Search Results - machine learning algorithms in forestry

Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches by Antúnez, Pablo, Wehenkel, Christian, Basave-Villalobos, Erickson, Calixto-Valencia, Celi Gloria, Valenzuela-Encinas, César, Ruiz-Aquino, Faustino, Sarmiento-Bustos, David

“…This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass…”
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A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms by Tang, Ziyi, Ye, Yu, Jiang, Zhidian, Fu, Chaowei, Huang, Rong, Yao, Dong

“… Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms…”
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Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India by Uniyal, Swati, Purohit, Saurabh, Chaurasia, Kuldeep, Rao, Sitiraju Srinivas, Amminedu, Eadara

“…•Geospatial data with Machine learning algorithms can accurately predict urban forest aboveground biomass and carbon in arid region…”
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Configuracao de algoritmos de aprendizado de maquina na modelagem florestal: um estudo de caso na modelagem da relacao hipsometrica/Tunning machine learning algorithms for forestry modeling: a case study in the height-diameter relationship by da Costa Filho, Sérgio Vinícius Serejo, Arce, Julio Eduardo, Montaño, Razer Nizer Rojas, Pelissari, Allan Libanio

“…No presente estudo foram aplicados quatro algoritmos de aprendizado de máquina na tarefa de modelagem da relação hipsométrica de povoamentos de Pinus taeda L…”
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Forestry Digital Twin With Machine Learning in Landsat 7 Data by Jiang, Xuetao, Jiang, Meiyu, Gou, YuChun, Li, Qian, Zhou, Qingguo

“… In this study, we propose a machine learning-based digital twin approach for forestry. A data processing algorithm was designed to process Landsat 7 remote sensing data as model's input…”
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Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India by Shivaprakash, Kadukothanahally Nagaraju, Swami, Niraj, Mysorekar, Sagar, Arora, Roshni, Gangadharan, Aditya, Vohra, Karishma, Jadeyegowda, Madegowda, Kiesecker, Joseph M.

“…) and machine learning algorithms (ML) in the forestry…”
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Using Machine Learning in Forestry by Kamber Can Alkiş, Zennure Uçar, Abdurrahim Aydın, Remzi Eker

“…) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning…”
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Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile by Jaque Castillo, Edilia, Fernández, Alfonso, Fuentes Robles, Rodrigo, Ojeda, Carolina G.

“…–biophysical factors that trigger fires. Those were used to deliver a model of fire hazard using machine learning algorithms, including principal component analysis and Kohonen self-organizing maps in two experimental scenarios…”
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Improving plot-level growing stock volume estimation using machine learning and remote sensing data fusion by Mirpulatov, I., Kedrov, A., Illarionova, S.

“… To automate the data analysis process, machine learning (ML) algorithms are widely applied, particularly in forestry tasks…”
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Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape by Ågren, Anneli M., Larson, Johannes, Paul, Siddhartho Shekhar, Laudon, Hjalmar, Lidberg, William

“…•Extreme Gradient Boosting was the best algorithm for mapping soil moisture.•The continuous soil moisture map would significantly contribute to practical forestry…”
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Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests by Yang, Qichi, Wang, Lihui, Huang, Jinliang, Lu, Lijie, Li, Yang, Du, Yun, Ling, Feng

“…Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of…”
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Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments by Zhao, Qingxia, Yu, Shichuan, Zhao, Fei, Tian, Linghong, Zhao, Zhong

“…•Accurate and quickly evaluate forest quality based on satellite images.•Four MLAs were implemented and compared to estimate forest parameters.•RF obtained the…”
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A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm by Nguyen, Thu Thuy, Ngo, Huu Hao, Guo, Wenshan, Chang, Soon Woong, Nguyen, Dinh Duc, Nguyen, Chi Trung, Zhang, Jian, Liang, Shuang, Bui, Xuan Thanh, Hoang, Ngoc Bich

“… In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1…”
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Machine learning assisted remote forestry health assessment: a comprehensive state of the art review by Estrada, Juan Sebastián, Fuentes, Andrés, Reszka, Pedro, Auat Cheein, Fernando

“… Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content…”
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Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity by Zhao, Tianyue, Liu, Shuchao, Xu, Jia, He, Hailong, Wang, Dong, Horton, Robert, Liu, Gang

“…), Campbell (1985) model (Campbell1985), Johansen (1975) model (Johansen 1975), Côté and Konrad (2005) model (Côté and Konrad 2005), and Lu et al. (2007) model (Lu 2007)) and seven machine learning…”
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Rapid Large-Scale Monitoring of Pine Wilt Disease Using Sentinel-1/2 Images in GEE by Zhi, Junjun, Li, Lin, Fang, Yifan, Zhi, Dandan, Guang, Yi, Liu, Wangbin, Qu, Lean, Fu, Xinwu, Zhao, Haoshan

“… to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine learning/deep learning algorithms with Sentinel-1/2 images in the Google Earth Engine cloud platform to implement province-wide…”
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A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform by Teluguntla, Pardhasaradhi, Thenkabail, Prasad S, Oliphant, Adam, Xiong, Jun, Gumma, Murali Krishna, Congalton, Russell G., Yadav, Kamini, Huete, Alfredo

“…•Captured spatial extent of very small to very large farms in Australia and China.•Applied Random Forest machine learning algorithm on cloud computing platform…”
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Land subsidence susceptibility assessment using random forest machine learning algorithm by Mohammady, Majid, Pourghasemi, Hamid Reza, Amiri, Mojtaba

“…The mechanism of land subsidence and soil deformation deals with the dissipation of excess pore water pressure and the compaction of soil skeleton under the…”
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Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms by Li, Yingchang, Li, Chao, Li, Mingyang, Liu, Zhenzhen

“… In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF…”
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Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application by Yildiz, Muslume Beyza, Yasin, Elham Tahsin, Koklu, Murat

“… To achieve this, we have developed a combination of deep and machine learning models that accurately classify the freshness of fish…”
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