1. Introduction
Wildfire related risk and damage have increased over the past three decades [
1], causing threats to ecosystem function and human society. Currently, global warming and extreme droughts are contributing to increased wildfire activities [
2]. Increased wildfires in return cause more warming effects through quick release of carbon sequestered in terrestrial ecosystems [
3]. Wildfires play important roles in ecosystem succession, biogeochemical cycles and climate change. Thus, accurately mapping burned areas, serving as the basic step for wildfire management, is crucial for the analysis of carbon emissions and fire risk as well as understanding the effects of climate change on ecosystems.
Remotely sensed images have been used for mapping burned areas for decades [
4,
5]. Fire-induced changes such as vegetation removal, structure alteration and charcoal deposition all cause spectral shift that enable multispectral remote sensing techniques to be applied. To be specific, notable responses are observed in Near infrared (NIR) (decrease) and shortwave infrared (SWIR) bands (increase) [
6]. Classification techniques like logistic regression [
7] and object-based classification [
8] have been proven to be effective in capturing spectral features of burned regions. Spectral indices like Normalized Burn Ratio (NBR) index [
9] and differenced Normalized Burn Ratio (dNBR) index [
10] are also commonly used to create burned maps. With improved availability of multitemporal remote sensing imagery, burned areas have also been mapped using time series analysis. Hawbaker et al. [
11] produced long term burn area products in the conterminous United States with Landsat images. Roteta et al. [
12] also used time series of Sentinel−2 A images to map burned areas in sub-Saharan Africa and the accuracy was claimed to be higher than that of the MODIS global fire product (MCD64 A1). However, remote sensing images are limited in capturing the three-dimensional (3D) vegetation structure, which makes it difficult to obtain under canopy fire information and canopy height measurement [
13]. To provide more accurate estimation of fire severity, terrestrial carbon storage and biomass emissions, methods based on 3D data are essential to complement existing image-based approaches [
14,
15].
Light detection and ranging (LiDAR) offers an effective way of producing 3D structures, which has previously been applied for mapping burned areas and forest fuel types [
16,
17]. By measuring the time interval of transmitted and received energy, LiDAR can calculate the distance between LiDAR sensor and targets, providing information on the three-dimensional structure of vegetated ecosystems [
18]. Thus, LiDAR can be used to monitor structure changes of vegetation caused by fires. Wang et al. [
19] mapped burned areas based on height differences of sagebrush derived from pre- and post-fire airborne LiDAR data and classified the whole region into three levels of severity using height thresholds. Montealegre et al. [
17] applied a logistic regression model to classify burned areas in Spanish forests based on post-fire airborne LiDAR data. Other studies, e.g., Garcia et al. [
16] integrated post-fire airborne LiDAR data and Landsat images to map burned areas and estimate consumed biomass with high accuracy. While using airborne LiDAR data provides detailed vegetation structure with high accuracy, the application of these data over large areas is constrained by the high expense associated with data acquisition.
At regional and global scales, spaceborne LiDAR has been instrumental in capturing 3D data globally and efficiently with fixed footprint and revisit time, providing large coverage and repeatable observations [
20,
21]. A notable spaceborne LiDAR mission includes National Aeronautics and Space Administration’s (NASA) first Ice, Cloud, and land Elevation Satellite (ICESat) mission, which carried the Geoscience Laser Altimeter System (GLAS), which is a waveform LiDAR instrument, launched in January 2003. GLAS is the first spaceborne LiDAR sensor with the aim of measuring ice sheets globally [
22]. The footprint of GLAS beams is 70 m and the along-track sampling distance is 172 m with a wavelength of 1064 nm [
20]. GLAS records backward energy with waveform LiDAR measurement for each footprint. This mission stopped collecting data in October 2009, after providing billions of LiDAR waveform data for the analysis of ice, cloud and vegetation [
16,
23]. These waveform data were also employed to evaluate fire disturbance in Alaska forests [
24], where structure changes of forests between burned and unburned areas were found to be significant. Moreover, García et al. [
25] used GLAS data to characterize canopy fuels. However, the ICESat footprint, though unprecedented at the time, is quite coarse (70 m) which limited the resolution of finer 3D details.
As a follow-up mission to ICESat, ICESat−2 was launched on September 15 2018 with the Advanced Topographic Laser Altimeter System (ATLAS) [
21,
26]. ATLAS is a photon counting LiDAR with a footprint of 14 m and along-track sampling distance of 0.7 m, which presents a tremendous improvement in sampling compared to its predecessor. The ATLAS instrument measures the time a photon takes to travel from ATLAS to Earth and back so as to determine the photon’s geodetic latitude and longitude. Unlike single waveform beams in GLAS, ATLAS emits three pairs of beams with a wavelength of 532 nm. Each pair consists of a strong beam and weak beam using a transmit energy ratio of 4:1. The improved spatial resolution and coverage of ICESat−2 will better assist the mapping of ice and vegetation. One of ICESat−2’s data products is the Land and Vegetation height product (ATL08), which provides terrain and canopy height measurement at 100 m segments along the ground track. The ATL08 product provides various canopy and terrain related metrics such as mean canopy height, max canopy height, apparent surface reflectance, the number of canopy photons, the number of terrain photon and canopy openness in each segment. ATL08 also provides cloud masks to help clean and filter the data. This standard canopy product will facilitate forests assessment at global scales and promote carbon monitoring. In this study, ATL08 data are used to map burned areas, which, based on current literature, is the first attempt of using such data for this purpose.
Machine learning has shown great success in classification and discrimination of remote sensing data [
27,
28]. With the capability to model complex class signatures without statistical assumptions on data distribution (non-parametric) and the ability to process high-dimensional data, machine learning approaches are widely accepted [
29]. Moreover, machine learning algorithms are more robust and produce higher classification accuracy than traditional parametric classifiers such as maximum likelihood [
27,
29]. Machine learning methods such as random forest [
30], gradient boosted regression [
11] and neural network have found application in various studies. Wu et al. [
31] evaluated support vector machine, Random Forest and decision tree to classify point clouds to obtain canola canopy structures, concluding that Random Forest provides better results. Krishna Moorthy et al. [
32] classified liana stems from point clouds by Random Forest with an accuracy of 88%. For this study, we used Random Forest to classify burned 100 m ATL08 photon segments and logistic regression is also included for comparison.
In this study, we investigated the application of ICESat−2 photon counting data for burned area mapping, which is the first attempt to employ spaceborne LiDAR in fire classification. Innovative aspects of this research include the use of machine learning methods with photon counting data to provide three-dimensional structural information along the satellite tracks. Optical images derived burn maps were used as references. Moreover, land cover maps were used to avoid interference of different land covers. The main goal was to investigate the feasibility of using ATL08 to map burned areas of wildfires. Our specific objectives were: (1) to develop a methodology for using ATL08 data for burned areas classification; (2) to compare the effects of Sentinel−2 and Landsat 8 images when used as reference images for identifying burned areas; (3) to compare the accuracy of different classification methods for ATL08 100 m-segments, like Random Forest and logistic regression; (4) to identify the most significant variables in ATL08 for classification of burned areas.
4. Discussion
4.1. Comparison of Sentinel−2 and Landsat 8
In this study, 24 LiDAR metrics were utilized as predictors for burned ATL08 segments classification. The classification accuracy reached 83%, which shows the feasibility of using ATL08 data for burn area mapping. This study suggests that it is possible to employ spaceborne LiDAR data in fire disturbances monitoring.
Optical images were used to provide a reference burn map and landcover map in previous experiments. The images were collected with the same spectral bands, from Blue band to SWIR2 band (
Table 2). Given their different spatial resolution, we wanted to investigate the effect of using Landsat 8 and Sentinel−2 images on ICESat−2 segments classification. Using a Sentinel−2 derived forest map and burn map, we got 592 forest segments (including burned forest and unburned forest) from two study sites. For Landsat 8 data, there were 744 forest segments in total. Therefore, Landsat 8 data introduced many false forest segments compared with Sentinel−2 data. This phenomenon can partly explain why Landsat 8 derived segments have slightly lower classification accuracy. In
Table 4, user’s accuracy and producer’s accuracy of burned segments are 81.54% and 74.65% to Sentinel−2 derived testing samples, 79.79% and 68.81% to Landsat 8 derived samples. Overall accuracies of testing samples using Random Forest are 83.15% and 76.23% for Sentinel−2 derived segments and Landsat 8 derived ones, respectively. These results demonstrate that Landsat 8 derived segments have slightly lower classification accuracy than that of Sentinel−2 derived segments. However, the accuracy of these two datasets are comparable in fire mapping, 83.15% versus 76.23%.
4.2. Comparison of Classification Methods
From
Table 4 and
Table 5, Random Forest performs better than logistics regression in ATL08 segments classification. Random Forest classification produces higher overall accuracy and kappa than that of logistic regression. Moreover, the user’s and producer’s accuracy of burned segments are also higher in Random Forest. Therefore, it can be seen that with the same training samples and testing samples, Random Forest can get better classification results. These benefits can be attributed to the capability of data processing without statistical assumptions (e.g., parametric). However, Random Forest is a black box model, which means users do not have access to a model.
In logistic regression, the stepwise procedure removes those insignificant metrics and helps to simplify the model. Although the accuracy of logistic regression is a little bit lower, it is more efficient in practice to have a simple model with acceptable accuracy.
4.3. Comparison of LiDAR Metrics
Based on
Figure 8, asr (apparent surface reflectance) is the most important metric (15.65% and 24.25% in importance) in burned ATL08 segments classification, n_ca_photons (the number of canopy photons) ranking the second. Other metrics such as n_te_photons (the number of terrain photons), n_toc_photons (the number of top of canopy photons), toc_roughness (top of canopy roughness), RH60, canopy_relief (canopy relief ratio) and CV (coefficient of variation) are less important. It is reasonable that reflectance contributes to capturing vegetation characteristics and changes. In Zhao et al. [
40], intensity related metrics in airborne LiDAR explained 60% of variations in leaf area index, which also illustrates the importance of reflectance.
Other 23 LiDAR metrics count 84.35% and 75.57% in importance for Sentinel−2 based samples and Landsat 8 based samples, indicating the effectiveness of LiDAR data. To check the feasibility of using other LiDAR metrics in burn area classification, we removed asr and used the remaining 23 metrics to classify burned ATL08 segments, with 100% of the segments samples derived from Sentinel−2 data.
Table 6 shows the OOB error (22.80%) that is acceptable without asr.
In fact, reflectance of optical images (asr in ATL08) is the sum of reflected photons within a pixel from which we cannot get height information directly. However, LiDAR helps to record the height information of each photons and based on which photons are classified into canopy photons and ground photons in ATL08, producing n_ca_photons and n_te_photons. Other metrics such as toc_roughness, RH60, canopy_relief and CV are also characteristics describing the 3D canopy structure which contribute to wildfire classification.
4.4. Limitations
The high classification accuracy shown in
Table 4 reveals the feasibility of using ATL08 metrics for burned areas mapping along tracks. However, there are some limitations. First, it is unavoidable that there is time lag between the fire occurrence and acquisition of ICESat−2 data. We know the revisit time of ICESat−2 is 91 days currently. Therefore, the time lag between fire occurrence and ICESat−2 track will be around three months or two months. During the time lag, regrowth of forests after fire will reduce the difference between burned and unburned forests, causing more challenges in burned areas classification.
Moreover, logging or harvesting after fire will introduce damages to forest structure. This kind of change will cause interferences to fire mapping. At first, logging will produce debris and abandoned stems which might increase surface fuels. Second, large boles are removed and the density of canopy declines. Under this situation, data collected from ATL08 cannot represent structure changes in real fire events. However, our model still works as logging decreases canopy density and canopy height.
Moreover, the laser wavelength of the ICESat−2 ATLAS sensor is 532 nm, which makes it possible to be obstructed by clouds. In this case, no photons, or only very few photons, can reach the land surface when there are clouds. Therefore, only a small portion of the ICESat−2 data is suitable for land surface monitoring in the presence of clouds.