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

Abstract

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.

An Automated Method for Surface Ice/Snow Mapping Based on Objects and Pixels from Landsat Imagery in a Mountainous Region

“…For the non-snow category, the user accuracies and producer accuracies are all higher than 97.9%, which is mainly because the non-snow-covered areas are larger than the ice/snow-covered areas in each image scene. Taking Image A as an example, the number of non-snow pixels (39,558,086) is greater than that of snow pixels (15,893,225) according to Table 3, which makes the user's and producer's accuracies of non-snow greater than 98%. Compared to the non-snow category, the user's and producer's accuracies of snow have obvious differences, due to the smaller covered areas, which is consistent with Zhao's research [45].…”

“…Remote sensing has become an important source of information for analyzing and delivering data on changes in various natural resources, particularly surface ice/snow [11,12]. Examples of studies using remote sensing techniques for various applications in relation to surface ice/snow resources include estimating snow-covered areas, computing the snow depth/water equivalent, mapping the regional snow cover, assessing ice/snow disasters, and hydrological and climate modeling [13][14][15][16]. Surface ice/snow has a high reflectivity in the visible and near-infrared (NIR) bands and a strong absorption in the shortwave infrared (SWIR) band, which is the theoretical basis for snow-cover mapping in optical remote sensing.…”

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

“…This severe weather disaster was characterized by sudden onset, excessive precipitation and long duration. The snow, ice and sleet caused severe forest losses and destroyed 1.98 × 10 7 ha of forests, accounting for nearly 13% of China's forests [7][8][9]. Among them, the broad-leaved forests suffered the most extensive damage [10].…”

Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

“…For ecologists, satellite remote sensing has become a potential goldmine for monitoring and predicting changes in vegetation activities over large regions in a repeatable manner [6,43,44], which is also widely used by climatologists [24][25][26][45][46][47] and researchers in natural disasters [48][49][50]. Normalized Difference Vegetation Index (NDVI), as an effective indicator to monitor vegetation and natural environment at regional and global scales, has been widely used in the research on vegetation activity [51][52][53].…”