Forest Damage Assessment Research Articles

Typhoon-Induced Forest Damage Mapping in the Philippines Using Landsat and PlanetScope Images

Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to the intensity and impact of disasters. However, little attention has been paid to forest cover losses due to typhoons during disaster assessments. In this study, forest damage caused by typhoons was measured using harmonic analysis of time series (HANTS) with Landsat-8 Operation Land Imager (OLI) images. The ΔHarmonic Vegetation Index was computed by calculating the difference between HANTS and the actual observed vegetation index value. This was used to identify damaged areas in the forest regions and create a damage map. To validate the reliability of the results, the resulting maps produced using ΔHarmonic VI were compared with the damage mapped from PlanetScope’s high-resolution pre- and post-typhoon images. The method achieved an overall accuracy of 69.20%. The accuracy of the results was comparable to the traditional remote sensing techniques used in forest damage assessment, such as ΔVI and land cover change detection. To further the understanding of the relationship between forest and typhoon occurrence, the presence of time lag in the observations was investigated. Additionally, different contributing factors in forest damage were identified. Most of the forest damage observed was in forest areas with slopes facing the typhoon direction and in vulnerable areas such as near the coast and hill tops. This study will help the government and forest management sectors preserve forests, which will ultimately result in the development of a more resilient community, by making it easier to identify forest areas that are vulnerable to typhoon damage.

Study on forest ecosystem damage assessment system and management system

Ecological environment damage events will destroy or damage the balance between animal and plant habitats and ecosystems, and even pose a threat to China’s ecological security. However, at present, there are some problems in the identification and evaluation of forest ecosystem damage, such as imperfect evaluation system, insufficient quantitative evaluation methods, imperfect damage compensation management system, and lack of analysis of the overall damage of the interaction between human activities and forest ecosystem. Based on the damaged object, the system involves a total of four first-class indicators, including physical damage, mental damage, economic forest fruit loss, forest by-products loss, processing and manufacturing loss, forest tourism loss, scientific research literature and history loss, soil conservation loss, water conservation loss, wind prevention and sand fixation loss, carbon fixation and oxygen release loss, atmospheric purification loss. There are 14 secondary indicators of emergency treatment fee and investigation and evaluation fee, as well as 22 tertiary indicators, and the value quantification method of each indicator is clarified by using market value method, alternative cost method, shadow engineering method, recovery cost method and other methods. The article also discusses the management system of forest ecosystem damage from the two aspects of forestry technology department and judicial administration department. The purpose is to provide reference for the quantification and standardization of forest ecosystem damage assessment technology and the improvement of management system.

EVALUATION OF FIELD-BASED BURN INDICES FOR ASSESSING FOREST FIRE SEVERITY IN LUHANSK REGION, UKRAINE

Evaluation of forest fire severity is a basis of post-fire forest management. Remote sensing-based methods enable reliable delineation of fire perimeters, however, assessments of the degree of forest damage need to be verified and adjusted through field sampling. The forest damage assessment conducted in this study is useful for practitioners to understand and justify the design of clear cuts for restoration purposes. Thus, the aim of the study is to verify the different approaches to field assessment of forest fire severity. In this paper, the authors present a site-specific assessment of large wildfires in Luhansk oblast, Ukraine occurred in 2020 using field-based burn severity indices. The Composite Burn Index (CBI) and the Geometrically Structured Composite Burn Index (GeoCBI) were used to estimate the extent of forest damage. The Burned Area Emergency Response (BAER) methodology was also tested to assess the extent of soil damage. The authors used PlanetScope images to delineate perimeters of burned areas. These perimeters were overlaid over a forest inventory database to extract forest attributes and site characteristics for all forested and unforested areas affected by fires. Within the fire perimeters, the burned area was stratified into six strata to independently account for forest damage in diverse types of land cover. In total 73 test plots were proportionally distributed among different classes of land cover to assess fire severity using CBI, GeoCBI, and BAER approaches. It was found that the fire’s footprints covered 39,782 hectares. Among that area, 21.2% were forested lands. About 78% of burned forests were pine plantations. The highest fire intensity levels were estimated within pure pine plantations that were grown in very dry sites, while the lowest ones were associated with hardwoods forests in moisture site conditions. The average estimates of fire severity using the field-based indices varied within strata (CBI>GeoCBI) which could be an issue for assessing burn severity using remote sensing-based approaches. The authors also concluded that the BAER methodology contributed less to assessing the fire intensity because soil burn severity is not directly related to vegetation damage. This work creates a foundation for further assessment of fire severity using satellite imagery. As a result of this study, a spatial data set of sample plots was proposed that can facilitate calibrating approaches used to map fire severity in the region

森林生态系统损害评估体系与管理制度研究

森林生态系统损害评估体系与管理制度研究

Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy

Abstract The frequency of extreme storm events has significantly increased in the past decades, causing significant damage to European forests. To mitigate the impacts of extreme events, a rapid assessment of forest damage is crucial, and satellite data are an optimal candidate for this task. The integration of satellite data in the operational phase of monitoring forest damage can exploit the complementarity of optical and Synthetic Aperture Radar (SAR) open datasets from the Copernicus programme. This study illustrates the testing of Sentinel 1 and Sentinel 2 data for the detection of areas impacted by the Vaia storm in Northern Italy. The use of multispectral Sentinel 2 provided the best performance, with classification overall accuracy (OA) values up to 86 percent; however, optical data use is seriously hampered by cloud cover that can persist for months after the event and in most cases cannot be considered an appropriate tool if a fast response is required. The results obtained using SAR Sentinel 1 were slightly less accurate (OA up to 68 percent), but the method was able to provide valuable information rapidly, mainly because the acquisition of this dataset is weather independent. Overall, for a fast assessment Sentinel 1 is the better of the two methods where multispectral and ground data are able to further refine the initial SAR-based assessment.

Assessment of tropical cyclone damage on dry forests using multispectral remote sensing: The case of Baja California Sur, Mexico

Tropical cyclones (TCs) are extreme weather events that frequently occur along the coast of northwestern Mexico, but an assessment of dry forest damage on this area after their impact had not been investigated yet. Thus, the effect of strong winds and high precipitation rates were analyzed for the first time on the dry forests of the southern tip of the Baja California peninsula. An analysis was performed on the development of the TC Odile (2014) with winds above 200 km/h and TC Lidia (2017) with high precipitation rates in excess of 400 mm/day. The study used remote sensing tools to calculate changes in land areas from the vegetation indices and the non-photosynthetic vegetation (NPV). The results showed a noticeable drop in the enhanced vegetation index (ΔEVI = 0.15) few days after Odile's landfall; after Lidia, the EVI values rose while the spatial distribution of changes on the normalized difference vegetation index (NDVI) and NPV showed the effect of large precipitation rates on vegetation damage. Intense winds from Odile triggered large changes in NDVI with an increase of NPV. This study provides insight into the sensitivity of this ecosystem to strong winds and heavy rainfall on the magnitude of vegetation damage.

Aboveground Biomass Allometric Models for Evergreen Broad-Leaved Forest Damaged by a Serious Ice Storm in Southern China

A catastrophic ice storm occurred in the spring of 2008, which severely destroyed nearly 13% of China’s forests; among them, the broad-leaved forest suffered the most extensive damage. In this study, allometric models of the evergreen broad-leaved forests damaged at different recovery stages after the disaster were established to estimate the aboveground biomass of damaged trees. Plant plots were established and surveyed in damaged forests to determine species composition and diameter distribution, and finally a sample scheme was formulated that contained 47 trees from 13 species. The destructive measurements of aboveground biomass of trees selected according to the scheme were conducted in 2008, 2010, 2012 and 2016, respectively. Undamaged trees in the same region were also selected to measure the biomass in 2010. Linear regression of logarithmic transformation of the power function form was performed using Diameter at Breast Height (DBH) as predictor to develop biomass allometric models. The results showed that the ice storm caused tree aboveground biomass loss, which caused different parameters of the tree biomass models at different recovery stages. The models have a high accuracy in predicting trunk and total aboveground biomass, with high determination coefficients (R2, 0.913~0.984, mean 0.957), and have a relatively low accuracy in predicting the biomass of branches and leaves (R2, 0.703~0.892, mean 0.784). The aboveground biomass reduced by 35.0% on average due to the ice storm, and recovered to the same level of undamaged trees in the same diameter 8 years after the disturbance. The branches and leaves recovered very fast, and the biomass of these parts exceeded that of the undamaged trees, reaching the same diameter 2 years after the disaster, indicating an over compensatory growth. The trees with a smaller diameter were mostly composed of middle and late succession species, and recovered faster than other species, indicating that the ice storm may alter the forest structure and accelerate community succession. The biomass allometric models built in this study, combined with forest inventory data, can estimate forest biomass loss and recovery after disturbance, and offer an important sense of the assessment of forest damage and the formulation of forest post-disaster management strategies.

Assessment of mangrove forest damage and its recovery in Banda Aceh city post-tsunami disaster

The Indian Ocean Tsunami on 26th December 2004, had caused devastation on land and coastal ecosystems. One of the worst affected ecosystems is the mangrove forest. Many areas of mangrove forest have been destroyed, and it may pose a long-term impact for the region, both in terms of forest and biodiversity conservation and in terms of the ability of the ecosystem to support the livelihoods of the coastal communities. The tragedy of tsunami has requested the rationale for conserving and sustainably managing natural ecosystems among the researchers and scientific community. The research was carried out to make a post-tsunami damage assessment and recovery of mangrove forest in Banda Aceh city area. Several high-resolution satellite imageries data have been effectively used to detect, assess and monitor the changes of mangrove forests in the pre and post-tsunami period using multi-temporal optical satellite data. An on-screen visual interpretation was made to identify the heterogeneous patches for the preliminary classification of fieldwork. A supervised classification was performed to obtain the pre and post Tsunami assessment maps. Accuracy assessment of the classified maps was performed on pixel-level using ground truth. The result highlighted the changes in the spatial extent of the mangrove forests in the study area as a result of Tsunami disaster. The latest condition of the mangrove forest recovery is about 90.3% of the total damage area.

Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany. . It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. 109 k m 2 forest area with RGB and NIR bands and 0.2-m spatial resolution. Around 10 7 pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was 92 % . During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management.

A hyperspectral GA-PLSR model for prediction of pine wilt disease

Pine wilt disease caused by a forest-invasive alien species, the pine wood nematode (Bursaphelenchus xylophilus) is considered as one of the most destructive pest problems. In recent years, spectroscopic technologies have shown great potentials for the assessment of forest damage due to their nondestructive, noninvasive, cost-effective, and rapidly responsive nature. This paper first identified the hyperspectral characteristics of pine wilt disease by measuring and analyzing the changes in spectral reflectance of healthy and infected Pinus massoniana trees. Then 16 spectral features were extracted from the spectral bands covering the green region (510~580 nm), the red region (620~680 nm), the red edge (680~760 nm), the near-infrared region (780~1100 nm), and coded as genes composing the chromosome of a genetic algorithm (GA). Based on the optimal spectral features with suitable fitness from the GA, a partial least squares regression (PLSR) prediction model was built with highest determination coefficient R2c = 0.91, R2v = 0.82, relative prediction deviation RPD = 3.3 and lowest root mean square error RMSEc = 0.23, RMSEv = 0.33 on the calibration and validation datasets. Compared with other PLSR models, our proposed GA-based approach significantly improves the prediction accuracy with few input spectral features.

Forest damage assessment specifics

The article deals with the issue of forming a suitable approach for assessing the damage caused by a forest fire in accordance with the Russian Federation Forest Code regulations. The subject of the research is the damage that can be caused by a forest fire. The existing methods of calculating the damage caused by forest fires have been analyzed and so has been the regulatory legal framework in the field of assessing the damage caused to forests by forest fires. None of the existing methodologies fully meets the provisions of the Russian Federation Forest Code Articles 100 and 95. The necessity of a conceptual approach to create a methodology for assessing the potential damage by a forest fire justifies the research rationale. The result of the study is the basis for the formation of a scientifically-based methodology for calculating the damage caused to forests, which is the monetary terms of damage.

Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing

The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer (MODIS) 13Q1 products are used, which include two vegetation indices data of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Furtherly, after Quality Screening (QS) and Savizky-Golay (S-G) filtering of MODIS 13Q1 data, four evaluation indices are obtained, which are NDVI with QS (QSNDVI), EVI with QS (QSEVI), NDVI with S-G filtering (SGNDVI) and EVI with S-G filtering (SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.

Examining the Multi-Seasonal Consistency of Individual Tree Segmentation on Deciduous Stands Using Digital Aerial Photogrammetry (DAP) and Unmanned Aerial Systems (UAS)

Digital aerial photogrammetric (DAP) techniques applied to unmanned aerial system (UAS) acquired imagery have the potential to offer timely and affordable data for monitoring and updating forest inventories. Development of methods for individual tree crown detection (ITCD) and delineation enables the development of individual tree-based, rather than stand based inventories, which are important for harvesting operations, biomass and carbon stock estimations, forest damage assessment, and forest monitoring in mixed species stands. To achieve these inventory goals, consistent and robust DAP estimates are required over time. Currently, the influence of seasonal changes in deciduous tree structure on the consistency of DAP point clouds, from which tree-based inventories can be derived, is unknown. In this study, we investigate the influence of the timing of DAP acquisition on ITCD accuracies and estimation of tree attributes for a deciduous-dominated forest stand in New Brunswick, Canada. UAS imagery was acquired five times between June and September 2017 over the same stand and consistently processed into DAP point clouds. Airborne laser scanning (ALS) data, acquired the same year, was used to reconstruct a digital terrain model (DTM) and served as a reference for UAS-DAP-based ITCD. Marker-controlled watershed segmentation (MCWS) was used to delineate individual tree crowns. Accuracy index percentages between 55% (July 25) and 77.1% (September 22) were achieved. Omission errors were found to be relatively high for the first three DAP acquisitions (June 7, July 5, and July 25) and decreased gradually thereafter. The commission error was relatively high on July 25. Point cloud metrics were found to be predominantly consistent over the 4-month period, however, estimated tree heights gradually decreased over time, suggesting a trade-off between ITCD accuracies and measured tree heights. Our findings provide insight into the potential influence of seasonality on DAP-ITCD approaches to derive individual tree inventories.

Forest Cover Change Analysis Using Remote Sensing Techniques in Madhupur Sal Forest of Bangladesh

Deforestation, degradation, damages, transformation and over exploitation of forests are the common problem in different parts of the world. Timely monitoring and assessment of forest resources may help to address and identify the above mentioned problems and thus proper guidance may be given the forest resources manager for rational planning and management of forests. Apart from the conventional methods of forest monitoring, remote sensing with its unique capability of synoptic viewing, real time and repetitive nature offers a potential tool for monitoring and evaluation of forest resources and hence remote sensing technology has been successfully used in various studies like forest inventory, monitoring of forest cover changes and forest damage assessment. In the present research forest cover change analysis in ‘Madhupur Sal Forest’ located in central part of Bangladesh has been investigated using satellite remote sensing data and spatial analysis. Transformation of ‘Sal forest’ to other landuse has been studied using the Landsat MSS (Multi Spectral Scanner) data of 1973 and Landsat 8 OLI (Operational Land Imager) data of 2015. Driving forces behind the transformation of ‘Sal forest’ has also been investigated through GPS (Global Positioning System) based ground verification and interview with the people living in the locality.J. Environ. Sci. & Natural Resources, 10(2): 73-78 2017

Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

ABSTRACTIn early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I1, I2), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km2, with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year−1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.

Assessment of Forest Damage in Croatia Caused by Natural Hazards in 2014

Background and Purpose: Recent natural disasters (ice-breaks, torrents, floods) that affected five Croatian counties caused significant damage on forest stands and forest infrastructure. Since in Croatia there is no common methodology for fast and reliable assessment of forest damage, the aim of this paper is to develop and present methodology for rapid damage assessment and to prescribe urgent recovery measures, as well as to provide first preliminary results of the total damage to forests. Materials and Methods: An attempt was made to develop the methodology which would rely on existing legislations, regulations, instructions and experiences for forest damage assessment as much as possible. Estimation of forest damage was based on field observations, spatial data of forest management units and data from the existing Forest management plans. Results: According to conducted assessment, forests of Primorsko-goranska County are the most affected by the overall damage caused by ice- storm. Major damages occurred both on the forest and on forest roads. Ice-storm also caused serious damages on forests and on forest roads in Licko- senjska County and Karlovacka County, while floods and torrents caused damages on forest roads in Zagrebacka County, Sisacko-moslavacka County and Karlovacka County. Money-wise, the total forest damage amounts 942 252 183 €. Conclusions: Methodology for rapid damage assessment presented in this paper resulted on first preliminary estimate of range, intensity and cost of forest damage caused by recent natural disturbances. More precise data on forest damage will be known after the implementation of the proposed emergency recovery measures. Also, certain improvements of methodology in terms of precision and collection of data may be achieved by incorporating remote sensing methods.

Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale

The remote-sensing technique is a cost-effective tool for monitoring large-scale forest damage sustained by typhoon events. Taking Cangnan County as the study area, this study aimed to extract the spatial pattern of damaged forest and determine the influencing factors of Typhoon Saomai in 2006, using Landsat Enhanced Thematic Mapper Plus (ETM+) data before and after the typhoon event. The results showed that 183 km2 of forest land were damaged by Typhoon Saomai. There was no obvious diverse influence on forest damage within 25 km of Saomai’s path, after that the area of damaged forest decreased rapidly. For the land uses of construction, crop, and grass, decrease in normalized difference vegetation index was considerable under 100 m elevation and the number of damaged forest pixels showed positive correlation with vegetation aggregation, because trees standing in isolation, alongside roads, or in small groupings were easily damaged. For forest land, the number of damaged forest pixels decreased with higher elevation and relative aspect; when the relative aspect was in the range 0–40°, the number of damaged forest pixels was highest. Considering the interactive effects of these factors on forest damage caused by the typhoon, vegetation aggregation had the strongest influence followed by elevation, land use, relative aspect, and distance from the typhoon’s path.

Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

Abstract: This paper introduces a method for rapid forest damage assessment using satellite images and stand-wise forest inventory data. Two Landsat 5 Thematic Mapper (TM) images from June and September 2010 and data from a forest stand register developed within the frameworks of conventional stand-wise forest inventories in Lithuania were used to assess the forest damage caused by wind storms that occurred on August 8, 2010. Satellite images were geometrically and radiometrically corrected. The percentage of damage in terms of wind-fallen or broken tree volume was then predicted for each forest compartment within the zone potentially affected by the wind storm, using the non-parametric k-nearest neighbor technique. Satellite imagery-based difference images and general forest stand characteristics from the stand register were used as the auxiliary data sets for prediction. All auxiliary data were available from existing databases, and therefore did not involve any added data acquisition costs. Simultaneously, aerial photography of the area damaged by the wind storm was carried-out and color infrared (CIR) orthophotos with a resolution of 0.5 x 0.5 m were produced. A precise manual interpretation of the effects of the wind storm was used to validate satellite image-based estimates. The total wind damaged volume in pine dominating forest (~1.180.000 m3) was underestimated by 2.2%, in predominantly spruce stands (~233.000 m3) by 2.6% and in predominantly deciduous stands (~195.000 m3) by 4.2%, compared to validation data. The overall accuracy of identification of wind-damaged areas was around 95-98%, based solely on difference data from satellite images gathered on two dates.

基于MODIS/NDVI时间序列的森林灾害快速评估方法——以贵州省为例

Snow disasters are one of the major natural disturbances to forest ecosystems in China.The increased frequency and severity of forest disturbances in recent years requires rapid and accurate regional forest damage assessment to support post-disturbance forest management,hazardous fuel management,post-hazard relief activities,and government compensation claims. Interpretation of MODIS/NDVIs to construct Guizhou Province′s remote sensing images time series between 2005 and 2008 was involved in this analysis.Specifically,the mean value of usefulness index was applied to identify key MODIS/NDVI images and the Savitzky-Golay filter was used to reconstruct key images first.Then,the ratio of forest pixels NDVI value(R2005—2007) was computed before and after the corresponding period of snow disaster in 2008,the respective mean and mean absolute of R2005—2007 were derived to determine undisturbed forest pixels and forest damage threshold(DT).Ultimately,with the support of remote sensing and geographical information system,disaster areas and damage ranks were identified by using this method.Light,moderate and severe damage were classified in the light of the R2007-2008 of forest damage pixels at county level.Further,in conjunction with field surveys of forest resources implemented by the Department of Forestry of Guizhou Province,the consistency between our derivations and field surveys was assessed. The preliminary results were as follows:(1) With the aid of usefulness index,MODIS/NDVI products captured on September 30(September 30 2005,September 30 2006 and September 30 2007) and May 9(May 9 2006,May 9 2007 and May 9 2008) were respectively chosen as key images corresponding to the period of the snow disasters,which had the lower mean value of usefulness index(2.06 and 2.41,respectively) form 2005 to 2008.After reconstructing the chosen MODIS/NDVI products using the Savitzky-Golay filter at the specified parameters(No.of the envelope iterations:3,adaption strength:2 and the window size:5),the mean NDVI value of six key images increased by about 0.04.(2) Based on statistical analysis,the 2005—2007 and δall(damage threshold)were estimated at 0.044 and 0.048 respectively without large-scale natural disturbance observed during September 30 and May 9,but there was distinct shift amplitude(0.041) between μ2005—2007 and μ2007-2008 after snow disasters.According to forest damage threshold,damaged pixels accounted for 28.6% of the total forest pixels,which was above the result based on subcompartment investigation(17.7%).(3) The snow disasters in the southeast and northeast Guizhou Province were the worst,containing Autonomous Prefectures of South Guizhou Province,Autonomous Prefectures of Southeast Guizhou Province,Tongren City,etc.And then,in terms of different damage levels,11 Severe damage counties(Dejiang,Yanhe,Duyun,etc) and 10 moderate damage counties(Meitan,Rongjiang,Tongzi,etc) were determined at county level.The derived forest damage maps due to disasters from the current methods were in consistency with field surveys in some extent,with a kappa coefficient of 0.86(above 0.75) derived from a statistical test. The adopted analytical flow for assessing forest losses due to snow disasters in Guizhou Province was based on remote sensing and geographical information systems operations.This method also provides a new idea for quick assessment on forest disasters at a regional scale,without relying on ground inventory or sampling.

Post-hurricane forest damage assessment using satellite remote sensing

This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was identified as the optimal damage indicator among these vegetation indices. An approach for detecting forest damage at a regional scale, without relying on ground inventory or sampling, was designed and validated. The validation showed that the relative change of pre- and post-hurricane NDII was linearly related to the damage severity estimated by the ground inventory with the coefficient of determination 0.79. This approach was applied to evaluate forest damage severity and the impacted region caused by Hurricane Katrina.