华北平原禹城市耕地变化与驱动力分析(英文)

Taking Yucheng, a typical agricultural county in Shandong Province as a case, this study applied Logistic regression models to spatially identify factors affecting farmland changes. Using two phases of high resolution imageries in 2001 and 2009, the study obtained the land use and farmland change data in 2001-2009. It was found that the farmland was reduced by 5.14% in the period, mainly due to the farmland conversion to forest land and built-up land, although part of forest land and unused land was converted to farmland. The results of Logistic regressions indicated that location, population growth and farmer income were main factors affecting the farmland conversion, while soil types and pro-curvature were main natural factors controlling the distribution of farmland changes. Regional differences and temporal-spatial variables of farmland changes affected fitting capability of the Logistic re-gression models. The ROC fitting test indicated that the Logistic regression models gave a good explanation of the regional land-use changes. Logistic regression analysis is a good tool to identify major factors affecting land use change by quantifying the contribution of each factor.     

基于logistic回归模型的农牧交错区土地利用变化驱动力分析——以内蒙古翁牛特旗为例

本文以农牧交错带的典型区域——内蒙古翁牛特旗为例,考虑土地利用变化过程的空间变量,建立了不同土地利用变化过程的logistic回归模型。结果表明:模型中转为耕地的主要解释变量是到农村居民点的距离和农业气候区;转为草地的主要解释变量是到农村居民点的距离、土壤表层有机质含量和到乡镇中心的距离;转为林地的主要解释变量是到农村居民点的距离和海拔;空间异质性和土地利用变化过程的时间变量共同影响着使用logistic回归模型来解释土地利用变化驱动力的能力;通过对草地logistic回归模型的检验,得出空间统计模型能较好地揭示不同土地利用变化过程的主要驱动力及其作用机理。     

Comparative performance of logistic regression and survival analysis for detecting spatial predictors of land-use change

Although survival analysis is known to outperform logistic regression, theoretically and according to evidence from other disciplines, little is known about how true this is in situations where the goal is detecting spatial predictors of land change. Furthermore, with the increasing availability of longitudinal land-change data, evidence is needed on the relative performance of these two different methods in situations with differing levels of data abundance. To fill this gap, we generated a pseudo land-change data set using an agent-based model of residential development in a virtual landscape. This agent-based model simulated the decisions of homebuyers in choosing residential locations based on the values of several spatial variables. Pseudo land-change maps, generated by the agent-based model with different weights on these spatial variables, were exposed to statistical analysis under the logistic and survival approaches. We evaluated how well the two approaches could reveal the spatial variables that were used in the agent-based model and compared the performance of the two methods when land-change data were collected under different sampling frequencies. Our results suggest that survival analysis outperforms logistic regression in detecting the variables that were included in agent decisions, largely because it takes into account time-dependent variables. Also, this research suggests that various properties of land-change processes (like amount of developed area and access of agents to information) affect the relative performance of these statistical approaches aimed at uncovering land-change predictor variables.     

土地利用变化的多尺度空间自相关分析——以内蒙古翁牛特旗为例

用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。     

GWR模型在土壤重金属高光谱预测中的应用

目前土壤重金属高光谱反演模型大多忽视了重金属与光谱变量间相关关系的空间异质性,这与实际情况不相吻合,而地理权重回归（GWR）模型能有效地揭示变量间关系的空间异质性。本文以福州市土壤重金属Cd、Cu、Pb、Cr、Zn、Ni为对象,构建土壤重金属预测的GWR高光谱模型,并将预测结果与普通最小二乘法回归（OLS）结果进行比较分析,探讨GWR模型在土壤重金属高光谱预测中的适用性及局限性。结果表明：① GWR模型在土壤重金属高光谱预测中适用与否取决于重金属对光谱变量影响的空间异质性程度：对于Cr、Cu、Zn、Pb等对光谱变量影响空间异质性大的元素,其GWR预测精度较OLS提高明显,表现为GWR模型的调节R²较OLS模型有了明显提高,分别为OLS模型的2.69倍、2.01倍、1.87倍和1.53倍;而AIC值以及残差平方和较OLS模型却明显降低,AIC值减少量均大于3个单位,残差平方和则仅分别为OLS模型的25.33%、30.09%、47.22%和86.84%;对于Cd和Ni等对光谱变量影响空间异质性小的元素,相较于OLS模型,GWR模型的调节R²分别提高了0.015和0.007,残差平方和分别减少了5.97%和4.18%,但AIC值却分别增加了2.737和2.762,GWR预测效果改善不明显;② 光谱变换可以有效增强土壤重金属的光谱特征,其中以光谱的倒数变换效果最好,而且该变换及其微分形式可以很好地提高模型的预测效果;③ GWR模型的应用前提是变量间关系的空间非平稳性,适合在与土壤光谱变量间关系具有显著空间异质性的重金属高光谱预测中推广。     

基于支撑向量回归的二端元混合像元分解

针对遥感影像混合像元光谱复杂,其非线性特征,传统LSMM分解模型难以进行有效的混合像元分解的不足。通过基于SVR的二端元混合像元分解的研究,从真实遥感影像上获取典型的植被、非植被光谱信息,构造二端元混合光谱库,进行SVR模型的混合像元分解。当样本量为6%时,交叉验证获得最佳模型参数（C=1024.0和g=4.0）,进一步对全部混合像元进行混合像元分解。实验结果表明：SVR分解结果RMSE为5.95,R²为0.958,优于LSMM方法（RMSE=7.71,R²=0.932）,且在各个不同真值丰度下具有更好的稳定性,证明该方法对于非线性混合光谱具有很好的学习和推广能力。此外,该方法的精度不随训练样本量的增加呈明显变化,体现出SVR在有限样本情况下能够保证高效率的训练能力。     

MLR和PLSR的沙壤土盐分含量光谱检测对比研究

为了快速有效检测南疆地区典型土壤（沙壤土）的盐分含量变化,利用光谱仪和电导仪测得南疆阿拉尔市红枣种植区盐渍土近红外高光谱和电导率数据,基于7种不同光谱预处理方法和2种特征波长选择算法,分别建立多元线性回归（MLR）和偏最小二乘回归（PLSR）的土壤盐分监测模型。结果表明：7种预处理方法中,归一化,多元散射,变量标准化和一阶导数能够有效提高土壤盐分的预测模型精度。基于多元逐步回归（SMR）波长选择方法的多元线性回归（SMLR）模型的R_val²>0.948 9,RPD>6.294 9,RMSEP<0.435 6;基于连续投影算法（SPA）的多元线性回归（SPA-MLR）模型的R_val²>0.956 8,RPD>6.922 1,RMSEP<0.361 6,预测结果要优于偏最小二乘回归（PLSR）模型,其中基于归一化处理后的SMLR和SPA-MLR的预测精度最为理想,分别为R_val²=0.979 2,RPD=9.907 8,RMSEP=0.287 6和R_val²=0.980 5,RPD=10.50,RMSEP=0.278 3,而且筛选的特征波长较少。说明归一化是更有效的光谱预处理方法,多元线性回归（MLR）更适合建立南疆典型沙壤土盐分含量的预测模型。     

Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain

Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km²) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.     

AutoLogistic 方法在土地利用格局模拟中的应用 —— 以张家界市永定区为例

土地利用/ 土地覆被变化(LUCC) 是当前研究全球变化的重要内容, 而区域土地利用 格局模拟是LUCC 研究的核心内容之一。以张家界市永定区为研究单元, 根据由2005 年土地 利用现状图和数字高程模型数据源得到的土地利用、地形、河流以及道路等空间数据, 对区 域土地利用类型空间格局的空间自相关性特征进行了建模研究, 并通过在传统Logistic 模型 中引入描述空间自相关性的成份, 实现了能够考虑自相关性因素的回归分析模型 (AutoLogistic 模型), 同时应用该模型对区域土地利用格局进行了模拟和分析。结果显示, 通 过与没有考虑空间自相关性的回归模型(传统Logistic 模型) 相比较, 该模型显示了更好的拟 合优度和更高的拟合准确率(耕地、林地、建设用地及未利用地的ROC 值分别从0.851、 0.913、0.877 和0.852 提高到0.893、0.940、0.907 和0.863)。研究结果说明了基于 AutoLogistic 方法的土地利用格局的相关性建模在一定意义上是合理的。同时研究结果也可以 为永定区及其相似地区的土地利用规划决策提供更为科学的依据。     

基于CLUE_S模型的矿区土地利用变化情景模拟——以徐州市贾汪矿区为例

以徐州市贾汪矿区1986、1996、2006和2016年4期遥感影像为数据源,基于CLUE-S模型,在传统Logistics回归模型的基础上引入空间自相关因子形成Autologistic回归模型,选取政策、自然环境、社会经济和空间约束等因素,对贾汪矿区2016年土地利用空间分布格局进行模拟以检验精度。在此基础上对研究区2026年趋势发展、经济发展和生态保护3种情景下的土地利用空间分布格局进行了模拟。结果表明：1）Autologistic回归模型在土地利用情景模拟过程中能够更好地反映真实的土地利用格局;2）研究区2016－2026年不同情景下,建设用地在3种情景下均呈现明显的增加趋势,未利用地面积持续减少,其中经济发展情景下建设用的增幅最大,生态保护用地情景下建设用地增幅最小,在生态保护情景下,林地、耕地等生态用地受到保护,建设用地的扩展速度被抑制。 关键词：土地利用变化;CLUE-S模型;Autologistic回归模型;情景模拟;贾汪矿区     

京津冀地区1985–2000期间林地变化的时空格局及其影响因素（英文）

林地是维护生态安全,实现区域可持续发展的根本基础资源。林地变化可能导致一些生态环境问题,包括土壤侵蚀,水资源短缺,干旱加剧以及生物多样性的丧失。本文以景观生态学和逻辑回归模型为基础,探讨了京津冀地区1985-2000期间林地变化的时空格局及其影响因素。格局分析结果表明,林地景观破碎化正在下降和林地形状变得越来越规则。通过建立Logistic回归模型,这项研究旨在探讨这一区域1985-2000期间林地变化的重要变量。对于京津冀地区1985-2000期间林地变化而言,土壤有机质含量,坡度(5°),到最近村庄的距离以及人均国内生产总值是最重要的解释变量。研究表明,空间异质性会影响到林地变化的逻辑回归模型的可预测性。     

GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study

This study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.     

Comparison of spatial and aspatial logistic regression models for landmine risk mapping

Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km² municipality in Bosnia and Herzegovina (BiH) and a large 4500 km² region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches.     

基于CA-ABM模型的福州城市用地扩张研究

以中国海西地区重要门户福州市为研究区,结合其地理位置多层次约束性条件,以地理加权回归模型作为元胞自动机(CA)层的转换规则,同时以2000-2015年多期LandsatTM/ETM+影像的城市用地情况为参照,借助GIS空间分析技术,对CA和多智能体(ABM)相耦合的城市用地扩张模型进行改进。然后利用传统的和改进后的CA-ABM模型,多角度、多层次地模拟福州市2000年、2005年、2010年、2015年城市用地扩张在微观格局上的变化。结果表明,传统的和改进后的CA-ABM模型的整体精度均在80%以上,模拟结果具有较强的可信度;改进的 CA-ABM模型模拟的点对点总体精度和Kappa系数均高于传统的CA-ABM模型,而且模拟结果更加接近实际的城市用地扩张分布情况。结论可为平衡城市化进程和合理规划城市用地提供重要的理论技术支撑。     

黄土丘陵小流域土壤水分空间预测的统计模型

在6个土层和10次土壤含水量测定的基础上,利用土地利用与地形等6类20个环境因子变量,建立了黄土丘陵区小流域土壤水分空间预测的6种多元线性回归模型,并提出了5类13个指标对模型进行了评价与比较。研究表明,各模型组之间的差异较大,以直接回归模型组为最优,PCA线性转换回归模型组次之,DCA非线性转换回归模型组最差。在每一组内,模型之间的差异相对较小,以变量全部入选模型稍优于变量逐步筛选模型。6种模型中,通用多元线性回归模型的拟合性最好、预测精度最高,但模型结构最为复杂、需要的环境因子最多;多元线性逐步回归模型不仅拟合性和无偏性方面很好,而且结构最为简单、需要的环境变量最少,因而为最优模型     

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data

Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set.     

都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比

以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。     

LUCC驱动力模型研究综述

驱动力研究是土地利用变化研究中的核心问题。土地利用变化驱动力模型是分析土地利用变化原因和结果的有力工具，模型通过情景分析可为土地利用规划与决策提供依据。基于不同理论的驱动力研究方法很多，论文选取了几种国内外应用较多的LUCC驱动力模型进行综述，分析了每个模型的优缺点及适用范围，最后得出结论：1) 基于过程的动态模型更适于研究复杂的土地利用系统。2) 基于经验的统计模型能弥补基于过程的动态模型的不足。3) 基于不同学科背景的模型进一步集成将是LUCC驱动力模型未来的发展趋势。     

Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico

Understanding and analysis of drivers of land-use and -cover change (LUCC) is a requisite to reduce and manage impacts and consequences of LUCC. The aim of the present study is to analyze drivers of LUCC in Southern Mexico and to see how these are used by different conceptual and methodological approaches for generating transition potential maps and how this influences the effectiveness to produce reliable LUCC models. Spatial factors were tested for their relation to main LUCC processes, and their importance as drivers for the periods 1993–2002 and 2002–2007 was evaluated by hierarchical partitioning analysis and logistic regression models. Tested variables included environmental and biophysical variables, location measures of infrastructure and of existing land use, fragmentation, and demographic and social variables. The most important factors show a marked persistence over time: deforestation is mainly driven by the distance of existing land uses; degradation and regeneration by the distance of existing disturbed forests. Nevertheless, the overall number of important factors decreases slightly for the second period. These drivers were used to produce transition potential maps calibrated with the 1993–2002 data by two different approaches: (1) weights of evidence (WoE) to represent the probabilities of dominant change processes, namely deforestation, forest degradation, and forest regeneration for temperate and tropical forests and (2) logistic RM that show the suitability regarding the different land-use and -cover (LUC) classes. Validation of the transition potential maps with the 2002–2007 data indicates a low precision with large differences between LUCC processes and methods. Areas of change evaluated by difference in potential showed that WoE produce transition potential maps that are more accurate for predicting LUCC than those produced with RM. Relative operating characteristic (ROC) statistics show that transition potential models based on RM do usually better predict areas of no change, but the difference is rather small. The poor performance of maps based on RM could be attributed to their too general representation of suitability for certain LUC classes when the goal is modeling complex LUCC and the LUC classes participate in several transitions. The application of a multimodel approach enables to better understand the relations of drivers to LUCC and the evaluation of model calibration based on spatial explanatory factors. This improved understanding of the capacity of LUCC models to produce accurate predictions is important for making better informed policy assessments and management recommendations to reduce deforestation.     

Probabilistic Prediction Models for Aggregate Quarry Siting

Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features.