Results 161 to 170 of about 757,026 (325)

Investigating boreal forest successional stages in Alaska and Northwest Canada using UAV‐LiDAR and RGB and a community detection network

Remote Sensing in Ecology and Conservation, EarlyView.
This study develops a novel application of UAV‐LiDAR and Red Green Blue (RGB) data and network analysis to enhance our understanding of boreal forest succession. The results indicate that tree height and spectral variables are the most influential predictors of plant functional type in random forest algorithms, and high overall accuracies were attained.
Léa Enguehard, Birgit Heim, Ulrike Herzschuh, Viktor Dinkel, Glenn Juday, Santosh Panda, Nicola Falco, Jacob Schladebach, Jakob Broers, Stefan Kruse   +9 more
wiley   +1 more source

Lidar-inertial SLAM method integrated with visual QR codes for indoor mobile robots. [PDF]

Sci Rep
Yang L, Tao Y, Li M, Zhou J, Jiao K, Li Z.   +5 more
europepmc   +1 more source

Spectral characterization of plant diversity in a biodiversity‐enriched oil palm plantation

Remote Sensing in Ecology and Conservation, EarlyView.
How well can airborne imaging spectroscopy detect plant diversity in vertically complex agroforestry systems? We tested this in a biodiversity‐enriched oil palm plantation in Sumatra, Indonesia, using high‐resolution hyperspectral data. We calculated spectral alpha and beta diversity and compared them to field‐based plant diversity.
Vannesa Montoya‐Sánchez, Anna K. Schweiger, Michael Schlund, Gustavo Brant Paterno, Stefan Erasmi, Holger Kreft, Dirk Hölscher, Fabian Brambach, Bambang Irawan, Leti Sundawati, Delphine Clara Zemp   +10 more
wiley   +1 more source

REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining. [PDF]

Sensors (Basel)
Raisuddin AM, Holmblad J, Haghighi H, Poledna Y, Drechsler MF, Donzella V, Aksoy EE.   +6 more
europepmc   +1 more source

Estimativa de impactos da extração seletiva de madeiras na Amazônia utilizando dados LIDAR [PDF]

, 2019
Charton Jahn Locks, Eraldo Aparecido Trondoli Matricardi   +1 more
openalex   +1 more source

Wall‐to‐wall Amazon forest height mapping with Planet NICFI, Aerial LiDAR, and a U‐Net regression model

Remote Sensing in Ecology and Conservation, EarlyView.
Tree canopy height is a key indicator of forest biomass and structure, yet accurate mapping across the Amazon remains challenging. Here, we generated a canopy height map of the Amazon forest at ~4.8 m resolution using Planet NICFI imagery and a deep learning U‐Net model trained with airborne LiDAR data.
Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi   +21 more
wiley   +1 more source

Cross-dataset late fusion of Camera-LiDAR and radar models for object detection. [PDF]

Sci Rep
Ali A, Tawfik MM, Saafan MM.
europepmc   +1 more source

Topographic Change from Lidar in Eastern Kentucky, USA (2012–2023, 2017-2023): Datasets covering the July 2022 Storm

Scheip, Corey, Weidner, Luke, Graham, Alexander, Hsiao, Vicky, Winters, Susan   +4 more
openalex   +1 more source

Improving forest age estimation to understand subtropical forest regrowth dynamics using deep learning image segmentation of time‐series historical aerial photographs

Remote Sensing in Ecology and Conservation, EarlyView.
Accurately estimating forest age is key to understanding how forests recover and evaluating restoration success. We developed a two‐step deep learning approach using historical greyscale aerial photographs to map forest age at fine spatial scales. By combining a pre‐trained model with localized fine‐tuning, our U‐Net + ResNet50 architecture achieved ...
Ying Ki Law, Yi‐Fei Gu, Shuwen Liu, Guangqin Song, Aland H. Y. Chan, Cham Man Tse, Zhonghua Liu, Martha J. Ledger, Billy C. H. Hau, Sawaid Abbas, Jin Wu   +10 more
wiley   +1 more source

Adaptive Cross-Modal Denoising: Enhancing LiDAR-Camera Fusion Perception in Adverse Circumstances. [PDF]

Sensors (Basel)
Ghaffar MA, Zhang K, Pan N, Peng L.
europepmc   +1 more source