Effects of field plot configurations on the uncertainties of ALS-assisted forest resource estimates

Tomppo, Erkki; Kuusinen, Nea; Mäkisara, Kai; Katila, Matti; McRoberts, Ronald E.

doi:10.1080/02827581.2016.1259425

Cited by 25 publications

(27 citation statements)

References 21 publications

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“…The same set of textural features was extracted from aerial images with a pixel lag of 2.7 meters additionally to the lag of 5.7 meters used by Tomppo et al (2016).…”

Section: Extraction Of Remote-sensing Featuresmentioning

confidence: 99%

“…Pulse returns with heights greater than 1.3 m above the ground were classified as canopy. The DTM was produced in two phases ( Tomppo et al 2016). In the first phase the area was divided into 8 × 8 m grid cells and each grid cell's ground elevation was calculated using the average elevation above sea level of the ground points inside the grid cell if there were at least three ground points.…”

Section: Remote Sensing Datamentioning

confidence: 99%

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Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Tuominen¹,

Pitkänen²,

Balazs³

et al. 2017

Silva Fenn.

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Highlights• 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories.• Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables.• Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery. AbstractOptical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials.On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower shortrange correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

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“…The same set of textural features was extracted from aerial images with a pixel lag of 2.7 meters additionally to the lag of 5.7 meters used by Tomppo et al (2016).…”

Section: Extraction Of Remote-sensing Featuresmentioning

confidence: 99%

Section: Remote Sensing Datamentioning

confidence: 99%

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Tuominen¹,

Pitkänen²,

Balazs³

et al. 2017

Silva Fenn.

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“…I dati di volume riferiti all'ettaro sono stati ottenuti mediante "ricostruzione del popolamento virtuale" secondo il metodo di Bitterlich (1984). L'idoneità delle prove di numerazione angolare per la calibrazione di modelli che usino dati telerilevati, soprattutto quando la variabile in stima sia il volume, è stata dimostrata in diversi studi (Hollaus et al 2007, Maltamo et al 2007, Jochem et al 2011, Tonolli et al 2011, Scrinzi et al 2015, Tomppo et al 2016.…”

Section: Rilievi Dendrometrici Sul Terrenounclassified

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Scrinzi¹,

Floris²,

Clementel³

et al. 2017

Forest@

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Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy). The AlForLab project is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region. Estimating the main dendrometric variables of Calabrian forests using models based on publicly available remote sensed data is one of the main purposes of the project. This paper describes the procedures used to develop several thematic maps (raster and vector) of timber volume and phytomass to be used in planning and management activities at both regional and forest property scale, as well as for felling plans, logging projects etc. We used public LiDAR data at medium-low resolution (1.6 pts m -2 ), acquired on about 90% of Calabrian territory in the frame of a national remote sensing programme of the Italian Ministry of the Environment. Field data from the second National Forest Inventory (INFC 2005) on 311 sample points were used for model calibration, as well as new field data acquired specifically for AlForLab project on 143 angle count samples. A series of regression models to predict volume and its corresponding aboveground biomass (dry and fresh weight) were developed and digital maps at different spatial resolutions were produced, as well as their estimate uncertainties. These models and their mapping products are also an important part of the new-establishing forest Decision Support System CFOR. The adopted models, though based on the same mathematical equation, have specific coefficients for different species and groups of species, according to a forest type classification system compatible with the fourth level of Corine Land Cover. In this way it is possible to apply the models without accessing more detailed forest type maps. All estimation methods and procedures are consistent with national forest inventory models, and with the other new tools proposed by AlForLab project to estimate timber volume, such as the regional tariffs and the field sampling inventory procedures. R 2 adjusted values (for models at the highest typological detail) are between 60% and 85%, whereas uncertainties of timber volume estimate (ESS%) range from 25% (for main forest species) up to 50% (for less spread forest types). All processing steps to produce digital maps were performed on open-source environment (R and QGIS).Keywords: ALS, LiDAR, Timber Volume, Forest Biomass, Estimation Models, CHM, AlForLab Received: Feb 09, 2017; Accepted: Apr 19, 2017; Published online: May 15, 2017 Citation: Scrinzi G, Floris A, Clementel F, Bernardini V, Chianucci F, Greco S, Michelini T, Penasa A, Puletti N, Rizzo M, Turco R, Corona P, 2017. Modelli di stima del volume e delle fitomasse del soprassuolo arboreo delle principali formazioni forestali della Calabria mediante dati LiDAR. Forest@ 14: 175-187 [online 2017-05-15]

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“…This enables the use of NFI sample plot data as field reference data with the remote sensing material of high spatial resolution, such as aerial imagery and ALS data. Also the change from angle-count tree sampling to fixed size sample plots in 2012 improved the applicability of NFI plots as reference data for ALS, though the effect of plot configurations on the error of ALS-based estimates was found to be small (Maltamo et al 2009;Tuominen et al 2014;Tomppo et al 2016). However, image acquisitions or ALS over large areas in various conditions, at different times of the growing season, or even under leaf-off conditions and with different devices complicate the use of NFI sample plots, because the field data should cover the entire variation of forest attributes in the area of interest, that is, within an image.…”

Section: Further Aspects Of Availability Of Forest Data For Scenario mentioning

confidence: 99%

Multi-source forest inventory data for forest production and utilization analyses at different levels

Haakana¹

2017

Diss. For.

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National forest inventory (NFI) data are commonly used in national and regional scenario analyses on forest production and utilization possibilities. There is an increased demand for similar analyses at the sub-regional level, and further, to incorporate spatially explicit data into the analyses. However, the fairly sparse network of NFI sample plots allows analyses only for large areas. The present dissertation explored whether satellite imagery, NFI sample plot data and the k nearest neighbour estimation method can be employed in generating spatial forest data for scenario analyses at the local level. The method was first applied in the area of two villages in Eastern Finland to quantify the effects of administrative land use and technical land-form constraints on timber production. Secondly, the impacts of three alternative regional felling strategies on suitable habitat for the Siberian flying squirrel (Pteromys volans) were assessed.As a scenario analysis tool, the Finnish forestry dynamics model MELA was used. Management units for simulations of forest development and management activities were delineated by means of image segmentation and digital maps on restriction areas, and new weights for NFI sample plots, that is, the representativeness in these units, were estimated by means of satellite image data. The performance of different segmentation methods and different spectral features in the estimation were examined. Image segments corresponding to forest stands enabled the use of patch-and landscape-level models in the prediction of suitable habitat.Satellite image-based estimation of new NFI sample plot weights was found to be a feasible method for generating forest data for scenario analyses in areas smaller than is possible with the plot data only, for example, for municipalities. Satellite imagery with large geographic coverage and continuous NFI field measurements provide cost-efficient data sources for versatile impact and scenario analyses at the local level.

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Effects of field plot configurations on the uncertainties of ALS-assisted forest resource estimates

Cited by 25 publications

References 21 publications

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy)

Multi-source forest inventory data for forest production and utilization analyses at different levels

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