Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data
Tóm tắt
Dead fine fuel moisture content (DFFMC) is a key factor affecting the spread of forest fires, which plays an important role in evaluation of forest fire risk. In order to achieve high-precision real-time measurement of DFFMC, this study established a long short-term memory (LSTM) network based on particle swarm optimization (PSO) algorithm as a measurement model. A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed. The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set. The surface fine dead fuel of Mongolian oak (Quercus mongolica Fisch. ex Ledeb.), white birch (Betula platyphylla Suk.), larch (Larix gmelinii (Rupr.) Kuzen.), and Manchurian walnut (Juglans mandshurica Maxim.) in the maoershan experimental forest farm of the Northeast Forestry University were investigated. We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy, meteorological, and spectral meteorological fusion methods. The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1% for Mongolian oak, 1.3% for white birch, 1.4% for larch, and 1.8% for Manchurian walnut, and these values were lower than those of the near-infrared method and the meteorological method. The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.
Tài liệu tham khảo
Bai TC, Tao W, Chen YQ (2019) Comparison of near-infrared spectrum pretreatment methods for Jujube leaf moisture content detection in the sand and dust area of Southern Xinjiang. Spectrosc Spectr Analysis 39(4):1323–1328 (in Chinese)
Bilgili E, Coskuner KA, Usta Y, Goltas M (2019) Modeling surface fuels moisture content in Pinus brutia stands. J For Res 30(2):577–587
Brown TP, Inbar A, Duff TJ, Lane PN, Sheridan GJ (2022) The sensitivity of fuel moisture to forest structure effects on microclimate. Agric For Meteorol 316(108857):1–15
Byram GM, Jemison GM (1943) Solar radiation and forest fuel moisture. J Agric Res 67(4):149–176
Cawson JG, Nyman P, Schunk C, Sheridan GJ, Duff TJ, Gibos K, Bovill WD, Conedera M, Pezzatti GB, Menzel A (2020) Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide. Int J Wildland Fire 29(6):548–559
Ellis TM, Bowman DM, Jain P, Flannigan MD, Williamson GJ (2022) Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob Chang Biol 28(4):1544–1559
Hiers JK, Stauhammer CL, O’brien JJ, Gholz HL, Martin TA, Hom J, Starr G, (2019) Fine dead fuel moisture shows complex lagged responses to environmental conditions in a saw palmetto (Serenoa repens) flatwoods. Agric For Meteorol 266–267:20–28
Hu HQ, Lu X, Sun L, Guan D (2016) Dynamics and prediction models of ground surface dead fuel moisture content for typical stands in Great Xing’an Mountains, Northeast China. Chin J Appl Ecol 27(7):2212–2224 (in Chinese)
Hu HQ, Luo BZ, Luo SS, Sun L (2019) Water content of surface ground fuel in Larix gmelinii-Betula platyphylla mixed forest of Nanwenhe, Daxing’an Mountains. Chin J Ecol 38(5):1314–1321 (in Chinese)
Jia JP, He XQ, Jin YJ (2009) Statistics (4th edition). China Renmin University Press, Beijing p 374. (in Chinese)
Lee HT, Won M, Yoon S, Jang K (2020) Estimation of 10-hour fuel moisture content using meteorological data: amodel inter-comparison study. Forests 11(982):1–19
Lei WD, Yu Y, Li XH, Xing J (2022) Estimating dead fine fuel moisture content of forest surface, based on wireless sensor network and back-propagation neural network. Int J Wildland Fire 31(4):369–378
Li X, Sun ZQ, Lu S, Omasa K (2021) A multi-angular invariant spectral index for the estimation of leaf water content across a wide range of plant species in different growth stages. Remote Sens Environ 253(112230):1–19
Liu JB, Sun P, Sun L (2018) Study on moisture content prediction model of surface fuels in principal stands, Kunming. J Central South University For Technol 38(5):53–58 (in Chinese)
Maffei C, Lindenbergh R, Menenti M (2021) Combining multi-spectral and thermal remote sensing to predict forest fire characteristics. ISPRS J Photogramm Remote Sens 181(2021):400–412
Man ZY, Hu HQ, Zhang YL, Liu FC, Li Y (2019) Dynamic change and prediction model of moisture content of surface fuel in Maoer Mountain of northeastern China. J Beijing For Univ 41(3):49–57 (in Chinese)
Masinda MM, Li F, Liu Q, Sun L, Hu TX (2021) Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain, Northeast China. J For Res 32(5):2023–2035
Miller EA (2018) Moisture sorption models for fuel beds of standing dead grass in Alaska. Fire 2(2):1–18
Ni C, Zhang Y, Wang D (2018) Moisture content quantization of Masson pine seedling leaf based on stacked autoencoder with near-infrared spectroscopy. J Electr Comput Eng 8696202:1–8
Nolan RH, Foster B, Griebel A, Choat B, Medlyn BE, Yebra M, Younes N, Boer MM (2022) Drought-related leaf functional traits control spatial and temporal dynamics of live fuel moisture content. Agric For Meteorol 319(108941):1–10
Peng B, Zhang JW, Xing J, Liu JQ (2021a) Online moisture measurement of dead fine fuel on the forest floor using near-infrared reflectometry. Rev Sci Instrum 92(065103):1–8
Peng B, Zhang JW, Xing J, Liu JQ (2021b) Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan National Forest, China using a LoRa wireless network. J For Res 33(3):899–909
Qi HQ, Zhou Q, Lu XM, Wan XQ (2013) Design and implementation of forest fire monitoring system based on Google maps. Video Eng 37(17):139–182 (in Chinese)
Shmuel A, Ziv Y, Heifetz E (2022) Machine-learning-based evaluation of the time-lagged effect of meteorological factors on 10-hour dead fuel moisture content. Forest Ecol Manag 505(119897):1–9
Sun L, Liu Q, Hu TX (2021) Advances in research on prediction model of moisture content of surface dead fuel in forests. Scientia Silvae Sinicae 57(4):141–152 (in Chinese)
Tsuchikawa S, Ma T, Inagaki T (2022) Application of near-infrared spectroscopy to agriculture and forestry. Anal Sci 38(2022):635–642
Xing J, Ye YH, Ma Z, Peng B, Yang LS, Song WL (2018) NIR spectral characteristics of moisture content for forest litter. Spectrosc Spectr Anal 38(10):3101–3105 (in Chinese)
Yebra M, Quan XW, Riaño D, Larraondo PR, Van Dijk AIJM, Cary GJ (2018) A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens Environ 212(2018):260–272
Zhang R, Hu HQ, Qu ZL, Hu TX (2021) Diurnal variation models for fine fuel moisture content in boreal forests in China. J For Res 32(3):1177–1187