Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

Latifi, Hooman; Heurich, Marco

doi:10.3390/rs11111260

Cited by 17 publications

(14 citation statements)

References 18 publications

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“…Forest inventories based on remote sensing data vary in terms of scale, sensors, and calculated forest structural attributes. For example, Latifi and Heurich [1] reported that lidar point clouds advantageously fused with optical imagery are the most prominent choices for inventory of forest structural variables at the landscape and regional scales. Spatial distribution of trees and standing or fallen dead trees are key forest variables for estimation of forest attributes such as aboveground biomass and growing stock.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Krzystek

Serebryanyk²,

Schnörr

et al. 2020

Remote Sensing

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Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Krzystek

Serebryanyk²,

Schnörr

et al. 2020

Remote Sensing

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show abstract

“…Forest inventories based on remote sensing data, particularly lidar point clouds fused with optical imagery, are the most prominent options for the inventory of forest structural variables (Latifi & Heurich, 2019). Forest attributes such as aboveground biomass and growing stock can be estimated from the spatial distribution of tree species and dead wood.…”

Section: Introductionmentioning

confidence: 99%

Classification of Tree Species and Standing Dead Trees by Fusing Uav-Based Lidar Data and Multispectral Imagery in the 3d Deep Neural Network Pointnet++

Briechle

Krzystek

Vosselman

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Knowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice.

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“…Additionally, lidar facilitates precise 3D mapping of forests at the tree level (Reitberger et al, 2008). In combination with MS imagery, lidar has demonstrated promising potential for the calculation of forest structural variables (Latifi and Heurich, 2019).…”

Section: Key Idea and Applicationsmentioning

confidence: 99%

“…Airplanes, helicopters, and innovative platforms such as UAVs equipped with lidar sensors and MS or hyperspectral cameras enable acquisition of high-resolution data from a bird's eye view. In particular, the fusion of lidar point clouds and optical multi-channel imagery is the most prominent option for the inventory of forest structural variables (Latifi and Heurich, 2019). In a preprocessing step, single trees are typically delineated from ALS data.…”

Section: Conventional Approachesmentioning

confidence: 99%

“…Forest inventories based on remote sensing data, particularly lidar point clouds fused with optical imagery, are the most prominent options for the inventory of forest structural variables (Latifi and Heurich, 2019). Forest attributes such as above-ground biomass and growing stock can be estimated from the spatial distribution of tree species and dead wood.…”

Section: Introductionmentioning

confidence: 99%

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UAV-based high-resolution remote sensing and machine learning for risk management in hazard and disaster areas

Briechle

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Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

Cited by 17 publications

References 18 publications

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Classification of Tree Species and Standing Dead Trees by Fusing Uav-Based Lidar Data and Multispectral Imagery in the 3d Deep Neural Network Pointnet++

UAV-based high-resolution remote sensing and machine learning for risk management in hazard and disaster areas

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