Multifrequency Subsurface Soil Moisture Retrieval for Forest Flows: A Case Study in Te Hiku, New Zealand by Yu-Huan Zhao, Delwyn Moller, Dean Meason, Mahta Moghaddam

“…A subsurface soil moisture retrieval algorithm, using a pathfinder simultaneously acquired P- and L-band radar data over forested areas, is proposed in this article. It employs a generalized radar backscattering model for forests and a second-order polynomial function for soil moisture profile. …”
Get full text

Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire by Beyza Yılmaz, Mehveş Demirel, Filiz Bektaş Balçık

“…The differences of the pre and post-fire indices were calculated to determine the burned forest area. Error matrix was produced for accuracy assessment. …”
Get full text

Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani

“…Although approaches like multiple imputation (MI) and random forest (RF) proximity-based imputation offer improvements over naive deletion, they exhibit limitations in complex missing data scenarios or sparse high-dimensional settings. …”
Get full text

An approach for accurate identification and monitoring of species in mangrove forests based on multi-source spectral data and deep learning by Monterrubio-Martínez Erandi, Trujillo-Acatitla Rubicel, Tuxpan-Vargas José, Moreno-Casasola Patricia

“…Accordingly, this study presents an approach that integrates remote sensing data in a study area with a diverse range of ecological scenarios, comprising monospecific mangrove forests, which are dominated by a single species and mixed mangroves with flooded freshwater forested wetlands. …”
Get full text

Effects of sample tree selection and calculation methods on the accuracy of field plot values in area-based forest inventories by Lennart Noordermeer, Terje Gobakken, Johannes Breidenbach, Rune Eriksen, Erik Næsset, Hans Ole Ørka, Ole M. Bollandsås

“…Accurate field plot data on forest attributes are crucial in area-based forest inventories assisted by airborne laser scanning, providing an essential reference for calibrating predictive models. …”
Get full text

Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data by Win Sithu Maung, Satoshi Tsuyuki, Takuya Hiroshima, San San Htay

“…Initially, we tested machine learning models such as Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost). …”
Get full text

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management by Alireza Etaati, Mostafa Bastam, Ehsan Ataie

“…The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.…”
Get full text

Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest by Jean A. C. Dias, Pedro H. Do V. Guimaraes, Williane G. S. Pereira, Leonardo De O. Tamasauskas, Marivan S. Gomes, Alan B. S. Correa, Karla Figueiredo, Gilson Costa, Gabriel Brito Costa, Fernando A. R. Costa, Marcos C. Da R. Seruffo

Get full text

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis. by Claudia Bühnemann, Simon Li, Haiyue Yu, Harriet Branford White, Karl L Schäfer, Antonio Llombart-Bosch, Isidro Machado, Piero Picci, Pancras C W Hogendoorn, Nicholas A Athanasou, J Alison Noble, A Bassim Hassan

“…Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. …”
Get full text

Integration of In Situ and Sentinel-2 Data to Assess Soil Quality in Forest Monitoring: The Case Study of the Vesuvius Fires by Lucia Santorufo, Massimiliano Gargiulo, Valeria Memoli, Giulia Maisto, Rossella Barile, Giuseppe Ruello

“…The climatic conditions in southern Italy favor the occurrence and spread of forest fires, with severe long-lasting consequences on the local flora and fauna. …”
Get full text

A novel approach for snow depth retrieval in forested areas by integrating horizontal and vertical canopy structures information by Shanna Yue, Liyun Dai, Jie Deng, Yanxing Hu, Lin Xiao, Tao Che

“…The NMSV index was then incorporated into development of snow depth retrieval algorithm to improve accuracy of passive microwave snow depth estimation in forested areas. Compared to the Chang algorithm and the AMSR-E snow depth product, this study demonstrated higher accuracy in the mid- to high-latitude forested areas of Eurasia, with an R value approximately twice as high and a reduction in the overall root mean square error (RMSE) by 2.3 cm and 7.2 cm, respectively. …”
Get full text

Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States by Duo Jia, Cangjiao Wang, Christopher R. Hakkenberg, Izaya Numata, Andrew J. Elmore, Mark A. Cochrane

“…The interplay of nine factors affecting GEDI L4A is quantified, including the simulated waveform strategy deviation (SWSD) used in GEDI L4A, canopy characteristics (tree height, crown size, and canopy cover), canopy heterogeneity (crown size standard deviation, tree height standard deviation, and tree density), and other factors (forest type and topographic slope). Results show that compared with NEON observations, GEDI L4A generally underestimates the AGBD (Bias: −31.65 Mg/ha), with a moderate relative error exhibited in 14 of 19 sites (%RMSE ranging from 19% to 50%). …”
Get full text

Applying complex network metrics to individual-tree diameter growth modeling by Leite e Lopes I, Sousa da Mata A, Oliveira Castro RV, Viana da Páscoa KJ, Souza Jarochinski e Silva C, Rezende Gomide L

“…The innovative approach used in this study offers robust modeling to support forest growth analysis. Therefore, we encourage the application of this interdisciplinary tool to generate insights into forest science.…”
Get full text

Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models by Okikiola M. Alegbeleye, Krishna P. Poudel, Curtis VanderSchaaf, Yun Yang

“…National-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. …”
Get full text

Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

“…In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. …”
Get full text

Detecting forest and linear woody feature change between 1954 and 2019 in Southeastern Canadian agroecosystems for regional biodiversity assessment by Darren Pouliot, Niloofar Alavi, Mao Mao, Jon Pasher, Jason Duffe

“…As a final assessment, comparison of area within 200 by 200 m extents showed good agreement, with a mean absolute error of 1.3% for LWFs. For forests this was 2.7%. …”
Get full text

Assessment of Multiple Scattering in LiDAR Canopy Waveform by Xuebo Yang, Cheng Wang, Xiaohuan Xi, Zheng Niu, Dong Li, Sheng Nie, Kaiyi Bi, Ran Wang

“…Multiple scattering of laser ray leads to distance calculation error and accumulated intensity error in LiDAR waveform. …”
Get full text

Scale‐Dependent Inter‐Catchment Groundwater Flow in Forested Catchments: Analysis of Multi‐Catchment Water Balance Observations in Japan by Tomoki Oda, Kenta Iwasaki, Tomohiro Egusa, Tayoko Kubota, Sho Iwagami, Shin'ichi Iida, Hiroki Momiyama, Takanori Shimizu

“…This study examined possible factors influencing IGF using random forest analysis based on annual water balance data from 152 forested catchments ranging from 0.09 to 9400 ha in Japan. …”
Get full text

Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
Get full text

Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and Extraction of Individual Tree Parameters by Guoji Tian, Chongcheng Chen, Hongyu Huang

“…The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. …”
Get full text