An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China

2021 ◽  
Vol 13 (23) ◽  
pp. 4846
Author(s):  
Yubo Zhang ◽  
Jiuchun Yang ◽  
Dongyan Wang ◽  
Jing Wang ◽  
Lingxue Yu ◽  
...  
Keyword(s):  
Land Use ◽  
High Precision ◽  
Northeast China ◽  
Prediction Models ◽  
Management Strategies ◽  
Global Environmental Change ◽  
Climate Impact ◽  
Fine Grained ◽  
Land System ◽  
Spatial Features

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.

scholarly journals Elevated Fe and Mn Concentrations in Groundwater in the Songnen Plain, Northeast China, and the Factors and Mechanisms Involved

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2392
Author(s):  
Yuanzheng Zhai ◽  
Xinyi Cao ◽  
Xuelian Xia ◽  
Bin Wang ◽  
Yanguo Teng ◽  
...  
Keyword(s):  
Land Use ◽  
Northeast China ◽  
Management Strategies ◽  
Oxygen Demand ◽  
Water Bodies ◽  
Agricultural Activities ◽  
Land Use Type ◽  
Songnen Plain ◽  
Fe And Mn ◽  
Mn Concentrations

Groundwater is an essential source of drinking and irrigation water. However, elevated Fe and Mn concentrations in groundwater have been found in recent decades, which can adversely affect human health and decrease crop quality and yields. The roles of hydrogeochemical changes and groundwater pollution (exogenous reductive material inputs) in this have not been studied adequately. We determined the distribution of Fe and Mn concentrations in groundwater in the Songnen Plain, northeast China, which is known for elevated Fe and Mn concentrations, and investigated the factors and mechanisms involved in causing the elevated concentrations. Chemical and statistical analyses indicated that the Fe and Mn concentrations in groundwater significantly correlated with climate parameters (precipitation and temperature), surface features (altitude, distance from a river, soil type, soil texture, and land use type) and hydrogeochemical characteristics (chemical oxygen demand and NH4+, NO3−, and P concentrations). In particular, the Fe and Mn concentrations in groundwater are higher in areas containing paddy fields and water bodies than other land use type areas. Areas with groundwater containing ultra-high Fe and Mn concentrations have almost all of the favorable factors. The main reasons for the elevated Fe and Mn concentrations in groundwater in the study area are the Fe/Mn mineral-rich strata and soil with abundant organic matter acting as sources of Fe and Mn to the groundwater and the reductive environment in the lower terrain and areas containing water bodies favoring Fe and Mn dissolution in the groundwater. Inputs of pollutants from agricultural activities have caused the Fe and Mn concentrations in groundwater to increase. Future studies should be performed to study interactions between pollutants from agricultural activities and Fe and Mn in groundwater and develop environmental management strategies for preventing future increases in Fe and Mn concentrations and promoting sustainable development of agriculture.

scholarly journals A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

2021 ◽  
Vol 10 (5) ◽  
pp. 348
Author(s):  
Zhenbo Du ◽  
Bingbo Gao ◽  
Cong Ou ◽  
Zhenrong Du ◽  
Jianyu Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Type ◽  
Northeast China ◽  
Black Soil ◽  
Factors Affecting ◽  
Soil Zone ◽  
Rainfed Farming ◽  
Study Results

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

scholarly journals Changes in Coastal Agricultural Land Use in Response to Climate Change: An Assessment Using Satellite Remote Sensing and Household Survey Data in Tien Hai District, Thai Binh Province, Vietnam

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 627
Author(s):  
Duong H. Nong ◽  
An T. Ngo ◽  
Hoa P. T. Nguyen ◽  
Thuy T. Nguyen ◽  
Lan T. Nguyen ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Management Strategies ◽  
Household Survey ◽  
Extreme Weather Events ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Uses

We analyzed the agricultural land-use changes in the coastal areas of Tien Hai district, Thai Binh province, in 2005, 2010, 2015, and 2020, using Landsat 5 and Landsat 8 data. We used the object-oriented classification method with the maximum likelihood algorithm to classify six types of land uses. The series of land-use maps we produced had an overall accuracy of more than 80%. We then conducted a spatial analysis of the 5-year land-use change using ArcGIS software. In addition, we surveyed 150 farm households using a structured questionnaire regarding the impacts of climate change on agricultural productivity and land uses, as well as farmers’ adaptation and responses. The results showed that from 2005 to 2020, cropland decreased, while aquaculture land and forest land increased. We observed that the most remarkable decreases were in the area of rice (485.58 ha), the area of perennial crops (109.7 ha), and the area of non-agricultural land (747.35 ha). The area of land used for aquaculture and forest increased by 566.88 ha and 772.60 ha, respectively. We found that the manifestations of climate change, such as extreme weather events, saltwater intrusion, drought, and floods, have had a profound impact on agricultural production and land uses in the district, especially for annual crops and aquaculture. The results provide useful information for state authorities to design land-management strategies and solutions that are economic and effective in adapting to climate change.

scholarly journals Land Use Dynamics and Optimization from 2000 to 2020 in East Guangdong Province, China

2021 ◽  
Vol 13 (6) ◽  
pp. 3473
Author(s):  
Yong Lai ◽  
Guangqing Huang ◽  
Shengzhong Chen ◽  
Shaotao Lin ◽  
Wenjun Lin ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Landscape Connectivity ◽  
Land Use Changes ◽  
Research Result ◽  
Global Environmental Change ◽  
Ecological Processes ◽  
Buffer Zones ◽  
Conflict Zones ◽  
Land Use Dynamics

Anthropogenic land-use change is one of the main drivers of global environmental change. China has been on a fast track of land-use change since the Reform and Opening-up policy in 1978. In view of the situation, this study aims to optimize land use and provide a way to effectively coordinate the development and ecological protection in China. We took East Guangdong (EGD), an underdeveloped but populous region, as a case study. We used land-use changes indexes to demonstrate the land-use dynamics in EGD from 2000 to 2020, then identified the hot spots for fast-growing areas of built-up land and simulated land use in 2030 using the future land-use simulation (FLUS) model. The results indicated that the cropland and the built-up land changed in a large proportion during the study period. Then we established the ecological security pattern (ESP) according to the minimal cumulative resistance model (MCRM) based on the natural and socioeconomic factors. Corridors, buffer zones, and the key nodes were extracted by the MCRM to maintain landscape connectivity and key ecological processes of the study area. Moreover, the study showed the way to identify the conflict zones between future built-up land expansion with the corridors and buffer zones, which will be critical areas of consideration for future land-use management. Finally, some relevant policy recommendations are proposed based on the research result.

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.

scholarly journals Cultivated Land Use Layout Adjustment Based on Crop Planting Suitability: A Case Study of Typical Counties in Northeast China

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Ge Song ◽  
Hongmei Zhang
Keyword(s):  
Land Use ◽  
Northeast China ◽  
Utilization Efficiency ◽  
Food Crops ◽  
Cultivated Land ◽  
Gis Technology ◽  
Land Use Patterns ◽  
Production Area ◽  
Structural Imbalance ◽  
Crop Planting

Cultivated land use layout adjustment (CLULA) based on crop planting suitability is the refinement and deepening of land use transformation, which is of great significance for optimizing the allocation of cultivated land resources and ensuring food security. At present, people rarely consider the land suitability of crops when using cultivated land, resulting in an imbalance between crop distribution and resource conditions such as water, heat, and soil, and adversely affects the ecological security and utilization efficiency of cultivated land. To alleviate China’s grain planting structural imbalance and efficiency loss, this paper based on the planting suitability of main food crops (rice, soybean, and maize) to adjust and optimize the cultivated land use layout (CLUL) in the typical counties of the main grain production area in Northeast China, using the agent-based model for optimal land allocation (AgentLA) and GIS technology. Findings from the study show that: (1) The planting suitability of rice, soybean, and maize in the region is obviously different. Among them, the suitability level of soybean and maize is high, and that of rice is low. The current CLUL of the food crops needs to be further optimized and adjusted. (2) By optimizing the layout of rice, soybean, and maize, the planting suitability level of the food crops and the concentration level of the CLUL spatial pattern have been improved. (3) The plan for CLULA is formulated: The study area is divided into rice stable production area, maize-soybean rotation area, maize dominant area, and soybean dominant area, and town or village is identified as the implementation unit of CLULA. The plan for CLULA will be conducive to the concentrated farming of food crops according to the suitable natural conditions and management level. The research realized the optimization of spatial structure and cultivated land use patterns of different food crops integrating farming with protecting land. The significance of the study is that it provides a scientific basis and guidance for adjusting the regional planting structure and solving the problem of food structural imbalance.

scholarly journals Rural Roads Are Paving the Way for Land-Use Intensification in the Uplands of Laos

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 330
Author(s):  
Jean-Christophe Castella ◽  
Sonnasack Phaipasith
Keyword(s):  
Land Use ◽  
Management Practices ◽  
Management Strategies ◽  
Farming Systems ◽  
Long Term Effects ◽  
Group Discussions ◽  
Rural Roads ◽  
External Resources ◽  
Hybrid Maize ◽  
The Impact

Road expansion has played a prominent role in the agrarian transition that marked the integration of swidden-based farming systems into the market economy in Southeast Asia. Rural roads deeply altered the landscape and livelihood structures by allowing the penetration of boom crops such as hybrid maize in remote territories. In this article, we investigate the impact of rural road developments on livelihoods in northern Laos through a longitudinal study conducted over a period of 15 years in a forest frontier. We studied adaptive management strategies of local stakeholders through the combination of individual surveys, focus group discussions, participatory mapping and remote-sensing approaches. The study revealed the short-term benefits of the maize feeder roads on poverty alleviation and rural development, but also the negative long-term effects on agroecosystem health and agricultural productivity related to unsustainable land use. Lessons learnt about the mechanisms of agricultural intensification helped understanding the constraints faced by external interventions promoting sustainable land management practices. When negotiated by local communities for their own interest, roads may provide livelihood-enhancing opportunities through access to external resources, rather than undermining them.

scholarly journals The projected impacts of smart decline on urban runoff contamination levels

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Rui Zhu ◽  
Galen Newman
Keyword(s):  
Land Use ◽  
Green Infrastructure ◽  
Prediction Models ◽  
Stormwater Runoff ◽  
Baseline Scenario ◽  
Vacant Land ◽  
Smart Decline ◽  
Contamination Levels ◽  
Indirect Impacts

AbstractThere has been mounting interest about how the repurposing of vacant land (VL) through green infrastructure (the most common smart decline strategy) can reduce stormwater runoff and improve runoff quality, especially in legacy cities characterized by excessive industrial land uses and VL amounts. This research examines the long-term impacts of smart decline on both stormwater amounts and pollutants loads through integrating land use prediction models with green infrastructure performance models. Using the City of St. Louis, Missouri, USA as the study area, we simulate 2025 land use change using the Conversion of Land Use and its Effects (CLUE-S) and Markov Chain urban land use prediction models and assess these change’s probable impacts on urban contamination levels under different smart decline scenarios using the Long-Term Hydrologic Impact Assessment (L-THIA) performance model. The four different scenarios are: (1) a baseline scenario, (2) a 10% vacant land re-greening (VLRG) scenario, (3) a 20% VLRG scenario, and (4) a 30% VLRG scenario. The results of this study illustrate that smart decline VLRG strategies can have both direct and indirect impacts on urban stormwater runoff and their inherent contamination levels. Direct impacts on urban contamination include the reduction of stormwater runoff and non-point source (NPS) pollutants. In the 30% VLRG scenario, the annual runoff volume decreases by 11%, both physical, chemical, and bacterial pollutants are reduced by an average of 19%, compared to the baseline scenario. Indirect impacts include reduction of the possibility of illegal dumping on VL through mitigation and prevention of future vacancies.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

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