Abstract
The smoke from biomass burning on Kalimantan Island has caused severe environmental problems in Southeast Asia’s primary burning regions and surrounding regions due to the overspread haze. To monitor the biomass burning aerosol plumes on Kalimantan Island, the high-temporal-resolution Himawari-8 satellite data were used in this study. However, studies are limited on smoke detection using satellite remote sensing for Kalimantan Island because of the difficulty caused by frequently occurring clouds and the lack of prior knowledge on applying traditional threshold methods. In this study, we used the multilayer perceptron (MLP) method to identify smoke over Kalimantan Island in August 2015, one of the most severe fire seasons. To prepare sufficient supervision information, a pixel-level labeled dataset was established based on the Himawari-8 data. Based on the labeled dataset, three MLP approaches and two sampling methods were applied to create training samples. A comparison between the detection results for the MLP approaches and classification tree analysis (i.e., CTA) showed that MLP is superior to CTA. The visualization results also showed that the detected smoke areas included those mixed with clouds. Some detected smoke is difficult to identify by the human eye, suggesting that the explanatory dataset built for this study is sufficiently comprehensive. Therefore, the pixel-level labeled dataset and MLP are suitable for regions that are frequently cloud-covered.
Highlights
The multilayer perceptron (MLP)-BN was based on MLP, and the batch normalization layer (BN) [28] was added after each rectified linear unit function (ReLU) [29]
Verifying the model changes improves generalization ability; we compared the evaluation indexes of MLP-BN, MLP-BN-Dropout and MLP-S based on the stratified samples
The F1 score in Table 2 shows that MLP-BN-Dropout is the superior of the three model structures, followed by MLP-BN and MLP-S being the worst
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. According to spectral feature analysis [15] and visual analysis [12], optical remote sensing satellite images from the visible, near-infrared and infrared bands contain spectral information from both biomass-burning aerosols and other objects. Despite balancing computational efficiency and accuracy, these threshold methods require prior knowledge of specific bands and the thresholds among regions for smoke detection These methods are difficult to apply to other satellite datasets. Multilayer perceptrons (MLPs) [21] approximate a function from input to output through a neural network This algorithm type detects smoke pixels through one-dimensional spectral features that consider the spatial features of the smoke. Since there is no guarantee of classification accuracy for these label datasets, it is difficult to verify whether the machine learning algorithm can determine the accurate spectral characteristics of smoke. The remainder of this paper is structured as follows: Section 2 introduces the study region, dataset and methodology; Section 3 shows the detailed analysis and results; and Section 4 provides further discussion
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
