Drought Monitoring Methodology Based on AVHRR Images and SPOT Vegetation Maps

Many regions of the world are experiencing an increase in the frequency and intensity of droughts. The province of Fars, Iran, has faced particularly severe drought and ground water problems over the course of the last decade. However, previous research on the subject reveals a lack of useful information regarding droughts in this province. This paper presents a fast, efficient and reliable method that can be used to produce drought maps in which Advanced Very High Resolution Radiometer (AVHRR) images are processed and then compared with SPOT vegetation maps. Ten-day maximum Normalized Difference Vegetation Index (NDVI) maps were produced and vegetation drought indices such as the Vegetation Condition Index (VCI) were calculated. Furthermore, a Temperature Condition Index (TCI) was extracted from the thermal bands of AVHRR images in order to produce the Vegetation Health Index (VHI). Remotely sensed data was then compared with hydrological and meteorological data from 1998 to 2007. The Standardized Precipitation Index (SPI) was used to quantify the precipitation deficit while the Standard Water Level Index (SWI) was developed to assess the groundwater recharge deficit. Instead of correlation coefficients, spatial correlation through visual comparison was found to provide better and more meaningful pictures. The highest correlation values were obtained when VHI or Drought Severity Index (DSI) values were correlated with the current month’s SWI data. DSI maps showed strong vegetation conditions existing for the majority of the study period. For most counties in Fars, strong Pearson correlations observed between the DSI and the SWI of the same month reflect high rates of ground water consumption. The results of this study indicate that the proposed method is a potentially promising method for early drought awareness which can be used for drought risk management in semi-arid climates such as in Fars, Iran. This study also recommends that the Iranian government develop programs to help decrease the consumption of ground water resources in the province of Fars to ensure the long term sustainability of the watersheds in this province.

Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area

Because the removal of topographic effects is one the most important preprocessing steps when extracting information from satellite images in digital Earth applications,the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years.As there is no superior topographic correction method applicable to all areas and all images,a comparison of topographic normalization methods in different regions and images is necessary.In this study,common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area,and the results were evaluated through different criteria.The results show that the simple correction methods[Cosine,Sun-Canopy-sensor(SCS),and Minnaert correction]are inefficient in exceptionally rough forests.Among the improved correction methods(SCSC,modified Minnaert,and pixel-based Minnaert),the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used.Thus,this method should be considered for topographic correction,especially in forests with severe topography.

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.