caa a22 4500 467901988 CHVBK 20180406152821.0 cr unu---uuuuu 170328e20061201xx s 000 0 eng 10.1007/s10661-005-9122-4 doi (NATIONALLICENCE)springer-10.1007/s10661-005-9122-4 Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets [Elektronische Daten] [Krishna Vadrevu, Anuradha Eaturu, K. Badarinath] Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger' in the study area. Our results also identify potential ‘hotspots' of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when' forest fires will most likely occur. Springer Science+Business Media, Inc., 2005 fires nationallicence underlying causes nationallicence best subset-multiple regression nationallicence SPOT vegetation datasets nationallicence India nationallicence Vadrevu Krishna Agroecosystem Management Program, Ohio State University, Columbus, USA aut Eaturu Anuradha Department of Mathematics and Computer Science, Valdosta State University, Valdosta, USA aut Badarinath K. Forestry and Ecology Division, National Remote Sensing Agency, Hyderabad, India aut Environmental Monitoring and Assessment Kluwer Academic Publishers 123/1-3(2006-12-01), 75-96 0167-6369 123:1-3<75 2006 123 10661 https://doi.org/10.1007/s10661-005-9122-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10661-005-9122-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vadrevu Krishna Agroecosystem Management Program, Ohio State University, Columbus, USA aut NATIONALLICENCE 700 1- Eaturu Anuradha Department of Mathematics and Computer Science, Valdosta State University, Valdosta, USA aut NATIONALLICENCE 700 1- Badarinath K. Forestry and Ecology Division, National Remote Sensing Agency, Hyderabad, India aut NATIONALLICENCE 773 0- Environmental Monitoring and Assessment Kluwer Academic Publishers 123/1-3(2006-12-01), 75-96 0167-6369 123:1-3<75 2006 123 10661 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer