Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework

Varun Mithal, Guruprasad Nayak, Ankush Khandelwal, Vipin Kumar, Ramakrishna Nemani, Nikunj Oza
2018 Remote Sensing  
This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the
more » ... ild fires in the tropics. This framework has been used to build burned area maps from MODIS surface reflectance data as features and Active Fire hotspots as training labels that are known to have high commission and omission errors due to the prevalence of cloud cover and smoke, especially in the tropics. Using the RAPT framework we report burned areas for 16 MODIS tiles from 2001 to 2014. The total burned area detected in the tropical forests of South America and South-east Asia during these years is 2,071,378 MODIS (500 m) pixels (approximately 520 K sq. km.), which is almost three times compared to the estimates from collection 5 MODIS MCD64A1 (783,468 MODIS pixels). An evaluation using Landsat-based reference burned area maps indicates that our product has an average user's accuracy of 53% and producer's accuracy of 55% while collection 5 MCD64A1 burned area product has an average user's accuracy of 61% and producer's accuracy of 27%. Our analysis also indicates that the two products can be complimentary and a combination of the two approaches is likely to provide a more comprehensive assessment of tropical fires. Finally, we have created a publicly accessible web-based viewer that helps the community to visualize the burned area maps produced using RAPT and examine various validation sources corresponding to every detected MODIS pixel. an increase in demand for automated and reliable tools to monitor forest fires from earth observing satellite data [4] . Existing satellite-based techniques for burn area assessment can be grouped into two broad categories-active fire (hotspot) detection and post-fire burned area mapping. Hotspot detection approaches use thermal energy associated with burning of biomass to map active (ongoing) fires with the purpose of real-time fire management. A number of papers have used active fire data as a proxy to report the burned area estimates [5] [6] [7] . However, hotspot detection methods are known to have a high omission error rate because they tend to miss burned pixels due to obstruction by clouds and smoke as well as due to limited satellite diurnal sampling (i.e., satellite overpass occurred when the fires were not burning) [8] . Moreover, active fire often overestimates burned area in regions with a large proportion of small, sub-pixel fires [9] . In contrast, post-fire burned area mapping techniques consider satellite observations of the land surface over a longer temporal interval around the burn date to create more reliable historical maps of burned areas [8, [10] [11] [12] [13] [14] . Note that post-fire mapping techniques are relatively more robust to issues due to cloud cover or smoke from fires because often burn scars remain detectable in the spectral observations for several months after the burn date. This paper presents a new post-fire burned area product that is particularly relevant for tropical forests where both of these issues are very common. Satellite-based post-fire burned area mapping algorithms face two key challenges. First, the relationship between the explanatory variables (spectral features) and target variable (burned/unburned) changes with spatial regions and land cover [8] . Therefore, training a single classification model to distinguish burned pixels from unburned pixels and applying it across different land cover classes and geographies can result in poor performance. To address this issue, existing approaches train separate customized models for each land cover and spatial region. However, this requires annotated training samples in each land cover and geographical region, which is infeasible due to the considerable human effort involved in collecting training samples using ground and aerial surveys. To address the challenge of lack of annotated training data for different spatial regions and land classes, previous approaches such as [8, 9] have used Active Fire hotspots for selecting training samples. However, active fire hotspots are only imperfect surrogates for burned areas; therefore, these previous studies also used hand-crafted "cleaning" rules while selecting training pixels from active fire hotspots to ensure that the training samples are accurate. While such "cleaning heuristics" are helpful in improving the performance of classification models, the spectral diversity of burned pixels can make such "cleaning heuristics" very brittle in some regions and land classes. The second challenge is that the unburned locations considerably outnumber the burned locations. Hence even with a small false positive rate, a traditional classification algorithm can result in a significant number of spurious burned areas. This issue has been addressed by previous approaches using the concept of identifying confident burns by using co-occurrence of active fire hotspots and burn scars in spectral observations [8] . Moreover, since landcover changes can occur due to other reasons such as logging, requiring the presence of active fire hotspots ensure that landcover changes other than fire are excluded. This paper presents an application of a novel machine-learning framework to map burned areas over tropical forests of South America and South-east Asia. This 3-stage framework RAPT (RAre Class Prediction in the absence of True labels) [15] is able to build data adaptive classification models using noisy training labels (See Section 3.1 for details). The RAPT framework was used to build a burned area product derived from MODIS Surface Reflectance 8-day composite product MOD09A1 [16] and MODIS active fire product MOD14A2 [17] for the tropical forests in Amazon and South-east Asia. An evaluation was performed using Landsat-based reference burned area maps that were constructed by following the procedure in [18] (see Section 3.2 for details). Finally, we have created a publicly accessible web-based viewer for visualizing our burned area product http://z.umn.edu/fireviewer/. The viewer shows the RAPT events corresponding to a user-selected MODIS tile and year as event polygons. For each polygon, the Landsat composites
doi:10.3390/rs10010069 fatcat:2h52rqxflnax5cpguquckumfaq