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https://github.com/andrew-plowright/ForestTools

Detect and segment individual tree from remotely sensed data
https://github.com/andrew-plowright/ForestTools

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Detect and segment individual tree from remotely sensed data

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README 

        
ForestTools 

======================================================================================================

![license](https://img.shields.io/badge/Licence-GPL--3-blue.svg) 

[![](https://www.r-pkg.org/badges/version/ForestTools)](https://www.r-pkg.org/pkg/ForestTools)

[![R-CMD-check](https://github.com/andrew-plowright/ForestTools/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/andrew-plowright/ForestTools/actions/workflows/R-CMD-check.yaml)

[![](https://cranlogs.r-pkg.org/badges/ForestTools)](https://CRAN.R-project.org/package=ForestTools)



The ForestTools R package offers functions to analyze remote sensing forest data. Please consult the [NEWS.md](NEWS.md) file for updates.



To get started, consult the [canopy analysis tutorial](inst/guides/treetop_analysis.md). For a quick guide on generating spatial statistics from ForestTools outputs, consult the [spatial statistics tutorial](inst/guides/spatial_statistics.md)



To cite the package use `citation("ForestTools")` from within R.



```

Plowright A. (2023). ForestTools: Tools for Analyzing Remote Sensing Forest Data. R package version 1.0.2,

https://github.com/andrew-plowright/ForestTools.

```



## Features



### Detect and segment trees



Individual trees can be detected and delineated using a combination of the

**variable window filter**  (`vwf`) and **marker-controlled watershed segmentation**

(`mcws`) algorithms, both of which are applied to a rasterized **canopy height model (CHM)**.

CHMs are typically derived from aerial LiDAR or photogrammetric point clouds.



![image info](./man/figures/treetops_segments.png)



### Compute textural metrics



**Grey-level co-occurrence matrices** (GLCMs) and their associated statistics can be computed for individual trees using a single-band

image and a segment raster (which can be produced using `mcws`). These metrics can be used as predictors for tree classification.



## References



This library implements techniques developed in the following studies:



* **Variable window filter**: [Seeing the trees in the forest](https://www.ingentaconnect.com/content/asprs/pers/2004/00000070/00000005/art00003) by Popescu, S. C., & Wynne, R. H. (2004)

* **Marker-controlled watershed segmentation**: [Morphological segmentation](https://www.sciencedirect.com/science/article/pii/104732039090014M) by Meyer, F., & Beucher, S. (1990)

* **Grey-level co-occurrence matrices**: [Robust radiomics feature quantification using semiautomatic volumetric segmentation](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0102107) by Parmar, C., Velazquez, E.R., Leijenaar, R., Jermoumi, M., Carvalho, S., Mak, R.H., Mitra, S., Shankar, B.U., Kikinis, R., Haibe-Kains, B. and Lambin, P. (2014)



## Research



The following is a non-exhaustive list of studies that use the ForestTools library. Several of these papers discuss topics such as algorithm parameterization, and may be informative for users of this library.



### 2024



* [UAV-based LiDAR and Multispectral images for forest trait retrieval](https://meetingorganizer.copernicus.org/EGU24/EGU24-19456.html) by Vignali, L., Panigada, C., Tagliabue, G., Savinelli, B., Garzonio, R., Colombo, & Rossini, M. (2024)



* [Coupling UAV and satellite data for tree species identification to map the distribution of Caspian poplar](https://link.springer.com/article/10.1007/s10980-024-01846-8) by Miraki, M., Sohrabi, H., Fatehi, P., & Kneubuehler, M. (2024)



### 2023



* [A novel post-fire method to estimate individual tree crown scorch height and volume using simple RPAS-derived data](https://fireecology.springeropen.com/articles/10.1186/s42408-023-00174-7) by Arkin, J., Coops, N. C., Daniels, L. D., & Plowright, A. (2023)



* [Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure ](https://www.mdpi.com/2072-4292/16/1/143) by Gao, S., Woodgate, W., Ma, X., & Doody, T. M. (2023)



* [From Local to Micro: Exploratory Data Analysis on Urban Forests and Microclimates in Portland, Oregon, USA](https://ieeexplore.ieee.org/abstract/document/10282088) by Yao, X., & Kim, M. (2023)



* [Mapping and monitoring of vegetation regeneration and fuel under major transmission power lines through image and photogrammetric analysis of drone-derived data](https://www.tandfonline.com/doi/full/10.1080/10106049.2023.2280597) by Sos, J., Penglase, K., Lewis, T., Srivastava, P. K., Singh, H., & Srivastava, S. K. (2023)



* [Patterns of Florida Bonneted Bat Occupancy at the Northern Extent of Its Range](https://meridian.allenpress.com/jfwm/article/doi/10.3996/JFWM-22-055/494603) by Schorr, R. A., Pitcher, K. A., Aldredge, R. A., & Lukacs, P. M. (2023)



* [Remotely sensed and ground measurements reveal intraspecific differences in early season needle unfolding and senescence, but lack of variability in litter flammability of Pinus halepensis](https://www.sciencedirect.com/science/article/pii/S0378112723007090) by Lombardi, E., Kefauver, S.C., Serrano, L., Sin, E., Piñas-Bonilla, P., Pérez, B., Luna, B., Zavala, G., de Dios, V.R. and Voltas, J. (2023)



* [A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis](https://www.mdpi.com/2072-4292/15/10/2621) by Penglase, K., Lewis, T., & Srivastava, S. K. (2023)



* [Biomass Estimation of Urban Forests Using LiDAR and High-Resolution Aerial Imagery in Athens–Clarke County, GA](https://www.mdpi.com/1999-4907/14/5/1064) by Henn, K. A., & Peduzzi, A. (2023)



* [Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images](https://www.mdpi.com/2072-4292/15/14/3599) by Kleinsmann, J., Verbesselt, J., & Kooistra, L. (2023)



* [Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images](https://www.mdpi.com/2072-4292/15/15/3779) by Wu, H., Zhuang, M., Chen, Y., Meng, C., Wu, C., Ouyang, L., Liu, Y., Shu, Y., Tao, Y., Qiu, T. and Li, J. (2023)



* [Modeling Biometrie Attributes from Tree Height Using Unmanned Aerial Vehicles (UAV) in Natural Forest Stands](http://www.scielo.org.co/scielo.php?pid=S0120-56092023000200002&script=sci_arttext) by Quiñonez-Barrazal, G., Pompa-García, M., Vivar-Vivar, E.D., Gallardo-Salazar, J.L., Hernández, F.J., Rodríguez-Flores, F.D.J., Solís-Moreno, R., Bretado-Velázquez, J.L., Valdez-Cepeda, R.D. and Hernández-Díaz, J.C. (2023)



* [Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13995) by Hardenbol, A. A., Korhonen, L., Kukkonen, M., & Maltamo, M. (2023)



* [Multi-temporal NDVI analysis using UAV images of tree crowns in a northern Mexican pine-oak forest](https://link.springer.com/article/10.1007/s11676-023-01639-w) by Gallardo-Salazar, J. L., Rosas-Chavoya, M., Pompa-García, M., López-Serrano, P. M., García-Montiel, E., Meléndez-Soto, A., & Jiménez-Jiménez, S. I. (2023)



* [A Lidar-based Method for 3D Urban Forest Evaluation and Microclimate Assessment, a Case Study in Portland, Oregon, USA](https://essopenarchive.org/doi/full/10.22541/essoar.170914530.09781933) by Yao, X., & Kim, M. (2023)



* [Effects of long‐term fixed fire regimes on African savanna vegetation biomass, vertical structure and tree stem density](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.14435) by Singh, J., Boucher, P. B., Hockridge, E. G., & Davies, A. B. (2023)



* [The role of in-channel vegetation in driving and controlling the geomorphic changes along a gravel-bed river](https://www.sciencedirect.com/science/article/pii/S0169555X23002234) by Picco, L., Pellegrini, G., Iroumé, A., Lenzi, M. A., & Rainato, R. (2023)



* [Using photogrammetry to assess the recovery of a cypress forest and its impact on water-borne erosion. Case study: Guadalupe Island](https://www.researchsquare.com/article/rs-3717140/v1) by Vera-Ortega, L. A., Hinojosa-Corona, A., Luna, L., & Gudiño-Elizondo, N. (2023)



* [UAV data collection parameters impact on accuracy of Scots pine stand mensuration](https://www.researchgate.net/profile/Roman-Zadorozhniuk/publication/371550369_UAV_data_collection_parameters_impact_on_accuracy_of_Scots_pine_stand_mensuration/links/64917bf1c41fb852dd19c381/UAV-data-collection-parameters-impact-on-accuracy-of-Scots-pine-stand-mensuration.pdf) by Zadorozhniuk, R. (2023)



* [Risk Analysis for Asset Protection in Hoyt Arboretum, Portland, OR](https://pdxscholar.library.pdx.edu/geog_master_GIS_reports/6/) by Kossnar, N. (2023).



* [Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS](https://www.sciencedirect.com/science/article/pii/S2666017222000347) by du Toit, F., Coops, N. C., Ratcliffe, B., El-Kassaby, Y. A., & Lucieer, A. (2023)



* [Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery](https://www.mdpi.com/2072-4292/15/3/844) by Kluczek, M., Zagajewski, B., & Zwijacz-Kozica, T. (2023)



* [Use of Drone RGB Imagery to Quantify Indicator Variables of Tropical-Forest-Ecosystem Degradation and Restoration](https://www.mdpi.com/1999-4907/14/3/586) by Lee, K., Elliott, S., & Tiansawat, P. (2023)



### 2022



* [Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data](https://www.mdpi.com/2072-4292/14/4/926) by Kubišta, J., & Surový, P. (2022)



* [Utilizing Single Photon Laser Scanning Data for Estimating Individual Tree Attributes](https://helda.helsinki.fi/bitstream/handle/10138/344212/isprs_annals_V_2_2022_431_2022.pdf?sequence=1) by Simula, J., Holopainen, M., & Imangholiloo, M. (2022)



* [UAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardens](https://www.mdpi.com/2072-4292/14/22/5904) by Lombardi, E., Rodríguez-Puerta, F., Santini, F., Chambel, M. R., Climent, J., Resco de Dios, V., & Voltas, J. (2022)



* [Cross-Comparison of Individual Tree Detection Methods Using Low and High Pulse Density Airborne Laser Scanning Data](https://www.mdpi.com/2072-4292/14/14/3480) by Sparks, A. M., Corrao, M. V., & Smith, A. M. (2022)



* [Slow development of woodland vegetation and bird communities during 33 years of passive rewilding in open farmland](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277545) by Broughton, R. K., Bullock, J. M., George, C., Gerard, F., Maziarz, M., Payne, W. E., Scholefield, P. A., Wade, D., & Pywell, R. F. (2022)



* [Application of unmanned aerial system structure from motion point cloud detected tree heights and stem diameters to model missing stem diameters](https://www.sciencedirect.com/science/article/pii/S2215016122001108) by Swayze, N. C., & Tinkham, W. T. (2022)



* [Limited increases in savanna carbon stocks over decades of fire suppression](https://www.nature.com/articles/s41586-022-04438-1) by Zhou, Y., Singh, J., Butnor, J. R., Coetsee, C., Boucher, P. B., Case, M. F., Hockridge, E. G., Davies, A. B., & Staver, A. C. (2022)



* [Automated Inventory of Broadleaf Tree Plantations with UAS Imagery](https://www.mdpi.com/2072-4292/14/8/1931) by Chandrasekaran, A., Shao, G., Fei, S., Miller, Z., & Hupy, J. (2022)



* [Use of Unoccupied Aerial Systems to Characterize Woody Vegetation across Silvopastoral Systems in Ecuador](https://www.mdpi.com/2072-4292/14/14/3386) by Iñamagua-Uyaguari, J. P., Green, D. R., Fitton, N., Sangoluisa, P., Torres, J., & Smith, P. (2022)



* [Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network](https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4206) by Koontz, M. J., Scholl, V. M., Spiers, A. I., Cattau, M. E., Adler, J., McGlinchy, J., Goulden, T., Melbourne, B. A., & Balch, J. K. (2022).



* [An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data](https://www.mdpi.com/2072-4292/14/17/4317) by Gao, T., Gao, Z., Sun, B., Qin, P., Li, Y., & Yan, Z. (2022)



* [Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13995) by Hardenbol, A. A., Korhonen, L., Kukkonen, M., & Maltamo, M. (2022)



* [Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13860) by Young, D. J., Koontz, M. J., & Weeks, J. (2022)



* [Assessing Structural Complexity of Individual Scots Pine Trees by Comparing Terrestrial Laser Scanning and Photogrammetric Point Clouds](https://www.mdpi.com/1999-4907/13/8/1305) by Tienaho, N., Yrttimaa, T., Kankare, V., Vastaranta, M., Luoma, V., Honkavaara, E., ... & Saarinen, N. (2022)



* [SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches](https://essd.copernicus.org/articles/14/4967/2022/) by van Geffen, F., Heim, B., Brieger, F., Geng, R., Shevtsova, I. A., Schulte, L., ... & Kruse, S. (2022)



* [Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran](https://link.springer.com/article/10.1007/s10668-022-02820-7) by Azizi, Z., & Miraki, M. (2022)



* [Effect of varied unmanned aerial vehicle laser scanning pulse density on accurately quantifying forest structure](https://www.tandfonline.com/doi/abs/10.1080/01431161.2021.2023229) by Sumnall, M. J., Albaugh, T. J., Carter, D. R., Cook, R. L., Hession, W. C., Campoe, O. C., ... & Thomas, V. A. (2022)



* [Correcting the Results of CHM-Based Individual Tree Detection Algorithms to Improve Their Accuracy and Reliability](https://www.mdpi.com/2072-4292/14/8/1822) by Lisiewicz, M., Kamińska, A., Kraszewski, B., & Stereńczak, K. (2022)



* [Combining aerial photos and LiDAR data to detect canopy cover change in urban forests](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273487) by Coupland, K., Hamilton, D., & Griess, V. C. (2022)



* [Effects of Flight and Smoothing Parameters on the Detection of Taxus and Olive Trees with UAV-Borne Imagery](https://www.mdpi.com/2504-446X/6/8/197) by Ottoy, S., Tziolas, N., Van Meerbeek, K., Aravidis, I., Tilkin, S., Sismanis, M., Stavrakoudis, D., Gitas, I. Z., Zalidis, G. & De Vocht, A. (2022)



* [Modeling the Missing DBHs: Influence of Model Form on UAV DBH Characterization](https://www.mdpi.com/1999-4907/13/12/2077) by Tinkham, W. T., Swayze, N. C., Hoffman, C. M., Lad, L. E., & Battaglia, M. A. (2022)



* [Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery](https://www.mdpi.com/1424-8220/22/9/3269) by Rodríguez-Puerta, F., Barrera, C., García, B., Pérez-Rodríguez, F., & García-Pedrero, A. M. (2022)



* [Extraction of individual trees based on Canopy Height Model to monitor the state of the forest](https://www.sciencedirect.com/science/article/pii/S2666719322000644) by Douss, R., & Farah, I. R. (2022)



* [Aprisco Field Station: the spatial structure of a new experimental site focused on agroecology](https://academic.oup.com/jpe/article/15/6/1118/6576147) by O’Brien, M. J., Carbonell, E. P., & Schöb, C. (2022)



* [UAV-Based Characterization of Tree-Attributes and Multispectral Indices in an Uneven-Aged Mixed Conifer-Broadleaf Forest](https://www.mdpi.com/2072-4292/14/12/2775) by Vivar-Vivar, E. D., Pompa-García, M., Martínez-Rivas, J. A., & Mora-Tembre, L. A. (2022)



### 2021



* [Detectability of the Critically Endangered Araucaria angustifolia Tree Using Worldview-2 Images, Google Earth Engine and UAV-LiDAR](https://www.mdpi.com/2073-445X/10/12/1316) by Saad, F., Biswas, S., Huang, Q., Corte, A. P. D., Coraiola, M., Macey, S., Marcos Bergmann, M., & Leimgruber, P. (2021)



* [Fine scale mapping of fractional tree canopy cover to support river basin management](https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.14156) by Gao, S., Castellazzi, P., Vervoort, R. W., & Doody, T. M. (2021)



* [Above Ground Biomass Estimation of Syzygium aromaticum using structure from motion (SfM) derived from Unmanned Aerial Vehicle in Paninggahan Agroforest Area, West Sumatra](http://jbioua.fmipa.unand.ac.id/index.php/jbioua/article/view/338) by Harapan, T. S., Husna, A., Febriamansyah, T. A., Mutashim, M., Saputra, A., Taufiq, A., & Mukhtar, E. (2021)



* [Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density](https://www.sciencedirect.com/science/article/abs/pii/S0034425721002601) by Swayze, N. C., Tinkham, W. T., Vogeler, J. C., & Hudak, A. T. (2021)



* [Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds](https://www.silvafennica.fi/article/10515/author/20257) by Hardenbol, A. A., Kuzmin, A., Korhonen, L., Korpelainen, P., Kumpula, T., Maltamo, M., & Kouki, J. (2021)



* [Correcting tree count bias for objects segmented from lidar point clouds](https://www.proquest.com/openview/4c03d80d21aa8d71509deaae79259b9f/1?pq-origsite=gscholar&cbl=2030384) by Strub, M. R., & Osborne, N. (2021)



* [Comparison of Accuracy between Analysis Tree Detection in UAV Aerial Image Analysis and Quadrat Method for Estimating the Number of Trees to be Removed in the Environmental Impact Assessment](https://koreascience.kr/article/JAKO202118752917743.page) by Park, M. (2021)



* [Arboricoltura di precisione: un nuovo approccio alla gestione del rischio caduta alberi basato sulla Geomatica](https://mediageo.it/ojs/index.php/GEOmedia/article/view/1810) by De Petris, S., Sarvia, F., & Borgogno-Mondino, E. (2021)



* [Canopy Extraction and Height Estimation of Trees in a Shelter Forest Based on Fusion of an Airborne Multispectral Image and Photogrammetric Point Cloud](https://www.hindawi.com/journals/js/2021/5519629/) by Wang, X., Zhao, Q., Han, F., Zhang, J., & Jiang, P. (2021)



* [Uav-based lidar scanning for individual tree detection and height measurement in young forest permanent trials](https://www.mdpi.com/2072-4292/14/1/170) by Rodríguez-Puerta, F., Gómez-García, E., Martín-García, S., Pérez-Rodríguez, F., & Prada, E. (2021)



* [UAV-derived forest degradation assessments for planning and monitoring forest ecosystem restoration: towards a forest degradation index](https://www.cifor-icraf.org/knowledge/publication/8199/) by Lee, K. (2021)



* [Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds](https://doi.org/10.1139/cjfr-2020-0433) by Creasy, M. B., Tinkham, W. T., Hoffman, C. M., & Vogeler, J. C. (2021)



* [Assessment of Above-Ground Carbon Storage by Urban Trees Using LiDAR Data: The Case of a University Campus](https://www.mdpi.com/1999-4907/12/1/62) by Gülçin, D., & van den Bosch, C. C. K. (2021)



* [Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models](https://www.mdpi.com/1999-4907/12/2/250) by Tinkham, W. T., & Swayze, N. C. (2021)



* [Ground-Penetrating Radar as phenotyping tool for characterizing intraspecific variability in root traits of a widespread conifer](https://link.springer.com/article/10.1007/s11104-021-05135-0) by Lombardi, E., Ferrio, J. P., Rodríguez-Robles, U., de Dios, V. R., & Voltas, J. (2021)



* [Bridging the genotype–phenotype gap for a Mediterranean pine by semi‐automatic crown identification and multispectral imagery](https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.16862) by Santini, F., Kefauver, S. C., Araus, J. L., Resco de Dios, V., Martín García, S., Grivet, D., & Voltas, J. (2021)



* [Tracking the rates and mechanisms of canopy damage and recovery following Hurricane Maria using multitemporal lidar data](https://www.biorxiv.org/content/10.1101/2021.03.26.436869v1.abstract) by Leitold, V., Morton, D. C., Martinuzzi, S., Paynter, I., Uriarte, M., Keller, M., Keller, M., Ferraz, A., Cook, B. D., Corp, L. A., & González, G. (2021)



* [Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality](https://www.nature.com/articles/s41467-020-20455-y) by Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J., & North, M. P. (2021)



### 2020



* [The wildlife‐livestock interface on extensive free‐ranging pig farms in central Spain during the “montanera” period](https://onlinelibrary.wiley.com/doi/abs/10.1111/tbed.13854) by Triguero‐Ocaña, R., Laguna, E., Jiménez‐Ruiz, S., Fernández‐López, J., García‐Bocanegra, I., Barasona, J. Á., ... & Acevedo, P. (2020)



* [Supporting Assessment of Forest Burned Areas by Aerial Photogrammetry: The Susa Valley (NW Italy) Fires of Autumn 2017](https://link.springer.com/chapter/10.1007/978-3-030-58811-3_59) by De Petris, S., Momo, E. J., & Borgogno-Mondino, E. (2020)



* [Applying unmanned aerial vehicles (UAVs) to map shrubland structural attributes in northern Patagonia, Argentina](https://doi.org/10.1139/cjfr-2019-0440) by Gonzalez Musso, R. F., Oddi, F. J., Goldenberg, M. G., & Garibaldi, L. A. (2020)



* [Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery](https://www.mdpi.com/2072-4292/12/21/3632) by Abd-Elrahman, A., Guan, Z., Dalid, C., Whitaker, V., Britt, K., Wilkinson, B., & Gonzalez, A. (2020)



* [Northern Bobwhite Non‐Breeding Habitat Selection in a Longleaf Pine Woodland](https://wildlife.onlinelibrary.wiley.com/doi/abs/10.1002/jwmg.21925) by Kroeger, A. J., DePerno, C. S., Harper, C. A., Rosche, S. B., & Moorman, C. E. (2020)



* [Evaluation of Features Derived from High-Resolution Multispectral Imagery and LiDAR Data for Object-Based Support Vector Machine Classification of Tree Species](https://www.tandfonline.com/doi/abs/10.1080/07038992.2020.1809363) by Roffey, M., & Wang, J. (2020)



* [Mapping Species at an Individual-Tree Scale in a Temperate Forest, Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification](https://www.mdpi.com/2072-4292/12/22/3710) by Plakman, V., Janssen, T., Brouwer, N., & Veraverbeke, S. (2020)



### 2019



* [High-resolution multisensor remote sensing to support date palm farm management](https://www.mdpi.com/2077-0472/9/2/26) by Mulley, M., Kooistra, L., & Bierens, L. (2019)



* [Quantifying canopy tree loss and gap recovery in tropical forests under low-intensity logging using VHR satellite imagery and airborne LiDAR](https://www.mdpi.com/2072-4292/11/7/817) by Dalagnol, R., Phillips, O. L., Gloor, E., Galvão, L. S., Wagner, F. H., Locks, C. J., & Aragão, L. E. (2019)



* [Forest inventory sensitivity to UAS-based image processing algorithms](https://afrjournal.org/index.php/afr/article/download/1282/818) by Maturbongs, B., Wing, M. G., Strimbu, B., & Burnett, J. (2019)



* [Remote sensing pipeline for tree segmentation and classification in a mixed softwood and hardwood system](https://peerj.com/articles/5837/) by McMahon, C. A. (2019)



* [Tree height in tropical forest as measured by different ground, proximal, and remote sensing instruments, and impacts on above ground biomass estimates](https://www.sciencedirect.com/science/article/abs/pii/S0303243419300844) by Laurin, G. V., Ding, J., Disney, M., Bartholomeus, H., Herold, M., Papale, D., & Valentini, R. (2019)



* [Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds](https://www.mdpi.com/2072-4292/11/12/1447) by Brieger, F., Herzschuh, U., Pestryakova, L. A., Bookhagen, B., Zakharov, E. S., & Kruse, S. (2019)



* [Multi-scale Assessment of Northern Bobwhite and White-tailed Deer Habitat Selection in Longleaf Pine Woodlands](https://repository.lib.ncsu.edu/bitstream/handle/1840.20/37046/etd.pdf?sequence=1) by Kroeger, A. J. (2019)



### 2018



* [Bayesian and classical machine learning methods: a comparison for tree species classification with LiDAR waveform signatures](https://www.mdpi.com/2072-4292/10/1/39) by Zhou, T., Popescu, S. C., Lawing, A. M., Eriksson, M., Strimbu, B. M., & Bürkner, P. C. (2018)



### 2017



* [Underproductive agriculture aids connectivity in tropical forests](https://www.sciencedirect.com/science/article/abs/pii/S0378112717308101) by Evans, L. J., Goossens, B., & Asner, G. P. (2017)