Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Chirici, Gherardo; Giannetti, Francesca; McRoberts, Ronald E.; Travaglini, Davide; Pecchi, Matteo; Maselli, Fabio; Chiesi, Marta; Corona, Piermaria

doi:10.1016/j.jag.2019.101959

Cited by 60 publications

(54 citation statements)

References 64 publications

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“…The 2 nd Italian National Forest Inventory (INFC 2007) is based on a three-phase, nonaligned, systematic sampling design, with results referred to the year 2005. More details on the Italian NFI are reported in Fattorini et al (2006) and Chirici et al (2020).…”

Section: Italian National Forest Inventory Reference Datamentioning

confidence: 99%

“…For this study we generated two reference grids for Italy, both projected using the coordinate system WGS 84 / UTM zone 32 North (EPSG:32632), at two different resolutions: 1 m and 23 m. The tessellation at 23 m was chosen to mimic the size of the field plots measured in the Italian NFI (Chirici et al 2020), and this generated a total of 569,769,690 cells. Following this approach all the raster layers potentially included in the geographical FIS should be resampled to the same 23 m resolution.…”

Section: Italian National Gridsmentioning

confidence: 99%

“…Despite the limitations found for some Italian Regions, our results show that REG-FM is the national forest mask most congruent with the forest area estimates of the Italian NFI. The Italian National Forest Mask could be useful for several applications, e.g., to create wall-to-wall spatial estimates of forest variables (Chirici et al 2020), to mask out non-forest areas when monitoring forest disturbances from clearcuts (Giannetti et al 2020 or windthrow damages, as well as for studying forest fragmentation and ecological networks at national scale level.…”

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 99%

“…Wall-to-wall maps from remotely sensed (RS) data can be used for the estimation of forest variables, also over small areas (Chirici et al 2020), e.g., growing stock volume (Saarela et al 2016), biomass (Naesset et al 2011), forest structural variables and diversity indices (Mura et al 2016). Among the different types of available RS technologies, Airborne Laser Scanner (ALS) emerged as the most effective to derive such maps and to support the development and parameterization of models for a broad range of information needed to enhance NFIs (Maltamo et al 2014, White et al 2016.…”

Section: Introductionmentioning

confidence: 99%

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Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

D’Amico¹,

Vangi²,

Francini³

et al. 2021

iForest

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Biogeosciences and Forestry Biogeosciences and Forestry Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy Giovanni D'Amico (1) , Elia Vangi (1-2) , Saverio Francini (1-2-3) , Francesca Giannetti (1-4-5) , Antonino Nicolaci (6) , Davide Travaglini (1) , Lorenzo Massai (4-5) , Yamuna Giambastiani (4-5-7) , Carlo Terranova (8) , Gherardo Chirici (1-5) Forest planning, forest management, and forest policy require updated, reliable, and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102,608.82 km 2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy.

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Section: Italian National Forest Inventory Reference Datamentioning

confidence: 99%

Section: Italian National Gridsmentioning

confidence: 99%

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

D’Amico¹,

Vangi²,

Francini³

et al. 2021

iForest

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“…The 4th NFI campaign in the Canary Islands and Extremadura used the TRIMBLE Juno 5B Handheld (Trimble Inc., USA), which can achieve real-time positioning accuracies between 2-4 m that can decrease to 1-3 m in the post-processing. As a showcase example, similar GNSS technology has been recently used in the NFI of Italy [35]. In the case of the NFI of Spain, Fernández-Landa et al (2018) [3] also observed a mean error of 8.6 m, using the 4th NFI data from Spain, between the nominal coordinates available from previous rounds of the NFI of Spain and the new coordinates measured with commercial-grade GNSS equipment.…”

Section: Positioning Equipment and Co-registration Assessmentmentioning

confidence: 98%

The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning

et al. 2020

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The level of spatial co-registration between airborne laser scanning (ALS) and ground data can determine the goodness of the statistical inference used in forest inventories. The importance of positioning methods in the field can increase, depending on the structural complexity of forests. An area-based approach was followed to conduct forest inventory over seven National Forest Inventory (NFI) forest strata in Spain. The benefit of improving the co-registration goodness was assessed through model transferability using low- and high-accuracy positioning methods. Through the inoptimality losses approach, we evaluated the value of good co-registered data, while assessing the influence of forest structural complexity. When using good co-registered data in the 4th NFI, the mean tree height (HTmean), stand basal area (G) and growing stock volume (V) models were 2.6%, 10.6% and 14.7% (in terms of root mean squared error, RMSE %), lower than when using the coordinates from the 3rd NFI. Transferring models built under poor co-registration conditions using more precise data improved the models, on average, 0.3%, 6.0% and 8.8%, while the worsening effect of using low-accuracy data with models built in optimal conditions reached 4.0%, 16.1% and 16.2%. The value of enhanced data co-registration varied between forests. The usability of current NFI data under modern forest inventory approaches can be restricted when combining with ALS data. As this research showed, investing in improving co-registration goodness over a set of samples in NFI projects enhanced model performance, depending on the type of forest and on the assessed forest attributes.

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From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources

et al. 2022

Self Cite

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A completely data‐driven, design‐based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least‐squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave‐one‐out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described.

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Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Cited by 60 publications

References 64 publications

Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy

The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning

From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources

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