Article
Environmental Sciences
Jiaming Lu, Chengquan Huang, Xin Tao, Weishu Gong, Karen Schleeweis
Summary: This study produced annual forest disturbance intensity maps for the contiguous United States from 1986 to 2015, revealing that higher disturbance intensities were found in the Southeastern and Western regions. The overall trend showed a slight increase in disturbance intensity over the study period.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
James C. Maltman, Txomin Hermosilla, Michael A. Wulder, Nicholas C. Coops, Joanne C. White
Summary: Forest age is a crucial variable for assessing biodiversity, sustainable land management, and forest carbon science. Estimating forest age using optical Earth observation data is challenging due to limited spectral link to the attribute of interest, especially for older forests. In this study, three approaches were combined to estimate forest age at a 30-m spatial resolution in Canada's forested ecosystems. Spatially explicit maps of forest age provide valuable information for understanding forest ecosystems and can be utilized in various policy, science, and management applications.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Biodiversity Conservation
Tracey S. Frescino, Gretchen G. Moisen, Paul L. Patterson, Chris Toney, Grayson W. White
Summary: Ecologists are using national forest inventories to address various issues. The 'FIESTA' R package provides a flexible platform for customized investigations using extensive inventory data. It contains functions to query databases, summarize data, extract spatial data, and generate estimates with variances.
Article
Forestry
Johannes Breidenbach, David Ellison, Hans Petersson, Kari T. Korhonen, Helena M. Henttonen, Jorgen Wallerman, Jonas Fridman, Terje Gobakken, Rasmus Astrup, Erik Naesset
Summary: Using satellite-based maps and National Forest Inventory observations, this study finds that the ability of the maps to detect harvested areas abruptly increased after 2015 in Finland and Sweden, rather than the actual harvested area.
ANNALS OF FOREST SCIENCE
(2022)
Article
Environmental Sciences
Txomin Hermosilla, Alex Bastyr, Nicholas C. Coops, Joanne C. White, Michael A. Wulder
Summary: Knowledge of tree species is essential for forest management and monitoring, and can be achieved through remote sensing and spatial modeling. This study used National Forest Inventory data and machine learning algorithms to map and classify tree species in Canada's forest-dominated ecosystems. The overall accuracy of the classification models was 93.1%, with geographic, climatic, and topographic variables being the most influential. The most common leading tree species nationally were black spruce, trembling aspen, and lodgepole pine, while regionally there was dominance of other tree species.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Andre Beaudoin, Ronald J. J. Hall, Guillermo Castilla, Michelle Filiatrault, Philippe Villemaire, Rob Skakun, Luc Guindon
Summary: Satellite forest inventories using k-nearest neighbor algorithm combined with Landsat and SAR data can accurately map forest attributes in Canada's northern boreal forests. This study demonstrates the feasibility and effectiveness of optimizing k-NN parameters and feature space for inventory mapping.
Article
Environmental Sciences
Lucia A. Fitts, Matthew B. Russell, Grant M. Domke, Joseph K. Knight
Summary: Forests serve as the largest terrestrial carbon sink, but are threatened by land use change. This study in six states utilized USDA Forest Service data to model the probability of forest conversion and carbon stock dynamics, revealing that areas with higher human population and housing growth rates are more likely to experience forest conversion. The results emphasize the importance of considering land use change in carbon accounting and the need for policy-makers to prioritize forest management activities and land use planning.
CARBON BALANCE AND MANAGEMENT
(2021)
Article
Remote Sensing
Katsuto Shimizu, Hideki Saito
Summary: This study explores a method to detect forestry harvesting and disturbance areas nationwide, as well as characterize post-harvest recovery using Landsat time series data. The results demonstrate the effectiveness and accuracy of the approach in providing valuable insights for forest management.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Forestry
Lance A. Vickers, Benjamin O. Knapp, John M. Kabrick, Laura S. Kenefic, Anthony W. D'Amato, Christel C. Kern, David A. MacLean, Patricia Raymond, Kenneth L. Clark, Daniel C. Dey, Nicole S. Rogers
Summary: Mixedwood forests in the northern United States cover a significant area and are most common in the Adirondack - New England, Laurentian, and Northeast ecological provinces. The presence of hardwood and softwood saplings indicates a potential shift towards hardwood dominance in the absence of disturbances.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Environmental Sciences
Jacob L. Strunk, David M. Bell, Matthew J. Gregory
Summary: This study demonstrates the potential of pushbroom Digital Aerial Photogrammetry (DAP) combined with multitemporal Landsat derivatives to enhance forest modeling and mapping over large areas. The National Agricultural Imagery Program (NAIP) provides high resolution photogrammetric forest structure measurements at low cost. DAP shows the greatest explanatory power for a wide range of forest attributes, but performance is improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to the models. Further investigation is needed to address local biases.
Article
Environmental Sciences
Shingo Obata, Chris J. Cieszewski, Roger C. Lowe, Pete Bettinger
Summary: This study explored the impact of using long Landsat time series data, forest disturbance record, and land cover data on satellite-based forest volume estimation accuracy. The results showed that utilizing the long Landsat time series improved model performance, whereas the inclusion of forest disturbance data had little effect. Additionally, applying a bias correction method effectively reduced the bias in estimates.
Article
Environmental Sciences
Guillermo Castilla, Ronald J. Hall, Rob Skakun, Michelle Filiatrault, Andre Beaudoin, Michael Gartrell, Lisa Smith, Kathleen Groenewegen, Chris Hopkinson, Jurjen van der Sluijs
Summary: Sustainable forest management requires detailed information on the spatial distribution, composition, and structure of forests. However, in regions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, this information is often lacking. The Multisource Vegetation Inventory (MVI) project used a combination of field data and remote sensing data from multiple sources to create a large area forest inventory map that could support strategic forest management in the NWT. This project demonstrated that a reasonably accurate forest inventory map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost.
Article
Remote Sensing
Konrad Turlej, Mutlu Ozdogan, Volker C. Radeloff
Summary: This study successfully mapped forest composition under different data availability conditions using a data driven approach with Landsat imagery. The results showed that high accuracy forest type mapping can be achieved even with missing data, and the number of acquisitions and seasonal image availability had an impact on classification accuracy.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Sciences
Todd A. Schroeder, Shingo Obata, Monica Papes, Benjamin Branoff
Summary: The Forest Inventory and Analysis (FIA) program of the U.S. Forest Service aims to estimate various forest attributes using a design-based network of sampling plots. This study explores the use of digital aerial photogrammetric (DAP) point clouds developed from stereo imagery to improve these estimates in southeastern mixed hardwood forests. The results show that using the DAP point clouds improved the precision of forest volume estimates compared to using tree canopy cover data.
Review
Ecology
Jonathan A. Knott, Greg C. Liknes, Courtney L. Giebink, Sungchan Oh, Grant M. Domke, Ronald E. McRoberts, Valquiria F. Quirino, Brian F. Walters
Summary: Large-scale ecological sampling networks aim to collect in situ data for various purposes, but the issue of outliers arising in data harmonization is often overlooked. This paper reviews the sources of outliers and their impact on estimates of above-ground biomass population parameters using a case study. The study shows that the inclusion or removal of outliers can lead to substantial differences in biomass estimates, highlighting the importance of proper use of field-collected and remotely sensed data in geospatial data harmonization.
METHODS IN ECOLOGY AND EVOLUTION
(2023)
Article
Engineering, Electrical & Electronic
Petteri Packalen, Jacob L. Strunk, Juho A. Pitkanen, Hailemariam Temesgen, Matti Maltamo
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2015)
Article
Remote Sensing
Joonghoon Shin, Hailemariam Temesgen, Jacob L. Strunk, Thomas Hilker
CANADIAN JOURNAL OF REMOTE SENSING
(2016)
Article
Plant Sciences
Joshua W. R. Baur, Joanne F. Tynon, Paul Ries, Randall S. Rosenberger
URBAN FORESTRY & URBAN GREENING
(2016)
Article
Remote Sensing
Joonghoon Shin, Hailemariam Temesgen, Jacob L. Strunk, Thomas Hilker
CANADIAN JOURNAL OF REMOTE SENSING
(2016)
Article
Forestry
Jacob L. Strunk, Peter J. Gould, Petteri Packalen, Krishna P. Poudel, Hans-Erik Andersen, Hailemariam Temesgen
Article
Plant Sciences
Paul D. Ries
URBAN FORESTRY & URBAN GREENING
(2019)
Article
Environmental Sciences
Petteri Packalen, Jacob Strunk, Tuula Packalen, Matti Maltamo, Lauri Mehtatalo
REMOTE SENSING OF ENVIRONMENT
(2019)
Article
Plant Sciences
Christine Johnson, Jenna H. Tilt, Paul D. Ries, Bruce Shindler
URBAN FORESTRY & URBAN GREENING
(2019)
Article
Forestry
Diogo N. Cosenza, Lauri Korhonen, Matti Maltamo, Petteri Packalen, Jacob L. Strunk, Erik Naesset, Terje Gobakken, Paula Soares, Margarida Tome
Summary: In this study, the performances of OLS, kNN, and RF in forest yield modeling were compared, revealing that OLS and RF had similar and higher accuracies compared to kNN. Variable selection did not significantly impact RF performance, while heuristic and exhaustive selection methods had similar effects on OLS. Caution is advised when building kNN models for volume prediction, with a preference for OLS with variable selection or RF with all variables included.
Article
Plant Sciences
Jenna H. Tilt, Paul D. Ries
Summary: Implementing and sustaining green infrastructure projects in small communities can be constrained by factors such as cumbersome regulations and a lack of regulatory structure. Catalysts for driving and sustaining these projects include close relationships with staff, landowners, and the public, as well as a dedicated source of funding.
URBAN FORESTRY & URBAN GREENING
(2021)
Article
Forestry
Diogo N. Cosenza, Petteri Packalen, Matti Maltamo, Petri Varvia, Janne Raty, Paula Soares, Margarida Tome, Jacob L. Strunk, Lauri Korhonen
Summary: This study examines the limits of predictor and training plot numbers for accurate prediction without overfitting in various models used in area-based approach. The findings suggest that some models tend to overfit when the number of predictors approaches the number of training plots. However, for most models, using larger datasets results in more accurate predictions.
CANADIAN JOURNAL OF FOREST RESEARCH
(2022)
Article
Environmental Sciences
Jacob L. Strunk, David M. Bell, Matthew J. Gregory
Summary: This study demonstrates the potential of pushbroom Digital Aerial Photogrammetry (DAP) combined with multitemporal Landsat derivatives to enhance forest modeling and mapping over large areas. The National Agricultural Imagery Program (NAIP) provides high resolution photogrammetric forest structure measurements at low cost. DAP shows the greatest explanatory power for a wide range of forest attributes, but performance is improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to the models. Further investigation is needed to address local biases.
Article
Forestry
Jacob L. Strunk, Robert J. McGaughey
Summary: We evaluated several area-based approaches to predict forest attributes using lidar data, including post-stratification, ordinary least squares (OLS) regression, k nearest neighbors (kNN), and random forest (RF). The study was conducted in South Carolina, USA. The results showed that lidar can effectively provide stand-level inferences for a wide range of forest attributes, although volume predictions for specific diameter classes were often inaccurate, especially for larger diameter trees. kNN and RF performed similarly and better than OLS and PS, but RF was more robust while kNN had practical advantages in simultaneous predictions of multiple attributes.
CANADIAN JOURNAL OF FOREST RESEARCH
(2023)
