摘要
A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central India.Identification of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS platform.The results from FR and AHP show similar trends.The FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR models.The results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.
A comparative study of Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) models are performed for forest fire risk (FFR) mapping in Melghat Tiger Reserve forest, central India. Identification of FFR depends on various hydrometeorological parameters (altitude, slope, aspect, topographic position index, normalized differential vegetation index, rainfall, air temperature, land surface temperature, wind speed, distance to settlements, and distance by road are integrated using a GIS platform. The results from FR and AHP show similar trends. The FR model was significantly higher accurate (overall accuracy of 81.3%, kappa statistic 0.78) than the AHP model (overall accuracy 79.3%, kappa statistic 0.75). The FR model total forest fire risk areas were classified into five classes: very low (7.1%), low (22.2%), moderate (32.3%), high (26.9%), and very high (11.5%). The AHP fire risk classes were very low (6.7%), low (21.7%), moderate (34.0%), high (26.7%), and very high (10.9%). Sensitivity analyses were performed for AHP and FR models. The results of the two different models are compared and justified concerning the forest fire sample points (Forest Survey of India) and burn images (2010-2016). These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.
