USING OF REMOTE SENSING IN NATURAL RESOURCE OF FOREST MANAGEMENT AT ZAWITA FOREST REGION

“…The result showed that we obtained six land cover types (dense forests, open forests, pastures, agricultural lands, soil and rocky lands). …”
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Predicting Object Communication Errors in Constructor Development by Abdul Majid Soomro, Awad Bin Naeem, Susama Bagchi, Babul Salam KSM Kader Ibrahim, Sanjoy Kumar Debnath

“…We evaluated this object communication error prediction using a set of 150 common errors drawn primarily from real-world open-source repositories and enriched with synthesized cases reflecting rare but critical inheritance-related bugs, ensuring comprehensive and realistic error representation. …”
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Detecting and reducing heterogeneity of error in acoustic classification by Oliver C. Metcalf, Jos Barlow, Yves Bas, Erika Berenguer, Christian Devenish, Filipe França, Stuart Marsden, Charlotte Smith, Alexander C. Lees

“…Second, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. …”
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The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive by A. Viana-Soto, C. Senf

“…<p>Forests in Europe are undergoing complex changes that require a comprehensive monitoring of disturbance occurrence. …”
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Hybrid regression method to predict forest variables from Earth observation data in boreal forests by Eelis Halme, Matti Mõttus

“…This study introduces a hybrid regression method, integrating the forest reflectance and transmittance model FRT with a random forest regressor. …”
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ForestAlign: Automatic forest structure-based alignment for multi-view TLS and ALS point clouds by Juan Castorena, L. Turin Dickman, Adam J. Killebrew, James R. Gattiker, Rod Linn, E. Louise Loudermilk

“…Access to highly detailed models of heterogeneous forests, spanning from the near surface to above the tree canopy at varying scales, is increasingly in demand. …”
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Square Root Compression and Noise Effects in Digitally Transformed Images by Craig E. DeForest, Chris Lowder, Daniel B. Seaton, Matthew J. West

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Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests by Spencer O’Keeffe, Bruce H. Thomas, Jim O’Hehir, Jan Rombouts, Michelle Balasso, Andrew Cunningham

“…Results showed that stratified tool selection, optimized for each forest development stage, achieved high accuracy for inventory, achieving stem detection rates up to 99.1% and errors as low as 0.94 m for height and 1.18 cm for diameter at breast height (DBH) in specific cases. …”
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Pan-European forest maps produced with a combination of earth observation data and national forest inventory plotsZenodo by Jukka Miettinen, Johannes Breidenbach, Patricia Adame, Radim Adolt, Iciar Alberdi, Oleg Antropov, Ólafur Arnarsson, Rasmus Astrup, Ambros Berger, Jón Bogason, Gherardo Chirici, Piermaria Corona, Giovanni D'Amico, Jiří Fejfar, Christoph Fischer, Florence Gohon, Thomas Gschwantner, Johannes Hertzler, Zsofia Koma, Kari T. Korhonen, Luka Krajnc, Nicolas Latte, Philippe Lejeune, Andrew McCullagh, Marcin Mionskowski, Daniel Moreno-Fernández, Mari Myllymäki, Mats Nilsson, Jérôme Perin, Juho Pitkänen, John Redmond, Thomas Riedel, Johannes Schumacher, Lauri Seitsonen, Laura Sirro, Mitja Skudnik, Arnór Snorrason, Radosław Sroga, Berthold Traub, Björn Traustason, Bertil Westerlund, Stephanie Wurpillot

“…The maps are on average nearly unbiased on European level (1.0 % of the mean AGB), but show significant overestimation for small biomass values (53 % bias for forests with AGB less than 150 t/ha) and underestimation for high biomass values (-55 % bias for forests with AGB higher than 500 t/ha).The created maps are the first of their kind as they are utilizing a large number of harmonized NFI plot observations and consistent remote sensing data for high-resolution forest attribute mapping. …”
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Comparative Analysis of Ultra-Wideband and Mobile Laser Scanning Systems for Mapping Forest Trees under A Forest Canopy by Z. Liu, H. Kaartinen, T. Hakala, H. Hyyti, A. Kukko, A. Kukko, J. Hyyppa, J. Hyyppa, R. Chen

“…To our best knowledge, this is the first study to compare UWB and MLS for mapping forest trees in the literature. The experimental results show that the proposed method can accurately measure tree stem locations under the forest canopy with a root-mean-square-error (RMSE) of 14.44 cm and a mean-absolute-error (MAE) of 12.39 cm, providing accuracy comparable to that of the three tested MLSs. …”
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P-Band PolInSAR Sub-Canopy Terrain Retrieval in Tropical Forests Using Forest Height-to-Unpenetrated Depth Mapping by Chuanjun Wu, Jiali Hou, Peng Shen, Sai Wang, Gang Chen, Lu Zhang

“…A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. …”
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An explicit forest carbon stock model and applications by Ningning Zhu, Bisheng Yang, Weishu Gong, Shen Ying, Wenxia Dai, Zhen Dong

“…First, the pixel size, forest canopy density, terrain slope, and forest height were used in the construction of EFM; Second, the EFM parameters were solved by simulated forest scene; Third, the EFM was used in simulated and real forest scenes to verify the accuracy, robustness, and applicability, the experiments show that the relative error is about 15%; Finally, the first time mapping forest carbon stock over 200,000 km2 area at 2 m scale was completed by the EFM. …”
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Estimation of Forest Aboveground Biomass Using Multitemporal Quad-Polarimetric PALSAR-2 SAR Data by Model-Free Decomposition Approach in Planted Forest by Cong Peng, Chao Fang, Jiangping Long, Tingchen Zhang, Huanna Zheng, Zilin Ye

“…Moreover, given the model-based or model-free decomposition methods, using the combined datasets from multitemporal SAR images led to a substantial increase in determination coefficient (R2) and a great decrease of relative root mean square error (rRMSE) of mapping forest AGB for each regression method than using the individual images. …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…DT showed that annual precipitation was the most important predictor for forest type classification with risk estimate of 0.412 (std error 0.31) and 0.478 (std error 0.52) for training and validation respectively. …”
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Complexity control method of random forest based HEVC by Peng WEN, Zongju PENG, Fen CHEN, Gangyi JIANG, Mei YU

“…High efficiency video coding (HEVC) has high computational complexity,and fast algorithm cannot perform video coding under restricted coding time.Therefore,a complexity control method of HEVC based on random forest was proposed.Firstly,three random forest classifiers with different prediction accuracy were trained to provide various coding configurations for coding tree unit (CTU).Then,an average depth-complexity model was built to allocate CTU complexity.Finally,the CTU coding configuration,determined by the smoothness,average depth,bit,and CTU-level accumulated coding error,was used to complete complexity control.The experimental results show that the proposed method has better complexity control precision,and outperforms the state-of-the-art method in terms of video quality.…”
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Predicting Diameter Distributions in Mixed Forests in Southern Mexico by Juan Carlos Guzmán-Santiago, Héctor Manuel de los Santos-Posadas, Benedicto Vargas-Larreta, Martín Gómez-Cárdenas, Wenceslao Santiago-García, Adan Nava-Nava

“…Understanding the diameter structure of a stand is crucial for making informed decisions regarding silviculture and forest management. This is achieved by collecting forest inventory data and applying them to probability density functions. …”
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Application of Forest Integrity Assessment to Determine Community Diversity in Plantation Forests Managed Under Carbon Sequestration Projects in the Western Qinba Mountains, China by Chun-Jing Wang, Dong-Zhou Deng, Wu-Xian Yan, Zhi-Wen Gao, Shan-Feng Huang, Ji-Zhong Wan

“…FIA scores were closely associated with Pielou’s evenness index of plant communities in plantation forests managed under carbon sequestration projects (R² = 0.104; mean square error = 0.014; standard error = 0.104; <i>p</i> = 0.012). …”
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How Surgeons and Surgical Leaders Manage Complications, Medical Errors, Malpractice, and Second Victim SyndromeThe Uses of SorrowTransparency, Compassion, and Truth in Medical Erro... by Peter M. Waters, David R. DeMaso, James J. Horgan, Steven L. Frick

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ANALYSIS OF MULTITEMPORAL AERIAL IMAGES FOR FENYŐFŐ FOREST CHANGE DETECTION by SHUKHRAT SHOKIROV, GÉZA KIRÁLY

“…Overall accuracy of classification was 77.2%, analysis showed that coniferous tree type classification was very accurate, but deciduous tree classification had a lot of omission errors. Based on the results and analysis, general information about forest health conditions has been presented. …”

Model error propagation in a compatible tree volume, biomass, and carbon prediction system by James A. Westfall, Philip J. Radtke, David M. Walker, John W. Coulston

“…However, the propagation of model error can be a concern as this compatibility often relies on predictions for one attribute providing the basis for other attributes. …”
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