Yanfang Liu

A land-use spatial optimization model based on genetic optimization and game theory

Abstract Land-use patterns can be considered as a consequence of competitions between different land-use types. How to coordinate the competitions is the key to land-use spatial optimization. In order to improve the ability of existing land-use spatial optimization models for addressing local land-use competitions (the competitions on land units), a loosely coupled model based on a genetic algorithm (GA) and game theory is constructed. The GA is repeatedly executed to separately optimize the spatial layout of each land-use type. The land-use status quo is overlaid with the optimization results to find local land-use competitions. The concept of land-use competition zones is introduced in this study. Using the competition zones as the basic units, the model utilizes multi-stakeholder games and the knowledge of land-use planning to coordinate the local land-use competitions. The final solution is obtained after the land-use coordination. Gaoqiao Town, Zhejiang Province is selected as the study area to verify the validity of the model. The experimental results confirm that the model is feasible to undertake land-use spatial optimization and to coordinate the competitions between different land-use types.

Regional land-use allocation with a spatially explicit genetic algorithm

Land-use allocation is an important way to promote the intensive and economic use of land resources and achieve the goal of sustainable development. It is a complex spatial optimization problem, and heuristic algorithms have been one of the most effective ways to solve it in past studies. However, heuristic algorithms lack the guidance of planning knowledge, which makes land-use patterns usually unreasonable in practice. This research proposes a spatially explicit genetic algorithm (SEGA) that integrates land-use planning knowledge with the genetic algorithm (GA). The SEGA transforms the spatially implicit computation mode of the GA into a spatially explicit optimization style, which helps to promote the effectiveness of regional land-use allocation. Gaoqiao Town, China, was selected as the study area to test the SEGA. Results show that: (1) land-use conversions are reasonable in accordance with planning knowledge, and they improve overall land-use suitability and spatial compactness; (2) compared with the GA, the SEGA is superior in achieving global objectives and simulating local dynamics. We demonstrated that planning knowledge is essential to heuristic algorithms for land-use allocation.

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08). The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2.04), was similar to the use of the SM–based cluster method with known SM (RPD = 1.79–2.08). The predictive results (RPD = 1.87–2.06) demonstrated that the NSMI-–based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Visible and near-infrared (VIS–NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS–NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS–NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest

Heavy metal contamination of peri-urban agricultural soil is detrimental to soil environmental quality and human health. A rapid assessment of soil pollution status is fundamental for soil remediation. Heavy metals can be monitored by visible and near-infrared spectroscopy coupled with chemometric models. First and second derivatives are two commonly used spectral preprocessing methods for resolving overlapping peaks. However, these methods may lose the detailed spectral information of heavy metals. Here, we proposed a fractional-order derivative (FOD) algorithm for preprocessing reflectance spectra. A total of 170 soil samples were collected from a typical peri-urban agricultural area in Wuhan City, Hubei Province. The reflectance spectra and lead (Pb) and zinc (Zn) concentrations of the samples were obtained in the laboratory. Two calibration methods, namely, partial least square regression and random forest (RF), were used to establish the relation between the spectral data and the two heavy metals. In addition, we aimed to explore the use of spectral estimation mechanism to predict the Pb and Zn concentrations. Three model evaluation parameters, namely, coefficient of determination (R2), root mean squared error, and ratio of performance to inter-quartile range (RPIQ), were used. Overall, the spectral reflectance decreased with the increase in Pb and Zn contents. The FOD algorithm gradually removed spectral baseline drifts and overlapping peaks. However, the spectral strength slowly decreased with the increase in fractional order. High fractional-order spectra underwent more spectral noises than low fractional-order spectra. The optimal prediction accuracies were achieved by the 0.25- and 0.5-order reflectance RF models for Pb (validation R2u202f=u202f0.82, RPIQu202f=u202f2.49) and Zn (validation R2u202f=u202f0.83, RPIQu202f=u202f2.93), respectively. A spectral detection of Pb and Zn mainly relied on their covariation with soil organic matter, followed by Fe. In summary, our results provided theoretical bases for the rapid investigation of Pb and Zn pollution areas in peri-urban agricultural soils.

Effects of urban form on the urban heat island effect based on spatial regression model

The urban heat island (UHI) effect is becoming more of a concern with the accelerated process of urbanization. However, few studies have examined the effect of urban form on land surface temperature (LST) especially from an urban planning perspective. This paper used spatial regression model to investigate the effects of both land use composition and urban form on LST in Wuhan City, China, based on the regulatory planning management unit. Landsat ETM+ image data was used to estimate LST. Land use composition was calculated by impervious surface area proportion, vegetated area proportion, and water proportion, while urban form indicators included sky view factor (SVF), building density, and floor area ratio (FAR). We first tested for spatial autocorrelation of urban LST, which confirmed that a traditional regression method would be invalid. A spatial error model (SEM) was chosen because its parameters were better than a spatial lag model (SLM). The results showed that urban form metrics should be the focus for mitigation efforts of UHI effects. In addition, analysis of the relationship between urban form and UHI effect based on the regulatory planning management unit was helpful for promoting corresponding UHI effect mitigation rules in practice. Finally, the spatial regression model was recommended to be an appropriate method for dealing with problems related to the urban thermal environment. Results suggested that the impact of urbanization on the UHI effect can be mitigated not only by balancing various land use types, but also by optimizing urban form, which is even more effective. This research expands the scientific understanding of effects of urban form on UHI by explicitly analyzing indicators closely related to urban detailed planning at the level of regulatory planning management unit. In addition, it may provide important insights and effective regulation measures for urban planners to mitigate future UHI effects.

Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine

Accurate estimation of soil organic matter (SOM) is essential in understanding the spatial distribution of SOM to identify areas that need fertilization and the required grade of those fertilizers. Visible and near-infrared spectroscopy is a promising alternative to time consuming and costly conventional soil assessment methods. However, this approach is highly dependent on selecting suitable preprocessing strategies and data mining techniques for regression analysis. In this study, 2D correlation coefficients, including ratio, difference, and normalized difference indices, were introduced to select sensitive spectral parameters. The performance of extreme learning machine (ELM) was evaluated via comparison with that of support vector machine (SVM) for SOM estimation. A total of 257 soil samples were collected from Hubei Province, Central China, with SOM contents and reflectance spectra measured in the laboratory. Five spectral pretreatments, except for the raw spectra, were applied. SVM and ELM models were calibrated on spectral parameters selected by one-dimensional and 2D correlation coefficients and subsequently applied to predict SOM. Results showed that 2D correlation coefficient can effectively highlight the detailed SOM information compared with that of one-dimensional correlation coefficient. The ELM models yielded superior predictability relative to SVM models in all eight established models. The most excellent estimation accuracy was obtained by 2D ratio index and ELM (TRI-ELM) method, with an independent validation R2 and a ratio of performance to interquartile range of 0.83 and 3.49, respectively. The SOM fertility levels of predicted SOM showed that TRI-ELM method presented the largest similarity to laboratory-measured SOM levels, and misclassified samples were all concentrated within one error level. In summary, our study indicates that the TRI-ELM model is a rapid, inexpensive, and relatively accurate method for identifying SOM fertility level.

On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China

A clear understanding of the relationship between ecosystem services (ESs) and urbanization provides new insight into urban landscape planning and decision making. Although a considerable amount of literature has focused on this topic, few studies address the spatial interactions between ESs and urbanization, especially at the local scale. Various models and multisource data were integrated to estimate ESs and urbanization in Wuhan City, China. The bivariate Morans I methods were employed to test and visualize the spatial correlations between ESs and urbanization. Spatial regression models were used to describe the spatial dependence of ESs on urbanization. Our results showed that all ESs have globally negative spatial correlations with urbanization, but focusing on local scale allowed spatial correlations to be categorized into four types: high ESs and high urbanization, high ESs and low urbanization, low ESs and high urbanization, and low ESs and low urbanization. Spatial regression models were identified as more suitable to measure the spatial dependence of ESs on urbanization, as they account for the effects of spatial autocorrelation. Among ESs, biodiversity conservation was the one most sensitive to increased urbanization, followed by outdoor recreation, water yield, grain productivity, carbon storage, and erosion prevention. The spatial exploration of the relationship between ESs and urbanization provides practical guidance for urban development planning and environmental protection.

Network and Geography: Dependence and Disparity Between Human Settlement Pattern and Socioeconomic Network in Chengui, China

AbstractThe spatial distribution of settlements in a heterogeneous landscape matrix influences the capacity of people to form and maintain relationships. Although such a perspective has gained seve...

Spatio-temporal variation of agricultural land consolidation in China: case study of Huangshi, Hubei Province

ABSTRACT Land fragmentation and soil degradation are major barriers to agricultural production. Agricultural land consolidation (ALC) can effectively address these problems. A regional case study in Central China was used to analyze the spatial characteristics of the implemented ALC projects (ALCPs) from 2003 to 2010, and the planned ALCPs from 2011 to 2020. ALCPs were classified into basic farmland construction projects (BFCPs) and low-hilly land consolidation projects (LHLCPs). The spatial distribution of BFCPs and LHLCPs was presented on maps with a 1:10,000 scale. A comparative analysis showed that the landscape indices of the project areas varied significantly in different phases. The implemented BFCPs are more centralized in space than the implemented LHLCPs. The proportionality (p) was proposed to evaluate the rationality of ALC planning at the town level. Results showed an apparent imbalance of p values among different towns. Shape regularity and centrality are important criteria for selecting ALCPs at spatio-temporal level. The maps provide a patch-based overview of the distribution and aggregation of ALCPs from 2003 to 2020. The findings have implications on assessing rationality and time scheduling of ALCPs.