Harmonized forest categories in central Italy

Chiavetta, Ugo; Camarretta, Nicolò; Garfì, Vittorio; Ottaviano, Marco; Chirici, Gherardo; Vizzarri, Matteo; Marchetti, Marco

doi:10.1080/17445647.2016.1161437

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…We hope that this could encourage the launch of an operational project for implementing an online National Geographic Forest Information System in Italy. We also recommend that the different forest mapping and monitoring programs currently active in Italy converge on a common nomenclature system, thus facilitating the production of harmonized maps (Chiavetta et al 2016). For this purpose, we suggest the adoption of the European Forest Types nomenclature systems (Barbati et al 2014), which covers all the extant forest types in Italy.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

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 instruments onboard the platform include a Hyperspectral Imager, able to capture images in a continuum of 239 spectral bands ranging between 400 and 2500 nm, 66 in the Visible Near Infra Red (VNIR) and 173 in the Short Wave Infra Red (SWIR) spectrum, with a spectral resolution smaller than 12 nm, and a spatial resolution of 30 m. Nine bands are acquired in a wavelength overlapping region between the VNIR and the SWIR cube. The spacecraft also carries a 5 m resolution Panchromatic Camera [35]. The images can be acquired in an area of interest spanning from 180 • W to 180 • E longitude and 70 • N to 70 • S latitude.…”

Section: Overview Of the Prisma Mission And Instrumentsmentioning

confidence: 99%

“…PRISMA images can be freely downloaded after registration at http://prisma-i.it/ index.php/en/ (accessed on 5 February 2021) (Figure 1). They can be released with three levels of preprocessing [35]:…”

Section: Preprocessing Levels Of Prisma Hyperspectral Cubesmentioning

confidence: 99%

“…PRISMA images can be freely downloaded after registration at (accessed on 5 February 2021) ( Figure 1 ). They can be released with three levels of preprocessing [ 35 ]: Level0: The L0 product contains raw data in binary files, including instrument and satellite ancillary data, like the cloud cover percentage. Level1: The L1 product is a top-of-atmosphere radiance imagery organized as follows: two radiometrically calibrated hyperspectral and panchromatic radiance cubes and two co-registered HYPER and PAN radiance cubes.…”

Section: Introductionmentioning

confidence: 99%

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The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Vangi

D’Amico

Francini

et al. 2021

Sensors

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Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.

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“…PRISMA images are characterized by a continuum of 240 spectral bands with 10 nm spectral resolution over the spectral region of 400 to 2500 nm, 66 in the Visible Near Infra-Red (VNIR) and 173 in the Short Wave Infra-Red (SWIR) spectrum and acquired at 30 m spatial resolution with approximate 200:1 Signal to Noise Ratio. The mission's goal is to determine whether the PRISMA sensor can be successfully used to monitor natural resources and atmospheric characteristics, as well as to assess potential new applications for environmental risk management and land observation [51][52][53][54].…”

Section: Hyperspectral Prisma Image Data Acquisition and Processingmentioning

confidence: 99%

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray¹,

Elmannai²,

Toumi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) for the prediction of several soil properties, including clay, sand, silt, organic matter, nitrate NO3-, and calcium carbonate CaCO 3 , using five VNIR spectra dataset combinations (% IS, % LS) as follows: C1 (0% IS, 100% LS), C2 (20% IS, 80% LS), C3 (50% IS, 50% LS), C4 (80% IS, 20% LS) and C5 (100% IS, 0% LS). Soil samples were collected at bare soils and at the upper (0-30 cm) layer. The data set has been split into a training dataset 80% of the collected data (n = 248) and a validation dataset 20% of the collected data (n = 61). The proposed PLSR and SVR models were trained then tested for each dataset combination. According to our results, SVR outperforms PLSR for both: C1 (0% IS, 100% LS) and C5 (100% IS, 0% LS). For Soil Organic Matter (SOM) prediction, it achieves (R 2 = 0.79%, RMSE = 1.42%) and (R 2 = 0.76%, RMSE = 1.3%), respectively. The data fusion has improved the soil property prediction. The highest improvement was obtained for the SOM property (R 2 = 0.80%, RMSE = 1.39) when using the SVR model and applying the second Combination C2 (20% of IS and 80% LS).

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Harmonized forest categories in central Italy

Cited by 8 publications

References 4 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 New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

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