Spectral indices for estimating ecological indicators of karst rocky desertification

Karst rocky desertification is a process of land desertification associated with human disturbance of the fragile eco-geological setting of karst ecosystems. The fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and exposed bedrock are key ecological indicators of the extent and degree of land degradation in karst regions. In this study, field spectral-reflectance measurements were used to develop a karst rocky desertification synthesis index (KRDSI) based on unique spectral features observed in non-vegetation land-cover types (NPV, bare soil and exposed bedrock) and were used to estimate the fractional cover of NPV, bare soil and exposed bedrock. Compared with linear spectral unmixing (LSU) using a tied-spectrum transform, the KRDSI is more consistent with the field measurement of non-vegetation land-cover fractions. This study indicates that ecological indicators of karst rocky desertification can be extracted relatively simply with the combination of vegetation indices and KRDSI values.     

Evaluating MODIS soil fractional cover for arid regions,using albedo from high-spatial resolution satellite imagery

Broad-scale high-temporal frequency satellite imagery is increasingly used for environmental monitoring. While the normalized difference vegetation index (NDVI) is the most commonly used index to track changes in vegetation cover, newer spectral mixture approaches aim to quantify sub-pixel fractions of photosynthesizing vegetation, non-photosynthesizing vegetation, and exposed soil. Validation of the unmixing products is essential to enable confident use of the products for management and decision-making. The most frequently used validation method is by field data collection, but this is very time consuming and costly, in particular in remote regions where access is difficult.

This study developed and demonstrates an alternative method for quantifying land-cover fractions using high-spatial resolution satellite imagery. The research aimed to evaluate the bare soil fraction in a sub-pixel product, MODIS Fract-G, for the natural arid landscapes of the far west of South Australia. Twenty-two sample regions, of 3400 sampling points each, were investigated across several arid land types in the study area. Albedo thresholds were carefully determined in Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument Stereo Mapping (ALOS PRISM) images (2.5 m spatial resolution), which separated predominantly bare soil from predominantly vegetated or covered soil, and created classified images. Correlation analysis was carried out between MODIS Fract-G bare soil fractional cover and ALOS PRISM bare soil proportions for the same areas. Results showed much lower correlations than expected, though limited agreement was found in some specific areas. It is posited that the Moderate Resolution Imaging Spectroradiometer (MODIS) fractional cover product, which is based on unmixing using the NDVI and a cellulose absorption index (CAI) proxy, may be generally unable to separate soil from vegetation in situations where both indices are low. In addition, separation is hampered by the lack of ‘pure pixels’ in this heterogeneous landscape. This suggests that the MODIS fractional cover product, at least in its present form, is unsuited to monitor sparsely vegetated arid landscapes.     

基于混合像元分解的喀斯特石漠化地物丰度估测

我国西南喀斯特地区长期存在以石漠化为特征的土地退化问题,是我国三大生态问题之一。喀斯特地区地表复杂度高,具有高度时空异质性,像元混合现象严重,植被、裸岩和裸土为喀斯特地区典型地物,使得评价喀斯特石漠化的关键指标(如裸岩率、植被覆盖度)获取比较困难,高光谱遥感在混合像元分解方面有独特优势,可以获取地物端元的丰度。通过地面试验表明光谱指数能够表征地物覆盖度,进而以Hyperion高光谱影像为数据源,利用连续最大角凸锥方法从影像中提取这3类地物的端元,运用半约束和全约束线性光谱分解方法估算其丰度。研究表明:半约束线性分解得到的丰度优于全约束分解结果,其反演的植被、裸土和裸岩的丰度与相应的光谱指数间具有显著线性相关性,确定系数R²分别为0.92、0.66与0.84,表明地物丰度能够表征其覆盖度。因此,通过混合像元分解算法反演地物丰度来提取喀斯特石漠化因子具有一定的可行性,这为高光谱遥感在喀斯特石漠化中的评价和监测奠定了理论和算法基础。     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.     

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.     

Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery

Hyperspectral determination of soil types has the potential to become an important addition to the methods used for classification and mapping of soils. In this study laboratory measured spectra of different soils, vegetation and crop residue were combined to simulate hyperspectral remote sensing imagery. The overall aim was to examine the spectral unmixing of these materials under laboratory conditions to better understand the limits to prediction of soil types and determination of cover fractions. Two different methods were utilized to mix spectra of the soil and vegetation and substantial differences were observed in the unmixing results from the different image types, particularly in mixed pixels. Results found pure soils were easily distinguished from each other when not mixed with vegetation, while some mixes of soil and vegetation were confused as pure soil spectra. The accuracy of abundance fractions retrieved in the unmixing process also varied substantially with soil type and vegetation cover.     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

用混合像元线性模型提取中等植被覆盖区的粘土蚀变信息

基于混合像元线性分解模型,针对中等植被覆盖区提出了一种提取粘土蚀变信息的新方法。主要分3步实现:用混合像元线性分解模型提取植被覆盖丰度;对线性模型进行完善,并依此重构不含有植被信息的新的多波段图像;利用TM5/TM7增强粘土蚀变信息。经验证,提取的植被信息以及粘土类蚀变信息与实际吻合较好,与基于比值-主成分分析的方法相比有明显的优越性。
     

A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series

There is a significant need to provide nationwide consistent information for land managers and scientists to assist with property planning, vegetation monitoring applications, risk assessment, and conservation activities at an appropriate spatial scale. We created maps of woody vegetation cover of Australia using a consistent method applied across the continent, and made them accessible. We classified pixels as woody or not woody, quantified their foliage projective cover, and classed them as forest or other wooded lands based on their cover density. The maps provide, for the first time, cover density estimates of Australian forests and other wooded lands with the spatial detail required for local-scale studies. The maps were created by linking field data, collected by a network of collaborators across the continent, to a time series of Landsat-5 TM and Landsat-7 ETM+ images for the period 2000–2010. The fractions of green vegetation cover, non-green vegetation cover, and bare ground were calculated for each pixel using a previously developed spectral unmixing approach. Time series statistics, for the green vegetation cover, were used to classify each pixel as either woody or not using a random forest classifier. An estimate of woody foliage projective cover was made by calibration with field measurements, and woody pixels classified as forest where the foliage cover was at least 0.1. Validation of the foliage projective cover with field measurements gave a coefficient of determination, R²,of 0.918 and root mean square error of 0.070. The user’s and producer’s accuracies for areas mapped as forest were high at 92.2% and 95.9%, respectively. The user’s and producers’s accuracies were lower for other wooded lands at 75.7% and 61.3%, respectively. Further research into methods to better separate areas with sparse woody vegetation from those without woody vegetation is needed. The maps provide information that will assist in gaining a better understanding of our natural environment. Applications range from the continental-scale activity of estimating national carbon stocks, to the local scale activities of assessing habitat suitability and property planning.     

Comparative analysis of urban reflectance and surface temperature

Urban environmental conditions are strongly dependent on the biophysical properties and radiant thermal field of the land cover elements in the urban mosaic. Observations of urban reflectance and surface temperature provide valuable constraints on the physical properties that are determinants of mass and energy fluxes in the urban environment. Consistencies in the covariation of surface temperature with reflectance properties can be parameterized to represent characteristics of the surface energy flux associated with different land covers and physical conditions. Linear mixture models can accurately represent Landsat ETM+ reflectances as fractions of generic spectral endmembers that correspond to land surface materials with distinct physical properties. Modeling heterogeneous land cover as mixtures of rock and/or soil Substrate, Vegetation and non-reflective Dark surface (SVD) generic endmembers makes it possible to quantify the dependence of aggregate surface temperature on the relative abundance of each physical component of the land cover, thereby distinguishing the effects of vegetation abundance, soil exposure, albedo and shadowing. Comparing these covariations in a wide variety of urban settings and physical environments provides a more robust indication of the global variability in these parameter spaces than could be inferred from a single study area. A comparative analysis of 24 urban areas and their non-urban peripheries illustrates the variability in the urban thermal fields and its dependence on biophysical land surface components. Contrary to expectation, moderate resolution intra-urban variations in surface temperature are generally as large as regional surface heat island signatures in these urban areas. Many of the non-temperate urban areas did not have surface heat island signatures at all. However, the multivariate distributions of surface temperature and generic endmember fractions reveal consistent patterns of thermal fraction covariation resulting from land cover characteristics. The Thermal-Vegetation (TV) fraction space illustrates the considerable variability in the well-known inverse correlation between surface temperature and vegetation fraction at moderate (< 100 m) spatial resolutions. The Thermal-Substrate (TS) fraction space reveals energetic thresholds where competing effects of albedo, illumination and soil moisture determine the covariation of maximum and minimum temperature with illuminated substrate fraction. The dark surface endmember fraction represents a fundamental ambiguity in the radiance signal because it can correspond to either absorptive (e.g. low albedo asphalt), transmissive (e.g. deep clear water) or shadowed (e.g. tree canopy shadow) surfaces. However, in areas where dark surface composition can be inferred from spatial context, the different responses of these surfaces may still allow them to be distinguished in the thermal fraction space.     

An object‐oriented approach for analysing and characterizing urban landscape at the parcel level

This paper presents an object‐oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high‐resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three‐level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object‐oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.     

Land degradation and erosion risk mapping by fusion of spectrally-based information and digital geomorphometric attributes

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describing the spectrum shape to the short-wave infrared region, and of identifying landscape units according to their sensitivity to land degradation. Our results demonstrate that the spatial distribution of regional patterns of land degradation can be reliably mapped by using both indices describing the spectrum shape and spectral unmixing. The latter holds great potential for operational mapping of soil conditions and erosion features from optical images. Moreover, landscape-unit analysis shows that DEM (Digital Elevation Model) variables combined with spectral information are very useful for land degradation assessment. This approach allowed us to segment the landscape into different units according to their lithology and vegetation characteristics, as well as their susceptibility to water erosion.     

The effects of vegetation on the ability to map soils using imaging spectrometer data

Remote sensing applied to tasks of mapping soil and rock surfaces must address the problem of vegetation cover in all but the most arid terrain. Masking out pixels with a high proportion of vegetation using a threshold on the near-infrared/red ratio is a popular strategy for live vegetation. The important effects of dead vegetation on the SWIR reflectance is usually ignored. Data gathered by the GER-II imaging spectrometer over a semi-arid area near Almaden, south central Spain were used to test the sensitivity of thematic soil mapping to variable cover of live and dead vegetation. After calibration to reflectance a least-squares unmixing analysis was performed using image end-members and proportions maps of vegetation and soil/rock components generated. Despite a low signal-to-noise ratio, three soil/rock and four vegetation endmembers were successfully mapped and validated from field estimates. A quantitative assessment was made of the effects of live and dead vegetation on the ability of the unmixing analysis to distinguish between granite and shale soils using synthetically mixed spectra gathered using field spectroradiometry and statistical analysis of the imaging spectrometer data. Dead vegetation was shown to have a greater impact on soil spectra than live vegetation. The ability to distinguish between the soils was lost at 50-60 per cent vegetation cover.     

USLE/RUSLE模型中植被覆盖因子多光谱影像计算

在各个土壤侵蚀模型中,准确确定植被覆盖影响因子是一项重要的工作。在以往的研究和应用过程中,对植被盖度值的确定通常是通过对地表植被覆盖类型或借助植被指数进行分级赋值,这些方法存在着分类标准的不确定性和较大随机误差。本文针对水土流失方程中植被盖度确定问题,利用多光谱数据,使用改进型植被指数分析及混合像元线性分解方法,结合地面调查数据,对土壤侵蚀模型中植被因子进行了遥感定量分析,实验结果证明了这种方法的优势所在。     

Estimating ground fractional vegetation cover using the double-exposure method

Fractional vegetation cover (FVC) is a key parameter in ecological models. It is important to determine the ground FVC quickly and accurately in studies of soil erosion, surface energy balance, and carbon cycling. As one of the FVC ground measurement methods, the photographic method is easy to operate with relatively high precision. However, its classification result showed poor accuracy when an image of a high-contrast scene contained a shadow region where a low signal-to-noise ratio (SNR) existed, because the single-exposure image in the photographic method did not contain sufficient surface information about both the illuminated and shadowed parts. This article presents application of a double-exposure photographic method to determine vegetation cover in the shadow region of an image. It consists of two measurements used in acquiring images (normal and over-exposure) and one image-processing part to handle the obtained images. Illuminated vegetation and soil, as well as the shadow region, was classified with the normally exposed image in the intensity, hue, and saturation (IHS) colour space, and the shadow region was further classified as shadowed vegetation and shadowed soil using the over-exposed image. The results indicate that the over-exposed image reduced the average bias of the FVC in the shadow region from 15.40% to ?4.14% and the root mean square error (RMSE) from 0.174 to 0.066. The RMSE of the entire scene was 0.055 in the over-exposed image and 0.092 in the single-exposed image. The double-exposure method also showed a better classification result than the high dynamic range method in deep shadow regions. This study shows that this method is capable of distinguishing vegetation and soil in the shadow region and thus it is an effective and accurate method for ground FVC measurement.     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated. We identified two types of coffee plantations (‘open-canopy’ and ‘close-canopy’) along with natural forest/shrubland, mangrove, water bodies, sandy coastal soils, bare soil, urban areas and agriculture. Notwithstanding the small sample size of the accuracy data, our assessment revealed an overall accuracy of 76.7% (Kappa coefficient?=?0.68), considering only the four classes with independent field data. The overall classification accuracy for distinguishing coffee plantations from non-mangrove natural forest was 81.6% and the classification accuracy for distinguishing ‘open-canopy’ from ‘close-canopy’ coffee plantations was 85.7%. We are encouraged by the results of this prototype study. They indicate that remote-sensing techniques can be used to distinguish different classes of coffee production systems and to differentiate coffee from natural forest.     

Quantifying the spatial distribution of soil mass wasting processes after the 2008 earthquake in Wenchuan, China: A case study of the Longmenshan area

The beautiful Longmenshan area is one of the main tourist attractions in Sichuan Province, China. The epicenter of a catastrophic earthquake measured at 8.0 Ms (China Seismological Bureau), occurred within this area at Wenchuan (31°01′16″N, 103°22′01″E) at 14:28 May 12, 2008 (Beijing time). The earthquake triggered numerous types of landslide transport and hazards, including soil and debris avalanches, rockfalls, slumps, debris flows, creation of barrier lakes and slope flattenings. This paper examines the landslide hazards in the Longmenshan area caused by the earthquake using remotely sensed images, mainly Beijing-1 Microsatellite data before and after the earthquake, compared to digital elevation maps and slope gradient maps, land use and vegetation cover maps. Areas of erosion and loss of vegetation were compared from pre- and post-earthquake data, from which were calculated changes in vegetated areas, bare slopes, and mass movement during the earthquake. These events occurred over altitudes from 1000 to 4000 m and on slope angles between 25 and 55°. The results show that the total area of erosion and land movement due to the earthquake increased by 86.3 km² (19.2% of the study area). Compared with pre-earthquake, the areas of very low intensity soil erosion and moderate intensity soil erosion were respectively reduced by 3.6 km², 24.3 km² and 30.9 km². On the other hand, the areas of severe and very severe intensity soil erosion were substantially increased by 45.8 km² and 99.2 km². In the post-earthquake stage, the bare areas (vegetation cover < 15%) have increased by 65.8 km². Without vegetation, the denuded earthquake damaged slopes and other high risk sites have become severe erosion problems. Thus, it is essential to continue long-term monitoring of mass wasting in the denuded areas and evaluate potential risk sites for future landslides and debris flows. We anticipate that these results will be helpful in decision making and policy planning for recovery and reconstruction in the earthquake-affected area.