Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

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Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Remote Sensing ◽

10.3390/rs13122297 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2297

Author(s):

Jonathon J. Donager ◽

Andrew J. Sánchez Meador ◽

Ryan C. Blackburn

Keyword(s):

Ponderosa Pine ◽

Laser Scanning ◽

Canopy Cover ◽

Basal Area ◽

Tree Height ◽

Absolute Error ◽

Forest Monitoring ◽

Individual Tree ◽

Structural Configurations ◽

Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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Design-based treatment of missing data in forest inventories using canopy heights from aerial laser scanning

Canadian Journal of Forest Research ◽

10.1139/cjfr-2013-0521 ◽

2014 ◽

Vol 44 (8) ◽

pp. 892-902 ◽

Cited By ~ 6

Author(s):

Piermaria Corona ◽

Gherardo Chirici ◽

Sara Franceschi ◽

Daniela Maffei ◽

Marzia Marcheselli ◽

...

Keyword(s):

Laser Scanning ◽

Central Italy ◽

Auxiliary Variable ◽

Sampling Variance ◽

Remotely Sensed Data ◽

Two Phase ◽

Aerial Laser Scanning ◽

Forest Inventories ◽

Airborne Laser ◽

Forest Survey

Nonresponse is often a problem in forest inventories. It may arise when sample plots are inaccessible because of hazardous terrain. To account for this problem, the use of nonresponse calibration weighting is considered in a complete design-based framework, i.e., both nonresponse and survey variables are viewed as fixed characteristics of the plots. Information derived from remotely sensed data is exploited to compensate for the missing plots. Calibration is performed adopting canopy height from airborne laser scanning as an auxiliary variable. Conditions for approximate unbiasedness of the calibration estimator in two-phase inventories are derived, and some estimators of the sampling variance are proposed. Results from one-phase inventories are achieved as a particular case. Dummy variables are introduced in the presence of different forest types. Monte Carlo results support the validity of the procedure. An application to a forest survey carried out in Central Italy is performed.

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Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Remote Sensing ◽

10.3390/rs12020247 ◽

2020 ◽

Vol 12 (2) ◽

pp. 247 ◽

Cited By ~ 1

Author(s):

Osian Roberts ◽

Pete Bunting ◽

Andy Hardy ◽

Daniel McInerney

Keyword(s):

Sensitivity Analysis ◽

Radiative Transfer ◽

Forest Inventory ◽

Laser Scanning ◽

Pulse Repetition Frequency ◽

Canopy Cover ◽

Airborne Laser Scanning ◽

Scanning Angle ◽

Forest Inventories ◽

Airborne Laser

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

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Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests

Interface Focus ◽

10.1098/rsfs.2017.0038 ◽

2018 ◽

Vol 8 (2) ◽

pp. 20170038 ◽

Cited By ~ 19

Author(s):

Sabina Roşca ◽

Juha Suomalainen ◽

Harm Bartholomeus ◽

Martin Herold

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Structure From Motion ◽

Laser Scanning ◽

Canopy Structure ◽

Terrestrial Laser Scanning ◽

Tree Height ◽

Forest Monitoring ◽

Forest Change ◽

Forest Inventories

Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) equipped with digital cameras have attracted much attention from the forestry community as potential tools for forest inventories and forest monitoring. This research fills a knowledge gap about the viability and dissimilarities of using these technologies for measuring the top of canopy structure in tropical forests. In an empirical study with data acquired in a Guyanese tropical forest, we assessed the differences between top of canopy models (TCMs) derived from TLS measurements and from UAV imagery, processed using structure from motion. Firstly, canopy gaps lead to differences in TCMs derived from TLS and UAVs. UAV TCMs overestimate canopy height in gap areas and often fail to represent smaller gaps altogether. Secondly, it was demonstrated that forest change caused by logging can be detected by both TLS and UAV TCMs, although it is better depicted by the TLS. Thirdly, this research shows that both TLS and UAV TCMs are sensitive to the small variations in sensor positions during data collection. TCMs rendered from UAV data acquired over the same area at different moments are more similar (RMSE 0.11–0.63 m for tree height, and 0.14–3.05 m for gap areas) than those rendered from TLS data (RMSE 0.21–1.21 m for trees, and 1.02–2.48 m for gaps). This study provides support for a more informed decision for choosing between TLS and UAV TCMs to assess top of canopy in a tropical forest by advancing our understanding on: (i) how these technologies capture the top of the canopy, (ii) why their ability to reproduce the same model varies over repeated surveying sessions and (iii) general considerations such as the area coverage, costs, fieldwork time and processing requirements needed.

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LIGHTER-THAN-AIR (LTA) UNMANNED AERIAL SYSTEM (UAS) CARRIER CONCEPT FOR SURVAILLANCE AND DISASTER MANAGEMENT

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1051 ◽

2019 ◽

Vol 3 ◽

pp. 1255

Author(s):

Ahmad Salahuddin Mohd Harithuddin ◽

Mohd Fazri Sedan ◽

Syaril Azrad Md Ali ◽

Shattri Mansor ◽

Hamid Reza Jifroudi ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Disaster Management ◽

Unmanned Aerial Systems ◽

Unmanned Aerial System ◽

Border Control ◽

Aerial Vehicles ◽

Operational Safety ◽

In Situ Methods ◽

Aerial Systems

Unmanned aerial systems (UAS) has many advantages in the fields of SURVAILLANCE and disaster management compared to space-borne observation, manned missions and in situ methods. The reasons include cost effectiveness, operational safety, and mission efficiency. This has in turn underlined the importance of UAS technology and highlighted a growing need in a more robust and efficient unmanned aerial vehicles to serve specific needs in SURVAILLANCE and disaster management. This paper first gives an overview on the framework for SURVAILLANCE particularly in applications of border control and disaster management and lists several phases of SURVAILLANCE and service descriptions. Based on this overview and SURVAILLANCE phases descriptions, we show the areas and services in which UAS can have significant advantage over traditional methods.

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Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation

Remote Sensing ◽

10.3390/rs12111808 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1808 ◽

Cited By ~ 1

Author(s):

Miłosz Mielcarek ◽

Agnieszka Kamińska ◽

Krzysztof Stereńczak

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Airborne Laser Scanning ◽

Surface Model ◽

Sources Of Information ◽

Related Factors ◽

Height Estimation ◽

Aerial Photogrammetry ◽

Airborne Laser ◽

The Difference

The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in estimating forest attributes in complex forest conditions. The main purpose of this study was to evaluate the accuracy of tree height estimation based on a crown height model (CHM) generated from the difference between a DAP-derived digital surface model (DSM) and an airborne laser scanning (ALS)-derived digital terrain model (DTM). The tree heights determined based on the DAP-CHM were compared with ground-based measurements and heights obtained using ALS data only (ALS-CHM). Moreover, tree- and stand-related factors were examined to evaluate the potential influence on the obtained discrepancies between ALS- and DAP-derived heights. The obtained results indicate that the differences between the means of field-measured heights and DAP-derived heights were statistically significant. The root mean square error (RMSE) calculated in the comparison of field heights and DAP-derived heights was 1.68 m (7.34%). The results obtained for the CHM generated using only ALS data produced slightly lower errors, with RMSE = 1.25 m (5.46%) on average. Both ALS and DAP displayed the tendency to underestimate tree heights compared to those measured in the field; however, DAP produced a higher bias (1.26 m) than ALS (0.88 m). Nevertheless, DAP heights were highly correlated with the heights measured in the field (R2 = 0.95) and ALS-derived heights (R2 = 0.97). Tree species and height difference (the difference between the reference tree height and mean tree height in a sample plot) had the greatest influence on the differences between ALS- and DAP-derived heights. Our study confirms that a CHM computed based on the difference between a DAP-derived DSM and an ALS-derived DTM can be successfully used to measure the height of trees in the upper canopy layer.

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A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal–Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning

Remote Sensing ◽

10.3390/rs11151804 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1804

Author(s):

Erik Næsset ◽

Terje Gobakken ◽

Ronald E. McRoberts

Keyword(s):

Laser Scanning ◽

Three Dimensional ◽

Laser Scanner ◽

Tree Height ◽

Residual Variance ◽

Tree Line ◽

Average Growth ◽

Vegetation Height ◽

Airborne Laser ◽

Height Change

The boreal tree line is in many places expected to advance upwards into the mountains due to climate change. This study aimed to develop a general method for estimation of vegetation height change in general, and change in tree height more specifically, for small geographical domains utilizing bi-temporal airborne laser scanner (ALS) data. The domains subject to estimation may subsequently be used to monitor vegetation and tree height change with detailed temporal and geographical resolutions. A method was developed with particular focus on statistically rigorous estimators of uncertainty for change estimates. The method employed model-dependent statistical inference. The method was demonstrated in a 12 ha study site in a boreal–alpine tree line in southeastern Norway, in which 316 trees were measured on the ground in 2006 and 2012 and ALS data were acquired in two temporally coincident campaigns. The trees ranged from 0.11 m to 5.20 m in height. Average growth in height was 0.19 m. Regression models were used to predict and estimate change. By following the area-based approach, predictions were produced for every individual 2 m2 population element that tessellated the study area. Two demonstrations of the method are provided in which separate height change estimates were calculated for domains of size 1.5 ha or greater. Differences in height change estimates among such small domains illustrate how change patterns may vary over the landscape. Model-dependent mean square error estimates for the height change estimators that accounted for (1) model parameter uncertainty, (2) residual variance, and (3) residual covariance are provided. Findings suggested that the two latter sources of uncertainty could be ignored in the uncertainty analysis. The proposed estimators are likely to work well for estimation of differences in height change along a gradient of small monitoring units, like the 1.5 ha cells used for demonstration purposes, and thus may potentially be used to monitor tree line migration over time.

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ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-629-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 629-632 ◽

Cited By ~ 1

Author(s):

E. Hadaś ◽

A. Borkowski ◽

J. Estornell

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Field Measurements ◽

Point Clouds ◽

Average Density ◽

Geometric Parameters ◽

Airborne Laser Scanning ◽

Maximum Height ◽

Airborne Laser ◽

Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter R. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points m<sip>−2. We noticed, that there was a narrow range of the R parameter, from 0.48 m to 0.80 m, for which all trees were detected and for which we obtained a high correlation coefficient (> 0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8 m for the tree height, 0.5 m for the crown base height, 0.6 m and 0.4 m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

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Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States

10.5194/tc-2020-37 ◽

2020 ◽

Author(s):

Jennifer M. Jacobs ◽

Adam G. Hunsaker ◽

Franklin B. Sullivan ◽

Michael Palace ◽

Elizabeth A. Burakowski ◽

...

Keyword(s):

Snow Depth ◽

Laser Scanning ◽

Coniferous Forest ◽

Potential Method ◽

Unmanned Aerial Systems ◽

Lidar System ◽

Aerial Vehicle ◽

Aerial Systems ◽

Lidar Observations

Abstract. Shallow snowpack conditions, which occur throughout the year in many regions as well as during accumulation and ablation periods in all regions, are important in water resources, agriculture, ecosystems, and winter recreation. Terrestrial and airborne (manned and unmanned) laser scanning and structure from motion (SfM) techniques have emerged as viable methods to map snow depths. Lidar on an unmanned aerial vehicle is also a potential method to observe field and slope scale variations of shallow snowpacks. This paper describes an unmanned aerial lidar system, which uses commercially available components, for snow depth mapping on the landscape scale. The system was assessed in a mixed deciduous and coniferous forest and open field for a shallow snowpack (

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Estimation of forest parameters using airborne laser scanning data

Geoscientific Instrumentation Methods and Data Systems Discussions ◽

10.5194/gid-5-549-2015 ◽

2015 ◽

Vol 5 (2) ◽

pp. 549-576

Author(s):

J. Cohen

Keyword(s):

Laser Scanning ◽

Tree Height ◽

National Forest Inventory ◽

Airborne Laser Scanning ◽

Forest Mapping ◽

Airborne Laser ◽

Forest Region ◽

Digital Elevation ◽

Industry Applications ◽

Elevation Model

Abstract. Methods for the estimation of forest characteristics by airborne laser scanning (ALS) data have been introduced by several authors. Tree height (TH) and canopy closure (CC) describing the forest properties can be used in forest, construction and industry applications, as well as research and decision making. The National Land Survey has been collecting ALS data from Finland since 2008 to generate a nationwide high resolution digital elevation model. Although this data has been collected in leaf-off conditions, it still has the potential to be utilized in forest mapping. A method where this data is used for the estimation of CC and TH in the boreal forest region is presented in this paper. Evaluation was conducted in eight test areas across Finland by comparing the results with corresponding Multi-Source National Forest Inventory (MS-NFI) datasets. The ALS based CC and TH maps were generally in a good agreement with the MS-NFI data. As expected, deciduous forests caused some underestimation in CC and TH, but the effect was not major in any of the test areas. The processing chain has been fully automated enabling fast generation of forest maps for different areas.

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