Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data

Teobaldelli, Maurizio; Cona, Francesco; Saulino, Luigi; Migliozzi, Antonello; D’Urso, Guido; Langella, Giuliano; Manna, Piero; Saracino, Antonio

doi:10.1016/j.rse.2017.02.008

Cited by 22 publications

(24 citation statements)

References 56 publications

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“…This pattern is consistent with those observed in other studies in relation to the prevalence of height variables (especially high percentiles) as key elements for extracting information from LiDAR data [19,38,70]. Analysis of the relative importance of the variables indicated a normal distribution skewed towards the top of the tree canopy profile of E. globulus and P. pinaster plantations (h95 and h80 percentiles), together with the symmetrical normal distribution of canopy elements in P. radiata plantations (h75 and h50 percentiles); these results are consistent with the observations of Teobaldelli et al [8]. According to the results obtained for other pure coniferous forests [38,[71][72][73], the set of models confirmed that the combination of height and canopy cover metrics represents a sufficient and concise quantitative description of a homogeneous vertical structure of the species analyzed.…”

Section: Discussionsupporting

confidence: 89%

“…Remote sensing systems have proved to be an effective option for overcoming the above-mentioned limitations and enabling forest data to be obtained in large areas with a more reasonable effort [6] and even in areas not previously sampled [1]. Among these systems, Airborne Laser Scanning (ALS), an active remote sensing methodology which transmits pulses of laser light towards the ground, is recognized to be an accurate, efficient and cost-effective approach to predicting forest variables such as stand height, volume and biomass [4,7,8]. In a practical applications of ALS data, georeferenced plots are first used to develop empirical models of the relationships between field measurements and ALS-derived metrics, and the models are then applied to the entire area of interest, thus predicting the forest attributes on the basis of ALS metrics alone [1].…”

Section: Introductionmentioning

confidence: 99%

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Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

et al. 2019

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The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8-38.3%, 34.2-41.9% and 31.7-38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

et al. 2019

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“…In summary, the more effective and accurate approach to estimate AGB by means of UAVs in natural TMF is the RGB alternative, applying an analysis at tree level. During the process, forest structure as well as individual H and DBH of each tree detected can be determined [101]. These parameters, in combination with mean WD of the specific forest type, allowed a realistic AGB calculation.…”

Section: Discussionmentioning

confidence: 99%

AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019

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The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

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“…The study area (Figure 1) is the Telesina Valley (~20,000 ha, 41 • 12 59.37" N, 14 • 31 33.43" E in southern Apennines, Italy), featuring a cultivated flat area, crossed by the Calore river and lying between the northern and southern slopes respectively by the Matese mountain chain and the isolated calcareous Taburno-Camposauro massif. The Telesina Valley represents a mosaic of different vegetation and land use types, including Mediterranean broadleaf-evergreen and deciduous-forests, conifer plantations, pasture grasslands, vineyards, olive groves and urban settlements [37]. The territory has a large forestry landscape (27.7% of the study area) and it is also known as suitable for the production of high-quality agriculture.…”

Section: The Study Areamentioning

confidence: 99%

“…Within SOILCONSWEB activities, discrete-return aerial LiDAR data, collected during 2011 leaf-off condition were used to distinguish forest stand parameters and structural diversity in the study area. A detailed methodological overview of the adopted procedures for LiDAR-derived vegetation indexes computation as well as non-parametric bootstrap resampling methods [40] used to validate the regression models of LiDAR metrics vs. field data can be found in Teobaldelli [37].…”

Section: Lidar Datamentioning

confidence: 99%

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

Marano

Langella

Basile

et al. 2019

Forests

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Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies.

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Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data

Cited by 22 publications

References 56 publications

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

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