Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km² study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.     

土壤制图中土壤类型配色模型构建与应用

在全国1∶5万土壤图集制图中,土壤类型的配色既需表现土类等高级类型的分布特征,也要表现土属等较低级类型的差别。我国土壤低级类型众多,且1∶5万基本比例尺图幅达2万余幅,采用传统人工设色方法进行土壤制图,不仅效率低,而且难以保持图幅间土壤颜色的协调一致性。针对这一技术难题,本研究采用图幅间相似配色方法和人机交互的设计思想,通过建立1个多层级管理色库、人工设置土壤类型的Q配色单元及其多个近似色系(色组),建立了Q配色单元的避让选色和区域土壤特征分析等5个组件模型,构建了土壤类型配色模型(SCO-Model)。该模型在大比例尺土壤制图中不仅反映了区域土壤的总体分布特征,也表达了土壤类型间的差异,特别是实现了大比例尺土壤制图中土壤类型的快速智能配色,大大提高了制图效率。     

机器学习用于耕地土壤有机碳空间预测对比研究——以亚热带复杂地貌区为例

耕地土壤有机碳(SOC)是土壤质量的重要指标,也是生态系统健康的重要表征。当前机器学习(Machine Learning, ML)用于SOC数字制图日益热门,但不同算法在高空间分辨率SOC数字制图中的对比研究尚有欠缺。本研究以福建省东北部复杂地形地貌区为例,采用10m空间分辨率Sentinel-2影像数据,选取地形、气候、遥感植被变量为驱动因子,重点分析当前常用的机器学习算法——支持向量机(SupportVector Machine,SVM)、随机森林(RandomForest,RF)在SOC预测中的差异,并与传统普通克里格模型(Ordinary Kriging, OK)进行比较。结果表明:基于地形、遥感植被因子和气候因子构建的RF模型表现最佳(RMSE=2.004,r=0.897),其精度优于OK模型(RMSE=4.571, r=0.623),而SVM模型预测精度相对最低(RMSE=5.190, r=0.431);3种模型预测SOC空间分布趋势总体相似,表现为西高东低、北高南低,其中RF模型呈现的空间分异信息更加精细;最优模型反演得到耕地土壤有机碳平均含量为15.33 g·kg-1; RF模型和SVM模型变量重要性程度表明:高程和降水是影响复杂地貌区SOC空间分布的重要变量,而遥感植被因子重要性程度低于高程。     

应用土壤-景观定量模型预测土壤属性空间分布及制图

以土壤-景观定量模型为基础的土壤制图方法在世界范围内得到了广泛研究。本文在皖南宣城的丘陵地带内选择研究区,从该区的数字高程模型(DEM)中获取景观信息:地形因子,定量地分析了土壤属性与地形因子之间的相关关系,并建立基于该关系的线性土壤-景观定量模型,最后应用该模型来预测土壤属性在空间上的分布并制图。结果表明:土体厚度和表层有机质含量与地形因子之间有着显著相关性;建立的线性回归模型分别能解释土体厚度、表层有机质含量空间变异的32.2%和35.3%;依据该模型预测的土体厚度和表层有机质含量具有较高的准确度,并能制图表达土壤属性在空间上的自然连续性。     

A multiscale soil–landform relationship in the glacial‐drift area based on digital terrain analysis and soil attributes

Understanding the linkages between structure and processes in soil landscapes involves analyses across several spatial and temporal scales. The transfer of information between scales requires the (1) identification of respective scale levels and (2) procedures for regionalization. Here, we present a multiparameter delineation of landform units and their attribution with typical Reference Soil Groups (RSG) of a landscape of NE Germany which is representative of young moraine regions. Data sources are a digital elevation model (DEM, 5 m × 5 m), a reference data set from sections of an intensively augered landscape, and expert knowledge. A conceptual digital soil map was constructed in the scale 1:5000 based on the Topographic Position Index (TPI). The methodology is applicable for multiscale analyses. Results are (1) the landform unit classified by digital terrain analysis of a DEM, (2) the attribution of RSG, and (3) the evaluation of the classification. Accuracy of the method was 57% overall, with 70% accuracy on typical erosional sites. The developed method allows identification of terrain‐related soil pattern with high spatial resolution in glacial‐drift areas. The high resolution of soil information can be used for delineation of management zones in precision farming, or as input for process studies and models requiring a translation of typological soil information into relevant soil properties (e.g., by pedotransfer functions).     

Assessing and mapping topsoil organic carbon stock at regional scale: A scorpan kriging approach conditional on soil map delineations and land use

In order to assess the potential of soils as C reservoir at regional scale, accurate estimates of soil organic carbon (SOC) are required, and different approaches can be used. This study presents a method to assess and map topsoil organic carbon stock (Mg ha⁻¹) at regional scale for the whole Emilia Romagna plain in Northern Italy (about 12 000 km²). A Scorpan Kriging approach is proposed, which combines the trend component of soil properties as derived from the 1:50 000 soil map with geostatistical modeling of the stochastic, locally varying but spatially correlated component. The trend component is described in terms of varying local means, calculated taking into account soil type and dominant land use. The resulting values of SOC, sand, silt, and clay contents are retained for calculating topsoil SOC stocks, using a set of locally calibrated pedotransfer functions (PTFs) to estimate bulk density. The maps of each soil attribute are validated over a subset of 2000 independent and randomly selected observations. As compared to the standard approach based on the mean values for delineation, results show lower standard errors for all the variables used for SOC stock assessment, with a relative improvement (RI) ranging from 4 per cent for SOC per cent to 24 per cent for silt. The total C stock (0–30 cm) in the study area is assessed as 73·24 ± 6·67 M t, with an average stock of 62·30 ± 5·55 Mg ha⁻¹. The SOC stock estimates are used to infer possible SOC stock changes in terms of carbon sequestration potential and potential carbon loss (PCL). Copyright © 2010 John Wiley & Sons, Ltd.     

Effects of land management practices and land cover types on soil loss and crop productivity in Ethiopia: A review

Identifying land management practices (LMPs) that enhance on-site sediment management and crop productivity is crucial for the prevention, reduction, and restoration of land degradation and contributing to achieving land degradation neutrality (LDN). We reviewed studies in Ethiopia to assess the effects of LMPs on soil loss (84 studies) and crop productivity (34 studies) relative to control practice. Yield variability on conserved lands was assessed using 12,796 fixed plot data. Effects of LMP on soil loss were 0.5–55 t ha⁻¹y⁻¹ compared to control practices yielding 50 to 140 t ha⁻¹y⁻¹. More than 55% of soil loss records revealed soil loss less than the tolerable rate (10 t ha⁻¹). Area closure, perennial vegetation cover, agronomic practices, mechanical erosion control practices, annual cropland cover, and drainage groups of practices led to 74.0 ± 18.3%, 69.0 ± 24.6%, 66.2 ± 30.5%, 66.1 ± 18.0%, 63.5 ± 20.0%, and 40 ± 11,1% soil loss reduction, respectively. A yield increase of 25.2 ± 15.0%, 37.5 ± 28.0%, and 75.4 ± 85.0% was found from drainage, agronomy, and mechanical erosion control practices, respectively. The average yield loss by erosion on fields without appropriate land management practice and on conserved fields was 26.5 ± 26.0% and 25 ± 3.7%, respectively. The findings suggest that practices that entail a continuous presence of soil cover during the rainy season, perennial vegetation, retention of moisture, and barriers for sediment transport were most effective at decreasing soil loss and increasing productivity. This review provides evidence to identify the best LMP practices for wider adoption and inform decision-making on LMP investments towards achieving sustainable solutions to reverse land degradation.     

基于土地利用结构特征的土壤侵蚀强度预测研究——以四川省为例

研究计算四川省土地利用结构特征指数和土壤侵蚀强度指数结果表明,该省土地利用结构特征指数与土壤侵蚀强度指数的变化具有同步性,用土地利用结构特征指数预测其土壤侵蚀强度变化是合理可行的。     

基于样点个体代表性的大尺度土壤属性制图方法

大空间尺度范围的土壤属性分布信息是陆地表层过程模拟的基础信息.基于野外样点进行空间插值是获得土壤属性空间分布信息的重要手段.现有的空间插值方法通常要求所用样点对研究区土壤属性空间分布规律具有良好的全局代表性.然而,受采样经费和野外采样条件的限制,所采集的样点往往难以全面地反映研究区土壤属性的空间分布规律.基于这样的样点用现有空间插值方法得到的土壤属性分布图通常精度较低,并且由样点全局代表性差带来的推测不确定性也无法得到度量.为了合理利用这些已采集的但全局代表性不好的样点,本文提出了基于样点“个体代表性”推测土壤属性空间分布并度量推测不确定性的方法.该方法在两点环境条件越相似、土壤属性就越相似的假设下,认为每一样点可以代表与其环境条件相似的地区,并且代表程度可以由两点的环境相似度度量;通过分析环境相似度计算推测不确定性,并以环境相似度为权重计算样点可代表地区的土壤属性值.将该方法应用于推测新疆伊犁地区土壤表层有机质含量,经验证,本文方法能够有效地利用全局代表性差的样点推测样点能够代表地区的土壤属性空间分布,并且所得的推测不确定性与预测残差呈现正向关系,能够有效地指示推测结果的可靠程度.     

基于局部加权回归的土壤全氮含量可见-近红外光谱反演

全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。     

基于RS与GIS技术的西吉县土地利用/覆盖变化分析

 以黄土高原较早实施退耕还林的宁夏西吉县为例,选择1990和2005年的2期TM遥感图像,对西吉县1990—2005年的各种土地利用类型,从土地利用结构及空间景观特征进行分析,得到退耕还林政策实施过程中的土地利用变化情况,为黄土高原地区的土地利用政策提供决策依据。     

Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

The objective of our study was to compare the performance of the empirical best linear unbiased predictor (E-BLUP) with residual maximum likelihood (REML) with that of regression kriging (RK) for predicting soil organic matter (SOM) with the presence of different external drifts. The study was conducted on a 933 km² area in Pinggu district of Beijing. Terrain attributes (elevation, slope and topographic wetness index) calculated from DEM were used as external drift variable. The root mean squared errors (RMSE) and the mean squared deviation ratio (MSDR) were used to assess the accuracy of prediction and the goodness of fit of the theoretical estimate of error respectively. RK resulted in both the most and least accurate predictions. REML-EBLUP provided more correct residual variogram models than RK for each trend model. Our results have shown that when the value of adjusted R² is greater than 0.45, there is litter difference in the ability to increase the accuracy between REML-EBLUP and RK; and when the value is less than 0.45, the performance of REML-EBLUP is significantly better than RK. It also suggested that topographical data can further improve the accuracy of the spatial predictions of SOM by using RK and REML-EBLUP.     

流域尺度土壤厚度的模糊聚类与预测制图研究

基于土壤厚度与景观位置和特征之间的关系,运用模糊c均值聚类(FCM)方法对西苕溪流域的土壤厚度分布进行了空间预测。选取高程、坡度、平面曲率、剖面曲率、径流强度系数和地形湿度指数6个地形因子进行模糊聚类,根据相应的聚类参数将流域地形组合分为8类。利用部分调查获得的土壤剖面数据,结合样点属性和专家经验为典型区赋值,最后由加权平均得到流域土壤厚度预测图。验证结果表明,FCM方法可以对地形因子组合进行有效合理的分级,其预测精度较高,模型的稳定性较好,是一种低成本高效率的制图方法。该方法在土壤厚度预测方面具有一定的可靠性。     

Classification of soil types using geographic object-based image analysis and random forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.     

Simulation of soil types in Teramo province (Central Italy) with terrain parameters and remote sensing data

Soil surveys are an essential source of information for land management although a limited budget often reduces the amount of data available. Even if the dataset is limited, geostatistics can provide a valid estimation tool through a weighted moving average interpolation (kriging). Often, however, the spatial variability of soil properties appears smoothed and short range variability is underestimated by this kind of interpolation technique. A more realistic distribution of a given variable on the territory can be obtained through models based on stochastic simulation.     

The impact of vegetative cover type on runoff and soil erosion under different land uses

The effects of different vegetation types on runoff generation and soil erosion were investigated. The study was conducted at the Southern part of West Bank, about 10 Km north-west of Hebron city, during 2005, 2006 and 2007. Five treatments were implemented; forests planted with P. halepensis (F), natural vegetation dominated by S. spinosum (W.S), natural vegetation where S. spinosum was removed (W/o.S), cultivated land (C), and deforestation (Df). Three types of data were estimated in each plot: runoff after each rainfall event, sedimentation at the end of the rainy season, and chemical and physical soil properties. The obtained results indicate that there are significant and important differences in runoff generation and sediment production with respect to the different types of vegetative cover. Forest and natural vegetation dominated by S. spinosum treatments exhibited the lowest amounts of runoff, with averages of 2.02 and 1.08 mm, respectively, in comparison to other treatments. The removal of S. spinosum significantly increased the total amount of runoff and sedimentation compared to the forest and S. spinosum treatments. In addition, runoff significantly increased (4.03 mm) for the Df treatment compared to that of the forest site. The greatest amount of sedimentation was observed in cultivated land and with deforestation.     

基于MODIS数据设计的中国土地覆盖分类系统与应用研究

该文阐述了国内外主要土地覆盖分类系统及其土地覆盖产品在中国的表现,依据中国植被编码体系在MODIS数据试验的基础上,对现有国内土地覆盖分类系统进行了重新设计,包括7大类22个二级类别,并利用2001～2002年MODIS1km时间序列NDVI数据和多波段反射率光谱数据对中国区域进行了土地覆盖分类,结果显示分类产品能较为准确地描述中国区域土地覆盖的实际情况。     

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (p > 0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE) between the measured and predicted parameter values. The R² and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies.     

基于Logistic回归的耕地数量演变及其空间要素边际效应分析

厘清耕地演变的驱动机制对中国耕地资源可持续利用具有重要现实意义。该研究以鄂南地区为例,利用2000—2005、2005—2010和2010—2015年3期Landsat遥感影像解译成果、15类POI(PointofInterest)空间数据等,采用主成分分析、多元Logistic回归模型、边际效应分析等方法,系统分析了研究区近15a以来耕地动态变化及其时空演变规律,并着重探讨了空间要素在驱动机制方面的双向边际效应。结果表明:1)耕地演变类型在空间上表现出相同类型的集聚性,转换类型受周边同类用地空间溢出效应影响明显。在时间上表现出不同转换类型的差异性,转换类型受土地利用政策宏观调控影响向建设用地单一转化趋势明显。2000—2015年间,耕地向建设用地的转换比例自28%过渡至44%后,迅速上升至96%,主要集中在城镇周边、主要道路沿线等区域。2)空间要素对不同时期耕地不同转化类型存在不同的边际效应,2000—2015年间耕地不同转化类型的边际效应变化幅度逐渐减小、不同转化类型边际效应曲线同类交点呈同向移动趋势。自然区位因素对耕地向建设用地转换和耕地向水域转换在POI空间要素不同取值时边际效应的变化较明显。3)空间要素边际效应曲线特征揭示其影响存在临界性,在临界值范围内,边际效应随着空间要素取值不同变化明显,但在临界值范围外,边际效应趋于稳定。耕地演变空间要素边际效应的定性定量分析对耕地布局优化与区域协调发展策略制定具有良好参考作用。     

半干旱沙区土类/亚类的遥感调查制图方法

传统土壤调查制图存在低时效性、低精度等问题。为了解决半干旱沙区土壤遥感调查制图问题,该文以科尔沁左翼后旗为例,基于野外实地调查和专家知识分析了半干旱沙区土壤类型分布特征与环境因素之间的关系,并探讨了基于多时相Landsat8 OLI影像数据的半干旱沙区土类/亚类遥感调查制图方法。结果表明:利用多时相Landsat8 OLI影像数据提取的归一化差异水体指数(modified normalized difference water index,MNDWI)、盐分指数(salt index,SI)、归一化差异湿度指数(normalized difference moisture index,NDMI)、归一化差异植被指数(normalized difference vegetation index,NDVI)等环境信息,可实现对沼泽土、盐碱土、草甸土、风沙土及其亚类等半干旱沙区主要土壤类型的遥感调查制图。应用本文提出的半干旱区土类/亚类遥感调查制图方法对科左后旗进行土壤遥感调查制图和精度验证,总体精度约为72.84%,Kappa系数为0.667 8。该方法可为半干旱沙区数字土壤调查制图提供思路和参考。