TY - GEN
T1 - Peat Depth Prediction System Using Long-Term MODIS Data and Random Forest Algorithm
T2 - 1st International Conference on Information System and Information Technology, ICISIT 2022
AU - Fadhurrahman, Muhammad
AU - Saputro, Adhi Harmoko
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Peatlands have an important role as global climate regulators because they store global amounts of carbon which, if degraded, will result in increased concentrations of greenhouse gases in the atmosphere. Peatland mapping using satellite imagery is considered effective for classifying a land cover area. Previous studies concluded that satellite imagery can be used to classify a peat area and a non-peat area. In this study, we use satellite imagery with a mounted MODIS sensor from 2015-2019 and calculate the index from MODIS bands. The Machine Learning (ML) method was used for generating a peat depth in Pulang Pisau, Kalimantan. Random Forest (RF), Support Vector Machine (SVM), Support Vector Regressor (SVR), Gradient Boosting (GB), and Ada Boost (AB) models were used to generate a peat depth map. The best performance was achieved by RF Classifier with accuracy 0.93 and RF Regressor with {R}^{2}=0.88
AB - Peatlands have an important role as global climate regulators because they store global amounts of carbon which, if degraded, will result in increased concentrations of greenhouse gases in the atmosphere. Peatland mapping using satellite imagery is considered effective for classifying a land cover area. Previous studies concluded that satellite imagery can be used to classify a peat area and a non-peat area. In this study, we use satellite imagery with a mounted MODIS sensor from 2015-2019 and calculate the index from MODIS bands. The Machine Learning (ML) method was used for generating a peat depth in Pulang Pisau, Kalimantan. Random Forest (RF), Support Vector Machine (SVM), Support Vector Regressor (SVR), Gradient Boosting (GB), and Ada Boost (AB) models were used to generate a peat depth map. The best performance was achieved by RF Classifier with accuracy 0.93 and RF Regressor with {R}^{2}=0.88
KW - Digital Mapping
KW - Machine Learning
KW - MODIS
KW - Peatlands
UR - http://www.scopus.com/inward/record.url?scp=85138695580&partnerID=8YFLogxK
U2 - 10.1109/ICISIT54091.2022.9872550
DO - 10.1109/ICISIT54091.2022.9872550
M3 - Conference contribution
AN - SCOPUS:85138695580
T3 - 2022 1st International Conference on Information System and Information Technology, ICISIT 2022
SP - 364
EP - 369
BT - 2022 1st International Conference on Information System and Information Technology, ICISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 July 2022 through 28 July 2022
ER -
