Remote gully erosion mapping using aster data and geomorphologic analysis in the Main Ethiopian Rift

The Main Ethiopian Rift(MER)is an area of extreme topography underlain by post-Miocene volcanic rocks,Jurassic limestone and a Precambrian basement.A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land.We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER)data and geomorphologic parameters to discern patterns and features of gully erosion in the MER.Maximum Likelihood Classifica-tion(MLC),Support Vector Machine(SVM),and Minimum Distance(MD)classifiers are used to extract different gully shapes and patterns.Several spatial textures based on Grey Level Co-occurrence Matrices(GLCMs)are then generated.Afterwards,the same classifiers are applied to the ASTER data combined with the spatial texture information.We used geomorphologic parameters ex-tracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes.The classifications show accuracies varying between 67% and 89%.Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening.This study reveals the utility of combining ASTER data and spatial textural infor-mation in discerning areas affected by gully erosion.     

东北黑土区侵蚀沟遥感影像特征提取与识别

东北黑土区是中国重要的粮食生产区,而长期的开垦造成了严重的水土流失现象,坡耕地表面出现大量的侵蚀沟。侵蚀沟的识别是土壤侵蚀监测的重要手段之一,目前遥感技术在侵蚀沟的识别中应用广泛,但自动化程度不高。针对特定地物影像的识别,如何选取最能够有效描述该地物的特征是解决问题的关键。本文构建了耕地和侵蚀沟遥感影像的训练样本集,基于样本集分别提取了由光谱特征和纹理特征组成的浅层特征、SIFT特征经编码后得到的中层特征,以及利用卷积神经网络提取的深层特征;再基于不同层次的特征选用合适的分类器对遥感影像进行分类,识别出含有侵蚀沟的遥感影像,形成了一套针对侵蚀沟的特征提取与识别方法,为东北黑土区的耕地保护提供有力支持。测试结果表明:基于中层特征的识别精度最高,为98.5%,但该特征需要人工设计,自动化程度有限;而利用卷积神经网络可自动提取深层特征,其识别精度达到了95.5%,同时大大提高了自动化程度,满足侵蚀沟影像的识别的需求。     

基于高分辨率影像的冲沟发育及其时空变化研究

选取栖霞小流域为例,以地理信息系统和遥感技术为平台,对近10 a来冲沟发育进行了定量研究,发现近10 a来冲沟面积增加了13.3%,冲沟数量基本保持不变。通过对冲沟在不同高程、坡度、坡向和土地利用等影响因子的分异特征研究发现,在不同的时空变化下,冲沟受人为和自然两方面的影响,但人类活动对冲沟的影响要大于自然过程,因此要特别注意采用合适的耕作方式并采取工程措施保持水土。     

An assessment of gully erosion along major armoured roads in south-eastern region of South Africa: a remote sensing and GIS approach

An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R²?=?0.69, α = 0.00) and road contributing surface area (R²?=?0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R² = 0.52, α = 0.00) and rainfall (R² = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.     

Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping

Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km² pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover.     

一种黄土区土壤侵蚀强度遥感调查新方法

通过对TM图像的线性纹理提取和密度统计,首先获得了黄土丘陵区沟谷密度图,然后由沟谷密度反演区域土壤侵蚀强度。这种方法避开了影响土壤侵蚀量的诸多复杂因子。可直接从水土流失的外在表现---沟谷密度来反推土壤侵蚀强度。研究结果证明,这种方法对快速调查大范围黄土丘陵区的土壤侵蚀强度是十分有效的。     

Use of remote sensing to map gully erosion along the Atbara River, Sudan

The problems and impact of gully erosion along the Atbara River (Sudan), situated in semi-arid and arid environments, were investigated. The total gross area of gullied land and the loss of arable land by gully erosion were estimated. Multi-date sets of panchromatic aerial photographs and Landsat images (TM) were selected to represent two sites in the arid (New Halfa) and semi-arid (Showak town) zones along the Atbara River. Photo interpretation was conducted using physiographic and element methods. The interpretations detected the effects of water action in different climatic zones on geology, lithology, vegetation and land use. The results showed that the traditional rainfed agriculture has accelerated gully erosion in the semi-arid rather than in the arid zone. The progressive rate of gully erosion in the semi-arid zone resulted in loss of arable land at about 13.4 km² yr-1 and 9.8 km² yr-1 in the periods 1985–1987 and 1987–1990, respectively. The study provided data on the monitoring and mapping of gully erosion along the Atbara River and its tributaries.     

植被稀疏地区沟蚀变化的地面激光扫描监测与沟蚀量估算

针对传统沟蚀监测手段劳作强度大,且数据采集的完整性、代表性受切沟复杂地形制约等问题,提出了一种针对植被稀疏地区沟蚀变化的地面激光扫描（terrestrial laser scanning,TLS）监测方法,形成了一套数据处理与侵蚀量计算技术流程。以河北省官厅水库东岸某大型切沟为例,利用高精度TLS进行两年3期野外监测与点云数据分析。通过点云配准、滤波、重采样及曲面拟合等预处理,生成不同采样分辨率下3期切沟表面模型,并提取地形信息;采用杨赤中滤波推估法计算并比较不同点云重采样分辨率下的沟蚀量。结果表明:（1）当点云重采样分辨率与切沟表面凹凸微结构暨石块粒径（2~6cm）接近时,沟蚀量估算值趋于稳定、结果可靠;（2）经侵蚀作用,切沟外壁表面高程整体降低2~20 cm;（3）切沟内壁侵蚀量不均衡,坡度较大处侵蚀最为显著。     

Land cover mapping at Alkali Flat and Lake Lucero,White Sands,New Mexico,USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.     

Assessment of Post-fire Soil Erosion Risk in Fire-Affected Watersheds Using Remote Sensing and GIS

Soil erosion is a prominent cause of land degradation and desertification in Mediterranean countries. The detrimental effects of soil erosion are exemplified in climate (in particular climate change), topography, human activities, and natural disasters. Forest fires, which are an integral part of Mediterranean ecosystems, are responsible for the destruction of above-and below-ground vegetation that protects against soil erosion. Under this perspective, the estimation of potential soil erosion, especially after fire events, is critical for identifying watersheds that require management to prevent sediment loss, flooding, and increased ecosystem degradation. The objective of this study was to model the potential post-fire soil erosion risk following a large and intensive wildland fire, in order to prioritize protection and management actions at the watershed level in a Mediterranean landscape. Burn severity and preand post-fire land cover/uses were mapped using an ASTER image acquired two years before the fire, air photos acquired shortly after the fire, and a Landsat TM image acquired within one month after-fire. We estimated pre-and post-fire sediment loss using an integrated GIS-based approach, and additionally we analyzed landscape erosion patterns. The overall accuracy of the severity map reached 83%. Severe and heavy potential erosion classes covered approximately 90% of the total area following the fire, compared to 55% before. The fire had a profound effect on the spatial erosion pattern by altering the distribution of the potential erosion classes in 21 out of 24 watersheds, and seven watersheds were identified as being the most vulnerable to post-fire soil erosion. The spatial pattern of the erosion process is important because landscape cover heterogeneity induced especially by fire is a dominant factor controlling runoff generation and erosion rate, and should be considered in post-fire erosion risk assessment.     

土地覆盖制图：基于最优化遥感数据的支撑向量机分类

遥感数据具有在不同空间、光谱和时间尺度上获取地表测量信息的能力,使其成为获取土地覆盖信息的一个主要数据源。影像分类即把卫星影像上的相关像元划分给某类已知的土地覆盖类型的过程。支撑向量机（SVMs）是一种土地覆盖分类的新技术。三种常用的SVMs是：基于线性和多项式的SVM以及具有高斯核函数的SVM分类器,分类能否成功地应用有赖于其各自选择的最佳参数。但是海量的遥感数据使得这些参数的确定速度十分缓慢。本文研究了一种新的基于最优化遥感数据压缩技术的SVM分类方法。研究显示用于获取SVM参数的数据量能够在不影响土地覆盖的分类精度的前提下进行压缩。数据压缩成功的应用于多项式和高斯核函数的SVM分类,而线性SVM的分类精度却非常低。     

Using geoinformatics and geomorphometrics to quantify the geodiversity of Crete,Greece

The geodiversity of Crete is quantified in this study, based on the classification of geomorphometric, geological and climatic factors. A number of geomorphometric variables, extracted from the ASTER Global Digital Elevation Model (ASTER G-DEM) in conjunction with geological and climatic information, are evaluated through various algorithms incorporated into Geographical Information System (GIS) software’s. The derived geoinformatic data sets are then analyzed to produce the geodiversity of Crete. The geodiversity map is used to quantify the geodiversity, by calculating landscape diversity and other spatial pattern indices. Those indices are evaluating the richness, evenness, fragmentation and shape of the landscape patch types. The outcome of this study has highlighted that western Crete is characterized by complex geodiversity with more irregular, elongated and fragmented landscape patterns relative to the eastern part of the island. The geodiversity indices provide insights into the processes shaping landscapes, particularly the “battle” between neotectonic landscape deformation and erosion/deposition. The methodology presented can be useful for decision makers when evaluating a regions geological heritage, planning the management of natural resources, or designating areas for conservation.     

Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+

Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.     

Fusion of VNIR and thermal infrared remote sensing data based on GA-SOFM neural network

The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR (visible and near infrared) data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM (genetic algorithms & self-organizing feature maps)-ANN (artificial neural network). Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.     

Gully boundary extraction based on multidirectional hill‐shading from high‐resolution DEMs

The gully boundary, which distinguishes gully from non‐gully areas, is a significant geomorphologic feature for research on gully development and gully erosion. This study presents a new method based on multidirectional hill‐shading maps, which identify the gully (i.e., shadow area) from inter‐gully (i.e., non‐shadow area). These shadows obtained from various illumination azimuths are merged; consequently, the border of the shadows, which is the gully boundary line, can be achieved. In this process, two key parameters, namely, altitude and azimuth of light, affect the accuracy of gully boundary extraction. The experiments in Yaojiawan area of China show that the method of average median slope of all sampling profiles across the gully boundary is effective and practical for light altitude selection. Moreover, the six azimuths are sufficient for gully boundary extraction in a loess hilly area. The application in the Madigou area indicates the replicability and rationality of this method. A comparison with the positive‐negative terrain and slope variation method confirms a higher accuracy of gully boundary extraction by the proposed method in terms of visual interpretation, length, and contour‐matching difference with reference to manually digitalized results. Accuracy assessment indicates that the proposed method is applicable for gully boundary extraction based on high‐resolution DEMs.     

MODIS土地覆盖分类的尺度不确定性研究

以空间异质性较强的枯水期鄱阳湖为研究区,以搭载于同一卫星平台、具有同一观测时间和较高空间分辨率的ASTER数据为参照,分析研究了MODIS数据在土地覆盖分类中由空间尺度带来的不确定性。首先基于MODIS三角权重函数,建立了从ASTER到MODIS的尺度转换方法;然后对不同空间分辨率的数据进行土地覆盖分类,并基于误差矩阵和线性模型分析了MODIS土地覆盖分类结果的误差来源。结果表明,空间分辨率和光谱分辨率与成像方式这两类因素对MODIS与ASTER分类结果差异的贡献比例约为(6.6—11.2):2;MODIS像元尺度对研究区水体的分类不确定性影响较低,而对森林的不确定性影响可达63%。由此可见,在基于MODIS数据的土地覆盖分类研究中,空间尺度所产生的不确定性是比较显著的。这些研究结果对于土地覆盖分类及变化检测、尺度效应和景观生态学不确定性研究,有积极的参考意义。     

Waveform-based point cloud classification in land-cover identification

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.     

Effects of watershed discretization on estimation of hydrologic parameters

This study evaluates how watershed discretization affects estimation of hydrologic parameters using GIS data. Two aggregation methods were evaluated using three GIS data sets for a large watershed in Kansas, which is discretized into five different levels. The two aggregation methods are weighted-average and dominant-value. The three GIS data sets, soils, land use, and temperature, constitute three commonly used hydrologic parameters with distinct spatial patterns. The study evaluated the aggregation effects measured in terms of statistical distribution, spatial distribution, information level, and spatial dependence of the aggregated data. Results indicate that: (1) statistically, the mean and modal values of the source data are well preserved through aggregation but with a reduced standard deviation; (2) changes in spatial patterns are less predictable than those of the statistical distribution, and the changes depend on the geometric similarity and spatial overlap between the source and target polygons; (3) the information level in general decreases with aggregation for the dominant method, and it increases for the average method although the original values are altered; and (4) spatial dependence generally increases with aggregation.     

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.     

甘孜州九龙县水土流失评价研究

本文以九龙县为研究区,根据沟壑密度、坡度、植被盖度、土地利用类型和高程5个因子,结合ARC-GIS空间分析功能和MATLAB数学建模编程的功能,应用GA-BP模型(遗传算法优化BP神经网络)对研究区的水土流失进行非线性智能定量评价,并与层次分析法的结果对比分析,阐述了GA-BP模型的优越性。本次研究可以为九龙县水土流失预防和治理提供有益的参考。