to the ecological environment, due to the release of pollutants that are stored in it. In this study, the Qixing River wetland, located in the Sanjiang Plain of China, was taken as the target research area. Heavy metals in snow cover, including Cu, Ni, Cr, Cd, Pb, and Zn were measured at 19 sampling sites. The results showed that the average concentrations of heavy metals were: Zn (103.46 ± 39.16) > Pb (13.08 ± 4.99) > Cr (11.97 ± 2.82) > Ni (9.55 ± 4.96) > Cu (6.19 ± 1.79) > Cd (0.55 ± 0.25) μg·L−1. Cr and Zn were between Class I and Class II in the “Environmental Quality Standards for Surface Water” of China (GB3838-2002). Pb in snow exceeded the upper limit of Class II, and was significantly higher than concentrations measured in water samples from the Qixing River wetland (p < 0.05), indicating that atmospheric deposition during winter was the major source of Pb. The water pollution index (WPI) indicated that 61.0% of samples could be considered of “clean” status, while the contribution of Zn, Pb, and Cr to WPI were 33.3%, 21.0%, and 19.3%, respectively. A preliminary evaluation of heavy metal inventory in snow cover showed that the residue level of Zn was the highest (2313.57 ± 1194.67 μg·m−2), while Cd was the lowest (13.91 ± 10.45 μg·m−2). The areas with high residues of heavy metals were all located near the buffer zone of the wetland (except for Zn), where snow depth tended to be greatest. Exposure analysis indicated that the risks to winter resident birds from snow ingestion was minimal, but it should be noted that the exposure risk was higher in birds with lower bodyweights. This study provides important information and scientific knowledge on the pollution characteristics and residue inventory of heavy metals in wetland ecosystems, while the results can also provide a monitoring method, reflecting atmospheric environmental quality at a local or regional scale.

scholarly journals An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2730
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Land Surface ◽  
Climatic Factors ◽  
Standardized Precipitation Index ◽  
Landsat 8 ◽  
Drought Frequency ◽  
Moisture Index ◽  
Area Index ◽  
Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

scholarly journals Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050

2021 ◽  
Vol 18 (3) ◽  
pp. 938
Author(s):  
Luoman Pu ◽  
Jiuchun Yang ◽  
Lingxue Yu ◽  
Changsheng Xiong ◽  
Fengqin Yan ◽  
...  
Keyword(s):  
Land Use ◽  
Food Security ◽  
Northeast China ◽  
Land Use Changes ◽  
Sanjiang Plain ◽  
Potential Production ◽  
Potential Yield ◽  
The Sanjiang Plain ◽  
Ecological Zones ◽  
The Future

Crop potential yields in cropland are the essential reflection of the utilization of cropland resources. The changes of the quantity, quality, and spatial distribution of cropland will directly affect the crop potential yields, so it is very crucial to simulate future cropland distribution and predict crop potential yields to ensure the future food security. In the present study, the Cellular Automata (CA)-Markov model was employed to simulate land-use changes in Northeast China during 2015–2050. Then, the Global Agro-ecological Zones (GAEZ) model was used to predict maize potential yields in Northeast China in 2050, and the spatio-temporal changes of maize potential yields during 2015–2050 were explored. The results were the following. (1) The woodland and grassland decreased by 5.13 million ha and 1.74 million ha respectively in Northeast China from 2015 to 2050, which were mainly converted into unused land. Most of the dryland was converted to paddy field and built-up land. (2) In 2050, the total maize potential production and average potential yield in Northeast China were 218.09 million tonnes and 6880.59 kg/ha. Thirteen prefecture-level cities had maize potential production of more than 7 million tonnes, and 11 cities had maize potential yields of more than 8000 kg/ha. (3) During 2015–2050, the total maize potential production and average yield decreased by around 23 million tonnes and 700 kg/ha in Northeast China, respectively. (4) The maize potential production increased in 15 cities located in the plain areas over the 35 years. The potential yields increased in only nine cities, which were mainly located in the Sanjiang Plain and the southeastern regions. The results highlight the importance of coping with the future land-use changes actively, maintaining the balance of farmland occupation and compensation, improving the cropland quality, and ensuring food security in Northeast China.

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