基于地形属性使用回归, 克里格和人工神经网络方法估算饱和导水率空间分布

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.     

Field-scale spatial variability of soil calcium in a semi-arid region: Implications for soil erosion and site-specific management

Excess calcium(Ca) in soils of semi-arid and arid regions has negative effects on soil structure and chemical properties, which limits the crop root growth as well as the availability of soil water and nutrients. Quantifying the spatial variability of soil Ca contents may reveal factors influencing soil erosion and provide a basis for site-specific soil and crop management in semi-arid regions. This study sought to assess the spatial variability of soil Ca in relation to topography, hydraulic attributes, and soil types for precision soil and crop management in a 194-ha production field in the Southern High Plains of Texas,USA. Soils at four depth increments(0–2, 0–15, 15–30, and 30–60 cm) were sampled at 232 points in the spring of 2017. The Ca content of each sample was determined with a DP-6000 Delta Premium portable X-ray fluorescence(PXRF) spectrometer. Elevation data was obtained using a real-time kinematic GPS receiver with centimeter-level accuracy. A digital elevation model(DEM) was derived from the elevation data, and topographic and hydraulic attributes were generated from this DEM. A generalized least-squares model was then developed to assess the relationship between soil Ca contents of the four layers and the topographic and hydraulic attributes. Results showed that topographic attributes, especially slope and elevation, had a significant effect on soil Ca content at different depths(P 0.01). In addition, hydraulic attributes, especially flow length and sediment transport index(STI), had a significant effect on the spatial distribution of soil Ca. Spatial variability of soil Ca and its relationships with topographic and hydraulic attributes and soil types indicated that surface soil loss may occur due to water or wind erosion, especially on susceptible soils with high slopes. Therefore, this study suggests that the application of PXRF in assessing soil Ca content can potentially facilitate a new method for soil erosion evaluation in semi-arid lands. The results of this study provide valuable information for site-specific soil conservation and crop management.     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

Estimating Soil Salinity in the Yellow River Delta,Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model (DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper (TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model (GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike’s information criterion (AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Red, near-infrared, and mid-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.     

依据传统土壤数据应用数字土壤制图法对香港山地地区土壤建立土纲信息

Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.     

基于不同地表曲面模型预测土壤有机碳含量

Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.     

普通克里格法在土壤有机碳制图中的应用

The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understand many ecosystem processes.This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in a selected part of Zambia.A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method.An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram.The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero.The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25.The root mean square error of the predictions was 0.64,which represented 58.18％ of the mean observed data for SOC.It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported.Therefore,the maps produced can be used as guides for various uses including optimization of soil sampling.     

四川丘陵区农林系统和农草系统流域的径流和土壤水分特征

A study was conducted in a hilly area of Sichuan Province, Southwestern China, to compare the streamflow and soil moisture in two upland watersheds with different land use patterns. One was an agroforestry watershed, which consisted mainly of trees with alder (Alnus cremastogyne Burkill) and cypress (Cupressus funebris Endl.) planted in belts or strips with a coverage of about 46%, and the other was a grassland primarily composed of lalang grass (Imperata cylindrica var. major (Nees) C. E. Hubb.), filamentary clematis (Clematis filamentosa Dunn) and common eulaliopsis (Eulaliopsis binata (Retz.) C. E. Hubb) with a coverage of about 44%. Streamflow measurement with a hydrograph established at the watershed outlet showed that the average annual streamflow per 100 mm rainfall from 1983 to 1992 was 0.36 and 1.08 L s^-1 km^-2 for the agroforestry watershed and the grass watershed, respectively. This showed that the streamflow of the agroforestry watershed was reduced by 67% when compared to that of the grass watershed. The peak average monthly streamflow in the agroforestry watershed was over 5 times lower than that of the grass watershed and lagged by one month. In addition, the peak streamflow during a typical rainfall event of 38.3 mm in August 1986 was 37% lower in the agroforestry watershed than in the grass watershed. Results of the moisture contents of the soil samples from 3 slope locations (upper, middle and lower slopes) indicated that the agroforestry watershed maintained generally higher soil moisture contents than the grass watershed within 0-20 and 20-80 cm soil depths for the upper slope, especially for the period from May through July. For the other (middle and lower) slopes, soil moisture contents within 20-80 cm depth in the agroforestry watershed was generally lower than those in the grass watershed, particularly in September, revealing that water consumption by trees took place mainly below the plow layer. Therefore, agroforestry land use types might offer a complimentary model for tree-annual crop water utilization.     

基于1:50 000土壤数据库并结合PKB方法估算中国浙江省土壤有机碳储量的研究

Soil organic carbon(SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change. A newly built 1:50 000 soil database of Zhejiang Province containing 2 154 geo-referenced soil profiles and a pedological professional knowledge-based(PKB) method were used to estimate SOC stock up to a depth of 100 cm for the Province. The spatial patterns of SOC stocks stratified by soil types,watershed(buffer analysis), topographical factors, and land use types were identified. Results showed that the soils in Zhejiang covered an area of 100 740 km2 with a total SOC stock of 831.49 × 106 t and a mean SOC density of 8.25 kg m-2, excluding water and urban areas. In terms of soil types, red soils had the highest SOC stock(259.10 × 106t), whereas mountain meadow soils contained the lowest(0.15 × 106t). In terms of SOC densities, the lowest value(5.11 kg m-2) was found in skel soils, whereas the highest value(45.30 kg m-2) was observed in mountain meadow soils. Yellow soils, as a dominant soil group, determined the SOC densities of different buffer zones in Qiantang River watershed because of their large area percentage and wide variation of SOC density values.The area percentages of various soil groups significantly varied with increasing elevation or slope when overlaid with digital elevation model data, thus influencing the SOC densities. The highest SOC density was observed under grassland, whereas the lowest SOC density was identified under unutilized land. The map of SOC density(0–100 cm depth) and the spatial patterns of SOC stocks in the Province would be helpful for relevant agencies and communities in Zhejiang Province, China.     

中国禹城土壤盐渍化的时空变异及其预测

This research used both geostatistics and GIS approach to compare temporal change of soil salt between 1980 and 2003, to analyze the spatial distribution of surface soil salt, to developed methods for predicting soil salinization potential based on recent improvements to the Dempster-Shafer theory, and to develop probability maps of potential salinization in Yucheng City, China. A semivariogram model of soil salt content was developed from the spherical model, and then employing kriging interpolation the spatial distribution of salt content in 2003 was obtained utilizing data from 100 soil sampling points. Potential salinization distribution was mapped using an approach that integrated soil data of the second general survey in 1980 in Yucheng City, which included groundwater salinity, groundwater depth, soil texture, soil organic matter content, and geomorphic maps. With the support of Dempster-Shafer theory and fuzzy set technique the factors that affected potential soil salinization were characterized and integrated;and then soil salinization was predicted. Finally a prognosis map of potential salinization distribution in the research area was obtained, with higher probability values indicating higher hazards to salinity processes. The distribution of the potential soil salinization probability was a successive surface.     

黄土小流域不同发育阶段地形指数变异特征分析

基于人工降雨实验获取的黄土模拟小流域高解析度DEM数据,提取其不同发育阶段的地形指数,分析了黄土小流域降雨侵蚀过程中地形指数的空间分异特征、平均地形指数变化规律及地形指数频率分布曲线特征。研究证明,地形指数空间分布与地形呈现很强的相关性,地形指数均值与坡度均值呈现对数变化关系,地形指数频率分布图随地形变化呈现规律性变化。地形指数空间变异特征分析可为研究流域地形和流域水文相似性提供基础资料。     

Relationship between soil depth and terrain attributes in karst region in Southwest China

Purpose

Soil depth generally varies in peak-cluster depression regions in rather complex ways. Because conventional soil survey methods in these regions require a considerable amount of time, effort, and consequently relatively large budget, new methods are required in karst regions.

Materials and methods

This study explored the relationship between soil depth and terrain attributes abstracted from digital elevation models (DEMs) at different spatial resolutions in the Guohua Karst Ecological Experimental Area, a representative region of peak-cluster depression in Southwest China. A uniform 140 m?×?140 m grid combined with representative hillslope methodology was used to select 171 sampling points where soil depth was measured. Nine primary and secondary terrain attributes, such as elevation, slope, aspect, especial catchment area, wetness index, length-slope factor, stream power index, relief degree of land surface, and distance from ridge of mountains, were computed from DEMs at different spatial resolutions. The optimal DEM spatial resolution was determined by Grey relational analysis (GRA) to reflect the correlations between soil depth and terrain attributes.

Results and discussion

GRA revealed that the 10-m spatial resolution DEM can best reflect the relationship between soil depth and terrain attributes; therefore, the terrain attributes at this resolution were used for multiple linear stepwise regression (MLSR) analysis. The result of MLSR indicated that slope, TWI, and elevation could explain about 61.4 % of the total variability in soil depth in the study area.

Conclusions

The terrain attributes of slope, WTI and elevation can be used to evaluate soil depth in this region very well. This proposed approach may be applicable to other peak-cluster depression regions in the karst areas at a larger scale.     

基于元胞自动机的黄土小流域地形演变模拟

黄土高原以沟沿线为基准分为正地形和负地形2种基本的地貌形态,黄土小流域正负地形演变是黄土高原地貌形态发育的缩影。该文采用元胞自动机建模方法,对人工降雨条件下室内黄土小流域正负地形的动态演变过程进行建模与模拟。试验使用近景摄影测量方法监测小流域发育过程,处理获得10mm分辨率的数字高程模型。模拟迭代过程逼真地刻画了黄土负地形区向正地形区不断蚕食的动态演化过程,并能反映出非常重要的黄土陷穴现象的发生。模拟结果在数量上和空间分布上都取得了较好的模拟效果。研究认为元胞自动机建模方法可以用来模拟黄土小流域的正负地形演变,有助于揭示黄土地形演化机制。     

水文地貌关系正确DEM的建立方法

 水文地貌关系正确DEM(hydrologically correct DEMs,Hc-DEM),是指符合水文地貌学基本原理,正确反映水文要素(水流方向、水流路径、水系网络、流域界线等)与地貌特征发生和位置关系的DEM。区域尺度水文和土壤侵蚀等研究中,地形因子参数只能利用DEM来提取,为了准确反映地面形态,有效提取地貌和水文特征因子,建立Hc-DEM是必需的。笔者对Hc-DEM的概念、建立方法进行了讨论和介绍;以黄土高原为例,提出了利用多种比例尺数字地形图和ANUDEM软件建立DEM的关键参数;通过与TIN方法建立的DEM的比较,对所建立的DEM进行了简要评价。研究表明,利用我国已有的数字地形图和ANUDEM软件,可以建立Hc-DEM,为流域水文和区域尺度水土流失定量分析模拟、区域尺度植被适宜性评价等研究提供更加直接的数据支持。     

Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County,Norway

The main objectives of this study were to model the relationship between WRB-1998 soil groups and terrain attributes and predict the spatial distribution of the soil groups using digital terrain analysis and multinomial logistic regression integrated in GIS in the Vestfold County of south-eastern Norway. A digital elevation model of 25 meter grid resolution was used to derive fifteen terrain attributes. A digitized soil map of thirteen WRB soil groups at the scale of 1:25,000 was used to obtain the reference soil data for model building and validation. First, the relationships between the soil groups and the terrain attributes were modeled using multinomial logistic regression. Then, the probability that a given soil type is present at a given pixel was determined from the logit models in ARCGIS to continuously map each soil group's spatial distribution. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes correlating with the spatial distribution of the soil groups. The prediction showed higher mean probability values for each soil group in the areas actually covered by that soil group compared to other areas, indicating the reliability of the prediction. However, the prediction performed poorly for soil groups that are not greatly influenced by topography but by other factors such as human activities.     

DEM栅格分辨率对丘陵山地区定量土壤-景观模型的影响

基于数字高程模型（Digital Elevation Model,DEM）的定量土壤-景观模型的精度依赖于DEM栅格分辨率,而DEM栅格分辨率如何影响土壤-景观模型及其预测精度目前研究较少。以西南丘陵山地区一典型汇水盆地为研究对象,以该区2.5、5、10、20和30 m DEM为基础,利用逐步线性回归方法建立起研究区不同分辨率下的定量土壤-景观模型,并应用这些模型预测研究区内土壤表层碱解氮含量的空间分布,进而比较DEM不同分辨率下土壤-景观模型及其预测精度。结果表明,随着DEM栅格分辨率的降低,比汇水面积、地形湿度指数的均值逐渐增加;平均坡度逐渐降低;曲率变化的范围逐渐减小。地形指数的这一变化规律对土壤-景观模型及其预测结果产生显著影响,模型的校正决定系数、平均绝对误差和均方根误差都以5 m栅格分辨率为转折点,分辨率低于5 m,模型的校正决定系数显著减小,平均绝对误差和均方根误差显著增加。     

基于地形因子和随机森林的丘陵区农田土壤有效铁空间分布预测

为了掌握丘陵地区农田土壤有效铁含量及其空间分布,本文以重庆市江津区永兴镇内同源成土母质的典型丘陵(2 km2)为研究区,采集309个土壤样点,利用普通克里格(Ordinary Kriging,OK)、多元线性回归(Multiple Linear Regression,MLR)、随机森林(Random Forest,RF)模型,结合高程、坡度、坡向、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数等地形因子对土壤有效铁进行空间分布预测,并通过85个验证点评价、筛选预测模型。结果表明:1)土壤有效铁与谷深、地形湿度指数存在极显著水平正相关关系,与坡度、平面曲率、剖面曲率、汇聚指数、相对坡位指数存在极显著水平负相关关系。2)随机森林模型的预测精度明显高于多元线性回归和普通克里格插值,其平均绝对误差为22.33 mg·kg-1、均方根误差为27.98 mg·kg-1、决定系数为0.76,是研究区土壤有效铁含量空间分布的最适预测模型。3)地形湿度指数和坡度是影响该区域土壤有效铁含量空间分布的主要地形因子。土壤有效铁与坡度、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数均达到极显著水平相关关系。4)研究区土壤有效铁含量范围为3.00~276.97 mg?kg-1,水田有效铁含量大于旱地;土壤有效铁具有较强的空间相关性,土壤有效铁含量空间变异主要受到结构性因素的影响。可见,基于地形因子的随机森林预测模型可以较好地解释丘陵区农田土壤有效铁含量的空间变异,研究结果为丘陵区土壤中、微量元素含量及空间分布预测提供方法借鉴和理论依据。     

Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain

The relationships between the spatial distribution of ground-cover and terrain attributes were examined in the Tabernas badlands (SE Spain) in order to understand the terrain-dependent driving forces of the spatially heterogeneous ground cover. Ground cover was mapped in the field and terrain attributes were derived from a 1-m resolution Digital Elevation Model (DEM). The association of spatial distribution of the landforms resulting from a regionalisation (using a nonhierarchical classification of the topographic overlays) and the ground-cover pattern was proved. From the analysis of relationships between terrain attributes and proportional abundance of ground-cover types, it was found that ground cover is arranged along topographic gradients: plant-covered surfaces are more abundant on low slope angles, concave slopes, relatively large contributing areas and with low length slope factor values. Unvegetated surfaces show contrary trends and lichens are associated with intermediate conditions. Relationships with local terrain attributes, such as slope angle or elevation, are more pronounced than those with terrain attributes related to sediment and water transfer, such as contributing area, wetness index or length slope factor which could be explained by the heterogeneity of runoff that is usually shorter than the hillslope length. The relationships established between the spatial distribution of ground-cover types and terrain attributes provide the basis for future development of a tool for mapping spatial distribution of ground cover in similar areas from only topographic information.     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.     

基于DEM的坡度研究--现状与展望

坡度是最基本的地貌形态指标,它对地表物质能量迁移转换具有重要影响。阐述了坡度研究的几个主要方面,包括坡度分级与坡度制图方法,DEM建立方法和DEM类型对坡度的影响,DEM分辨率对坡度的影响,坡度衰减和坡度变换研究等。指出传统坡度研究方法已与GIS的发展,DEM的广泛应用不相适应,也不能满足水文和土壤侵蚀定量模拟研究的需要。指出应从改善DEM质量和DEM对地形描述能力入手,将DEM及其基础上提取的坡度视为空间上连续变化的表面,引入数字图形图像处理方法,以区域尺度径流与土壤侵蚀模拟及其相关的数字地形分析为服务对象,对坡度问题展开系统研究。研究重点应包括:DEM类型对于坡度的影响,坡度衰减原理,坡度变换方法与变换结果应用等。