Refining and evaluating a Horvitz– Thompson-like stand density estimator in individual tree detection based on airborne laser scanning

2021 ◽  
Author(s):  
Kasper Kansanen ◽  
Petteri Packalen ◽  
Timo Lähivaara ◽  
Aku Seppänen ◽  
Jari Vauhkonen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Tuning Parameter ◽  
Density Estimator ◽  
Validation Data ◽  
Data Set ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser

Horvitz--Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than the previous papers and present and discuss the methods for estimating the tuning parameter of the estimator using a functional $k$-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a $13$\% lower RMSE than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data to the results.

Individual tree detection and crown segmentation based on metabolic theory from airborne laser scanning data

2021 ◽  
Vol 15 (03) ◽  
Author(s):  
Honglu Xin ◽  
Yadvinder Malhi ◽  
David A. Coomes ◽  
Yi Lin ◽  
Baoli Liu ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Metabolic Theory ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser

scholarly journals The transferability of airborne laser scanning based tree-level models between different inventory areas

2019 ◽  
Vol 49 (3) ◽  
pp. 228-236 ◽  
Author(s):  
Tomi Karjalainen ◽  
Lauri Korhonen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Nearest Neighbor ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Stem Volume ◽  
K Nearest Neighbor ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands

2011 ◽  
Vol 41 (3) ◽  
pp. 583-598 ◽  
Author(s):  
Jussi Peuhkurinen ◽  
Lauri Mehtätalo ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Statistical Approach ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Tree Size ◽  
Size Distributions ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Number Of Stems

Airborne laser scanning based forest inventories employ two major methods: individual tree detection (ITD) and the area-based statistical approach (ABSA). ITD is based on the assumption that trees are of a certain form and can be delineated using airborne laser scanning techniques, whereas ABSA is an empirical method based on the relations between area-level forest attributes and laser echo height distributions. These two methods are compared here within the same test area in terms of their usefulness for estimating mean forest stand characteristics and tree size distributions. All evaluations were performed using leave-one-out cross validation. The average errors in volume and basal area did not differ significantly between the methods. ABSA resulted in overall better accuracies when estimating the diameter and height of the basal area median tree and the number of stems, whereas ITD produced significantly biased estimates for the number of stems and the mean tree size. Tree size distributions were estimated with slightly better accuracy using ABSA. More comprehensive investigations revealed that both methods were not able to estimate forest structure (tree size distribution and spatial distribution of tree locations), which in turn, affected the estimation accuracies.

scholarly journals Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest

2021 ◽  
Vol 13 (14) ◽  
pp. 2753
Author(s):  
Łukasz Kolendo ◽  
Marcin Kozniewski ◽  
Marek Ksepko ◽  
Szymon Chmur ◽  
Bożydar Neroj
Keyword(s):  
Laser Scanning ◽  
Coniferous Forest ◽  
Local Maximum ◽  
Airborne Laser Scanning ◽  
Percentage Error ◽  
Practical Implementation ◽  
Individual Tree ◽  
Ground Sample ◽  
Tree Detection ◽  
Airborne Laser

Highly accurate and extensive datasets are needed for the practical implementation of precision forestry as a method of forest ecosystem management. Proper processing of huge datasets involves the necessity of the appropriate selection of methods for their analysis and optimization. In this paper, we propose a concept for and implementation of a data preprocessing algorithm, and a method for the empirical verification of selected individual tree detection (ITD) algorithms, based on Airborne Laser Scanning (ALS) data. In our study, we used ALS data and very extensive dendrometric field measurements (including over 21,000 trees on 522 circular sample plots) in the economic and protective coniferous stands of north-eastern Poland. Our algorithm deals well with the overestimation problems of tree top detection. Furthermore, we analyzed segmentation parameters for the two currently dominant ITD methods: Watershed (WS) and Local Maximum Filter with Growing Region (LMF+GR). We optimized them with respect to minimizing the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, our results show the crucial importance of the quality of empirical data for the correct evaluation of the accuracy of ITD algorithms.

scholarly journals Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 268
Author(s):  
Jan Novotný ◽  
Barbora Navrátilová ◽  
Růžena Janoutová ◽  
Filip Oulehle ◽  
Lucie Homolová
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Airborne Laser Scanning ◽  
Allometric Equations ◽  
Spruce Forest ◽  
Individual Tree ◽  
Detection Rates ◽  
Site Specific ◽  
Tree Detection ◽  
Airborne Laser

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

scholarly journals AUTOMATED INDIVIDUAL TREE DETECTION IN AMAZON TROPICAL FOREST FROM AIRBORNE LASER SCANNING DATA

CERNE ◽  
2019 ◽  
Vol 25 (3) ◽  
pp. 273-282
Author(s):  
PEDRO HENRIQUE KARANTINO MILLIKAN ◽  
CARLOS ALBERTO SILVA ◽  
Luiz Carlos Estraviz Rodriguez ◽  
Tupiara Mergen de Oliveira ◽  
Mariana Peres de Lima Chaves e Carvalho ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser
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