Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.

<p>以甘肃省白龙江流域主要城镇环境工程地质勘查及历次地质调查数据为基础, 分析了地貌、人类活动等因素对白龙江流域甘肃段地质灾害的控制作用, 选定了坡度、岩性、地震、河流、降水等评价因子, 采用层次分析法和信息量法组合赋权的方式确定评价因子的综合权重, 最后利用GIS的叠加、以及重分类功能进行了地质灾害危险性分区评价. 结果表明: 白龙江流域地质灾害主要分布于人口相对密集的城镇(河谷)一带, 地质灾害分布相对密集, 危险性较大. 而以农牧业为主的中、高山区地质灾害分布相对稀疏, 危险性较低.</p>

5·12汶川大地震诱发了大量滑坡、崩塌、泥石流等次生地质灾害,对人民群众的生命财产和社会经济的发展形成了严重威胁.针对次生地质灾害危险性评价,选取重灾区汶川县作为研究区域,利用遥感与地理信息技术的空间数据管理和空间数据分析平台,获取了研究区的次生地质灾害信息,分析了研究区内次生地质灾害与各影响因子,包括地形地貌、地层岩性、水系、地震断裂之间的相关性特征,并结合信息量法模型进行次生地质灾害危险性评价.高度、中度和轻度危险区的面积分别为1130.196km2、1739.584km2、1213.219km2.本次地震触发次生地质灾害的分布具有集群式分布的特点,即断裂带及其附近地区地质灾害集中发育,而远离断裂带区地质灾害很快衰减,呈零星分布;从灾害发育的区域特征分析,汶川县震后次生地质灾害呈现出北部和东部重、西部和南部轻的特点.所得研究结果与实际情况较吻合,表明地理信息系统结合信息量模型能够快速、有效地对次生地质灾害的空间分布以及危险性作出评价.

<p>汶川Ms 80级大地震诱发了数以万计的滑坡灾害。在大约48678 km2的滑坡影响区域内,作者采用震后遥感影像解译并结合野外调查的方法,共解译出48007处滑坡。应用GIS技术,建立了汶川地震诱发滑坡灾害及相关地形、地质空间数据库。采用地震滑坡确定性系数分析方法,分析了地震滑坡关于地震烈度、岩性、坡度、断层、高程、坡向、河流与公路等8个因素的易发程度。基于GIS栅格分析方法,分别对16种不同影响因子组合类型进行地震滑坡易发性评价。最后,应用AUC（Area Under Curve,评价曲线下面积）方法得到最佳因子组合及其对应的评价结果,使用自然分类法则方法将研究区按滑坡易发程度分为极高易发区、高易发区、中易发区、低易发区与极低易发区5类,极高易发区与高易发区面积之和约1169046km2,占研究区总面积的2402%,其中发育滑坡面积为52484 km2,占滑坡总面积的7373%。结果表明了极高与高易发区与实际滑坡之间有着良好的一致性,方法的评价结果成功率（AUC值）达到82107%。</p>

植被盖度作为地表特征指数之一,其发育和分布特征受控于生态地质环境,而地质灾害又是生态地质环境不良作用的表现方式之一,二者存在一定联系。以地质灾害发育且危害严重的丹巴县为例,采用1999年和2006年二期卫星遥感数据,提取这二个时相的植被盖度图,以及1999年～2006年植被盖度变化图,采用地理信息系统技术,将地质灾害与二期的植被覆盖图进行叠加和统计分析。其结果表明,地质灾害主要分布在海拔较低,人类工程活动强烈的低植被覆盖和较低植被覆盖区域,且地质灾害在植被覆盖度减少的区域较之植被盖度不变和增加的区域,更加容易突发,研究结果与区内其它地质灾害工作成果吻合。

宁夏中宁县是全区地质灾害较为发育的地区之一。在充分研究区域地质环境资料的基础上,利用SPOT6、ETM＋以及Land Sat8 OLI等多源、多时相遥感数据,在GIS技术和数学模型的支持下,通过对SPOT6遥感影像特征研究,建立地质灾害解译标志,共解译出崩塌65处、泥石流沟26条、地面塌陷3处;采用基于多源信息复合的支持向量机分类方法（SVM）,对研究区地表环境变化进行动态监测,分析了12年间地表环境变化。同时,对研究区植被、坡度、坡向与地质灾害分布的关系进行了讨论。研究结果表明,遥感技术不仅可以快速查明地质灾害的类型、规模以及分布特征,而且能够提高地质灾害解译效率和精度,在宁夏中宁县地质灾害详细调查工作中取得了较好应用效果。

明确滑坡和泥石流灾害发生百分率与地学因子间的关系，对相关预报和危险度评价研究具有重要意义.基于全国18431个滑坡和泥石流灾害点的定位信息，结合1 km×1 km栅格内6个环境因子（高程、高差、坡度、坡向、植被类型、植被覆盖度）的空间分布数据，利用频率比数分析方法分析了这些环境因子与滑坡和泥石流灾害的关系.结果表明：滑坡和泥石流更多地分布于我国三大自然阶梯的第1和第2过渡带中海拔较低的地区；1 km×1 km栅格内高程差为300 m时，滑坡和泥石流灾害发生的可能性最大；当坡度为30°时，滑坡和泥石流灾害爆发的可能性最大；山区林地和坡耕地是最易发生滑坡和泥石流的两个地类；植被覆盖度在80%～90%时，滑坡和泥石流灾害的发生频率最高.

白龙江流域影响滑坡形成的主要因素包括地质构造、地形地貌、水文、气象、植被和人工干扰等,其中地表植被作为滑坡形成的主要外在因素对滑坡的形成和发展具有重要的作用,已引起学术界的广泛关注.以植被覆盖度为主要度量指标,选择2002,2005,2008,2011年4个时段的遥感N DV I值,在改进像元二分法的基础上,探讨了近10年来该流域地表植被覆盖度及其变化特征,并分析了其与滑坡点空间格局的关系.结果表明：该流域植被覆盖度总体变化微弱,但区域内部植被覆盖度变化具有明显的空间异质性,其中低覆盖区域覆盖度呈明显的增加趋势；而历年来,该流域植被覆盖度高的地区滑坡点密度均较低,滑坡点占总滑坡的20.6%,空间分布呈离散状态,而植被覆盖度低的地区滑坡密度较高,滑坡点占总滑坡的34.5%,空间分布主要呈聚集状态,说明白龙江流域植被覆盖度与滑坡点密度呈现负相关关系,植被覆盖度的高低是滑坡发生的一个主要影响因素.研究表明,近10年来,该流域低植被覆盖区域的植被覆盖度增加较快,说明在国家实施退耕还林还草政策后,该流域植被恢复较快,生态脆弱程度减轻,这将为地方防治滑坡灾害的发生起到积极的推动作用.本研究结论可为全流域制定宏观尺度上的滑坡生态防治策略提供科学依据.

ABSTRACT A variety of vegetation indices have been used to assess the state and monitor the evolution of the terrestrial biosphere. Early indices were easy to compute but very sensitive to soil and atmospheric effects. Modified indices with a reduced sensitivity to soil brightness changes were then proposed. More recently, new indices have been designed to be less affected by either atmospheric or soil conditions, or both. In this paper, we propose and demonstrate an objective method to evaluate, through model simulation studies, the performance of a representative sample of three such indices (NDVI, SAVI and GEMI) with respect to their capability to retrieve the fractional vegetation cover and the leaf area index from AVHRR optical data. The proposed performance criterion is based on the concept of signal to noise ratio, where the signal is defined to capture the sensitivity of the index to the desired information, and the noise is designed to measure the sensitivity of this index to undesirable perturbations. It is found that no single index is optimal under all conditions, but that improved indices (GEMI and SAVI) are generally much better than the NDVI for assessing the fractional vegetation cover. Reliable estimates of the leaf area index appear very difficult to obtain when this parameter exceeds about two because of the insensitivity of the red reflectance to multiple leaf layers.

Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.

The vegetation coverage dynamics and its relationship with climate factors on different spatial and temporal scales in Inner Mongolia during 2001 2010 were analyzed based on MODIS-NDVI data and climate data. The results indicated that vegetation coverage in Inner Mongolia showed obvious longitudinal zonality, increasing from west to east across the region with a change rate of 0.2/10 N. During 2001 2010, the mean vegetation coverage was 0.57, 0.4 and 0.16 in forest, grassland and desert biome, respectively, exhibiting evident spatial heterogeneities. Totally, vegetation coverage had a slight increasing trend during the study period. Across Inner Mongolia, the area of which the vegetation coverage showed extremely significant and significant increase accounted for 11.25% and 29.13% of the area of whole region, respectively, while the area of which the vegetation coverage showed extremely significant and significant decrease accounted for 7.65% and 26.61%, respectively. On inter-annual time scale, precipitation was the dominant driving force of vegetation coverage for the whole region. On inter-monthly scale, the change of vegetation coverage was consistent with both the change of temperature and precipitation, implying that the vegetation growth within a year is more sensitive to the combined effects of water and heat rather than either single climate factor. The vegetation coverage in forest biome was mainly driven by temperature on both inter-annual and inter-monthly scales, while that in desert biome was mainly influenced by precipitation on both the two temporal scales. In grassland biome, the yearly vegetation coverage had a better correlation with precipitation, while the monthly vegetation coverage was influenced by both temperature and precipitation. In grassland biome, the impacts of precipitation on monthly vegetation coverage showed time-delay effects.

植被覆盖变化是全球变化研究的重要内容之一,由于NDVI与植被的分布密度呈线性相关,是指示大尺度植被覆盖的良好指标,因此在宏观植被盖度的估算中常被应用。利用1987年9月26日和2009年9月22日的Landsat TM卫星影像,以NDVI为桥梁,分别计算了北京市域的植被盖度和大于0.1的NDVI差值指数,北京市域与不同生态区域两个尺度对其植被变化情况进行了量化分析,结果表明,2009年与1987年相比,北京市极低覆盖度、中覆盖度和高覆盖度植被的面积均有所减少,其所占全市土地面积的比例从1987年到2009年分别降低了5.15%和0.54%和0.03%;而低覆盖度和极高覆盖度植被的面积比例则分别增加了5.71%和0.01%。大于0.1的植被差值指数统计结果显示,全市域植被质量以改善为主,全市植被发生改善变化的土地面积共919302.3 hm²,其中发生轻微改善的比例为28.31%,中度改善的为41.33%,极度改善的面积为30.36%;全市植被发生退化变化的面积326931.12 hm², 其中发生中度退化、轻微退化和极度退化的面积分别占到了退化变化土地面积的41.98%、43.20%和14.82%。从不同区域的植被差值指数看,植被发生退化变化最明显的区域为燕山山区北部、五环以内和五至六环间区域,这几个区域退化变化的植被面积占相应区域的面积比例分别达到了30.25%、58.17%和47.38%,而且均以严重退化与中度退化为主,两者合计的面积比例分别为15.79%、44.72%和34.19%。而发生退化变化面积比例最小的区域为太行山区和延庆盆地,其退化面积占该区域植被面积的比例分别为13.35%和17.02%,且退化程度均以轻微退化和中度退化为主,其面积比例介于5%-8%之间。从植被变化的驱动力看, 目前还看不出北京这种植被变化结果与气候变化之间的直接关联。北京市植被变化的驱动力主要还是人为因素。这包括了区域性的大环境绿化生态工程建设(包括山区与平原区),城市绿化市政工程建设、平原区农业结构调整、新农村生态环境建设,以及由于降水而导致的山区河岸带变化等。其中河流水面变化对河岸带植被变化的影响范围在多年平均水面线外0-150 m范围内,0-100 m范围为受影响较大的区域。

为研究农牧交错区植被覆盖度的变化,从盐池县1999年、 2004年和2010年3期TM/ETM＋影像图入手,利用归一化植被指数（NDVI）反演了盐池县的植被盖度,结果表明：1999年、2004年、 2010年平均植被盖度分别为24.08%,26.97%和23.92%,总体呈现出现先增加后减少的趋势。对这3a的植被覆盖度进一步进行分级,发现 1999年、2004年、2010年3个时期,低植被盖度所占的比例分别为59.61%,50.75%,62.6%,呈现先减少后增加的趋势;中等植被盖 度所占比例分别为34.54%,34.58%,29.20%,呈现先增加后减小的趋势,增加的趋势不明显;高植被盖度所占比例分别为 5.85%,14.68%,8.2%,先增加后减小。影响植被覆盖度变化的因素主要有人为及政策因素、气候因素（主要是降水）。