Data for: Predictive Modeling of Forest Fires: A New Dataset and Machine Learning Approach

This Dataset was created based on Remote Sensing data to predict the occurrence of wildfires, it contains Data related to the state of crops (NDVI: Normalized Difference Vegetation Index), meteorological conditions (LST: Land Surface Temperature) as well as the fire indicator “Thermal Anomalies”. All three parameters were collected from MODIS (Moderate Resolution Imaging Spectroradiometer), an instrument carried on board the Terra platform. The collected data went through several preprocessing techniques before building the final Dataset. The experimental Dataset is considered as a case study to illustrate what can be done at larger scales. The Data contains parameters with high influence of wildfires occurrence collected using remote sensing. The Dataset is composed of four columns, the first three columns are NDVI, LST, and Thermal Anomalies and the fourth column represents the corresponding class (fire or no_fire), the Dataset contains 804 rows: 386 instances of the class “fire” and 418 instances of the class “no fire” with 418 rows. Each row contains the collected data and its class. The data were downloaded from the official website of NASA's Land Processes Distributed Active Archive Center (LP DAAC), and then we preprocessed them using multiple preprocessing techniques to remove noises and correct inconsistencies, and finally extracting useful information. The study area is composed of multiple zones located in the center of Canada. The surface of this area is approximately 2 million hectares. These zones differ in their size, burn period, date of burn and extent. We have chosen to apply the experiment in a big region of Canada's forests because it is known for its high rate of wildfires and also for the availability of fire information (start and end fire date, cause of fire and the surface of the burned area in hectares), these information were acquired from The Canadian Wild-land Fire Information System (CWFIS) which creates daily fire weather and fire behavior maps year-round and hot spot maps throughout the forest fire season本数据集基于遥感数据构建，用于野火发生预测，涵盖作物状态数据（归一化差异植被指数（NDVI, Normalized Difference Vegetation Index））、气象条件数据（地表温度（LST, Land Surface Temperature））以及火情指标“热异常值”。上述三类参数均取自搭载于Terra平台的MODIS（中分辨率成像光谱仪，Moderate Resolution Imaging Spectroradiometer）。在构建最终数据集前，所采集的数据已通过多轮预处理技术完成清洗与优化。本实验数据集作为案例研究，用于展示更大尺度下可开展的相关研究工作。 本数据集包含经筛选的、对野火发生具有高影响力的遥感采集参数。数据集共包含四列数据：前三列为NDVI、LST与热异常值，第四列为对应类别标签（“野火发生”或“无野火发生”）。数据集总计804条样本，其中“野火发生”类别样本386条，“无野火发生”类别样本418条，每一行数据对应一组采集参数及其类别标签。 数据源自美国国家航空航天局（NASA）陆地过程分布式主动存档中心（LP DAAC, Land Processes Distributed Active Archive Center）官方网站，随后通过多种预处理技术进行去噪、不一致性校正，并提取有效信息。 本研究的研究区域位于加拿大中部的多个林区，总面积约200万公顷。这些区域在面积、燃烧周期、燃烧日期与过火范围上均存在差异。选择加拿大大片林区开展实验，一是由于该区域野火发生率极高，二是其野火相关信息（野火起止日期、起火原因及过火公顷数）公开可获取。此类信息取自加拿大荒野野火信息系统（CWFIS, Canadian Wild-land Fire Information System），该系统可全年每日生成火灾天气与火灾行为地图，并在林火季生成热点分布图。