Optimatization of sample points for monitoring arable land quality by simulated annealing while considering spatial variations

This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security. In China, the reduction and degradation of arable land due to industrialization and urbanization has gradually emerged as one of the most prominen challenges. In this context, the long-term dynamic monitoring of arable land quality becomes important for protecting arable land resources. However, little consideration has been given to optimizing sample points number and layout in previous monitoring studies on arable land quality. When considering the optimization of sample points, various strategies are needed, depending on the indicators. In addition, the distributio of soil properties displays spatial variations. However, existing sampling studies have paid little attention to spatial variations during scenarios with multiple indicators.Therefore, it is necessary to further investigate how to improve the efficiency and accuracy of arable land quality monitoring and evaluation by optimizing the number and layout of sample points when there are spatial variations in multiple indicators.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies

Operational Pattern of Urban-Rural Integration Regulated by Land Use in Metropolitan Fringe of China

Due to a lack of the realization mechanism and operational pattern of the urban-rural integration by land use, this study employs land use to regulate interface elements to achieve urban-rural integration development. Therefore, we analyzed urban-rural reality in Pukou District of Nanjing City, a typical metropolitan fringe of China, and investigated farmers’ willingness of typical representative villages. The results show that (1) According to the combination of resource environment, development intensity and development potential, Pukou District is divided into four land use areas, including optimization integration area, key development area, urban agricultural area, and ecotourism area. Most of the investigated farmers have a strong willingness to realize urban-rural integration by land use; (2) This study proposes an operational pattern of regional land use. The pattern is mainly based on “reality + willingness + policy” by using the three tools of “farmland reconsolidation, village reconstruction and factor reallocation”. It achieves urban-rural integration development through “zoning guidance–willingness driven–pattern selection–differentiated tools”

Density and stability of soil organic carbon beneath impervious surfaces in urban areas.

Installation of impervious surfaces in urban areas has attracted increasing attention due to its potential hazard to urban ecosystems. Urban soils are suggested to have robust carbon (C) sequestration capacity; however, the C stocks and dynamics in the soils covered by impervious surfaces that dominate urban areas are still not well characterized. We compared soil organic C (SOC) densities and their stabilities under impervious surface, determined by a 28-d incubation experiment, with those in open areas in Yixing City, China. The SOC density (0-20 cm) under impervious surfaces was, on average, 68% lower than that in open areas. Furthermore, there was a significantly (P<0.05) positive correlation between the densities of SOC and total nitrogen (N) in the open soils, whereas the correlation was not apparent for the impervious-covered soils, suggesting that the artificial soil sealing in urban areas decoupled the cycle of C and N. Cumulative CO2-C evolved during the 28-d incubation was lower from the impervious-covered soils than from the open soils, and agreed well with a first-order decay model (Ct = C1+C0(1-e-kt)). The model results indicated that the SOC underlying capped surfaces had weaker decomposability and lower turnover rate. Our results confirm the unique character of urban SOC, especially that beneath impervious surface, and suggest that scientific and management views on regional SOC assessment may need to consider the role of urban carbon stocks

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research

Genome-wide identification and characterization of GRAS transcription factors in sacred lotus (Nelumbo nucifera)

The GRAS gene family is one of the most important plant-specific gene families, which encodes transcriptional regulators and plays an essential role in plant development and physiological processes. The GRAS gene family has been well characterized in many higher plants such as Arabidopsis, rice, Chinese cabbage, tomato and tobacco. In this study, we identified 38 GRAS genes in sacred lotus (Nelumbo nucifera), analyzed their physical and chemical characteristics and performed phylogenetic analysis using the GRAS genes from eight representative plant species to show the evolution of GRAS genes in Planta. In addition, the gene structures and motifs of the sacred lotus GRAS proteins were characterized in detail. Comparative analysis identified 42 orthologous and 9 co-orthologous gene pairs between sacred lotus and Arabidopsis, and 35 orthologous and 22 co-orthologous gene pairs between sacred lotus and rice. Based on publically available RNA-seq data generated from leaf, petiole, rhizome and root, we found that most of the sacred lotus GRAS genes exhibited a tissue-specific expression pattern. Eight of the ten PAT1-clade GRAS genes, particularly NnuGRAS-05, NnuGRAS-10 and NnuGRAS-25, were preferentially expressed in rhizome and root. In summary, this is the first in silico analysis of the GRAS gene family in sacred lotus, which will provide valuable information for further molecular and biological analyses of this important gene family

Soil heavy metal contamination in rural land consolidation areas in the Yangtze River Delta, China

Due to the rapid urbanization of the Yangtze River Delta in China, large numbers of formerly rural residents have migrated to the cities. To adjust the structure of rural land use, the government has performed extensive land consolidation. Previous studies indicated that the land consolidation has affected farmland quality to some extent. However, the effect of the land consolidation on farmland heavy metal concentrations has rarely been reported. In this study, the Jintan District was used as an example, and 40 sampling sites of various consolidation types in 4 representative areas of rural land consolidation were selected. Soil samples were collected from these sites, and the heavy metal concentrations were analyzed. We used multivariate methods of correlation analysis and principal component analysis to study the conditions and sources of the heavy metal contamination in the soil. The results indicate that the mean concentrations of Cd, Hg, Ni, Cu, and Zn in the soil all exceeded the background values. The mean concentration of Cd was 0.409 mg/kg, and the enrichment factor (EF) was 4.54, making Cd the most prevalent heavy metal soil contaminant in the study area. The enrichment of soil heavy metals varied among the various representative areas. Suburban areas surrounding the central cities were mainly enriched in Hg, with an EF of 6.20. The comprehensive development zone displayed enrichment in Cd, with an EF of 7.79. The heavy metal concentrations in the soil also differed depending on the type of land consolidation. The reclaimed soil of rural settlements contained high levels of Cd and Zn, with EFs of 7.25 and 2.52, respectively, which were related to the land use before the land consolidation. The soil heavy metals of the study area were affected by both human activity and natural background contamination

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution

Comprehensive Land Consolidation as a Tool to Promote Rural Restructuring in China: Theoretical Framework and Case Study

In the context of current global rural decline, land consolidation has been adopted with the objectives of promoting rural vitalization and regional sustainable development. In this paper, we provide a theoretical framework for rural restructuring driven by comprehensive land consolidation (CLC). The framework describes three key mechanisms of rural spatial, economic, and social restructuring driven by CLC: improving spatial patterns and functions, vitalizing the collective economy, and reshaping the social community. Based on the theoretical framework, we present a case that exemplifies the micro processes of rural restructuring. Taking spatial restructuring as the material basis and carrier, CLC promotes economic restructuring from traditional agricultural production to modern agricultural production and industrial integration, as well as social restructuring from a traditional rural society to urbanization, communitization, and a society with diversified culture. After CLC, it is very important to further enhance the sustainability of the collective economic development and enhance the cohesion and prosperity of the social community

Site Selection of Affordable Housing in Direct Management Area under Jiangbei&rsquo;s New District in Nanjing

Affordable housing is an effective approach for relieving the residential stress of moderate and low-income citizens; it is not only a macro study but also a micro one in the processive phase focusing on site selection and so on. From this view, the site selection of affordable housing in the direct management district under Jiangbei New District in Nanjing is analyzed, including summarizing the status quo and construction models of affordable housing&rsquo;s site selection; quantifying the influential elements of site selection by the AHP method and GIS spatial analysis. The final result shows that the site of Jiangbei&rsquo;s new district in Nanjing, with a score ranging from 2.7792 to 3.8572, is recommended for affordable housing. According to this result, the optimization strategy of affordable housing planning and location is put forward to ensure the balance of work and housing and the interests of low-income groups

Site Selection of Affordable Housing in Direct Management Area under Jiangbei’s New District in Nanjing

Affordable housing is an effective approach for relieving the residential stress of moderate and low-income citizens; it is not only a macro study but also a micro one in the processive phase focusing on site selection and so on. From this view, the site selection of affordable housing in the direct management district under Jiangbei New District in Nanjing is analyzed, including summarizing the status quo and construction models of affordable housing’s site selection; quantifying the influential elements of site selection by the AHP method and GIS spatial analysis. The final result shows that the site of Jiangbei’s new district in Nanjing, with a score ranging from 2.7792 to 3.8572, is recommended for affordable housing. According to this result, the optimization strategy of affordable housing planning and location is put forward to ensure the balance of work and housing and the interests of low-income groups