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

Chirici, G., Giannetti, F., Travaglini, D.,

McRoberts, R., Maselli, F., Chiesi, M.,

Pecchi, M., Corona, P.

Laboratory of Forest Geomatics

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Introduction

National Forest Inventory sampling data

Wall-to-wallspatial

predictionRemote Sensing data

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+Small-area estimation

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Mapping

• NORDIC EUROPEAN COUNTRIES: Sweden, Finland, Denmark, Norway (Næsset et al., 2004; Nord-Larsen and Schumacher, 2012; Tomppo et al., 2008)

• NORTH AMERICA CONTINENT: Canada (Boudreau et al., 2008; Matasci et al., 2018), USA (Blackardet al., 2008)

• CENTRAL EUROPEAN COUNTRIES: Austria (Hollaus et al., 2009) and Switzerland (Waser et al., 2017, 2015)

are nowadays integrating the classical design-based sampling-based inventories with forest mapping using remote sensing

Forest cover Growing Stock Volume Forest area Biomass

Multispectral satellite images

LiDAR Photogrammetric data

Introduction

VARIABLES TO BE PREDICTED PREDICTORS

And in Mediterranean forests?Tests carried out in study sites

ITALY:Chirici et al., 2008; Chirici et al., 2016; Mura et al., 2017SPAIN: Fernández-Landa et al., 2018GREECE: Crysafis et al., 2017

Introduction

Moderate Resolution National Growing Stock Map of Italy

NATIONAL FOREST INVENTORY PLOTS WERE NOT AVAILABLE

Local forest inventory plots Predictors: MODIS + GLAS

canopy height model and the CORINE land cover maps

Grid resolution 1 km x 1 km

Introduction

INFC 20056.782 plot230.875 measured trees

Italian National Forestry and carbon Inventory

(INFC) were became open-access and freely

available on-line in 2016 as a spatial database at https://www.inventarioforestale.org/ (Borghettiand Chirici, 2016)

Introduction

New Possibilities

Unalligned systematic sampling design

Aims

The current study aimed atproducing:• set up the best option for wall-to-wall

estimates of forest growing stock (GSV) inItaly

• to do so we tested several approachesin a large test area (i.e. 48 657 km2) in Italyby combining remotely sensed, ancillaryvariables and INFC plots

• GSV spatial estimation with a high spatial

resolution (23x23 m)

• small-area model-assisted estimation

Study area

extend of 48 657 km2

more than 16 248 km2 of forest and other

wood land (INFC, 2004), 1350 plots

Complex environment: precipitation ranging

between 3000 mm and 600 mm, mean annual temperature ranging between 6 and 16 °C

Complex species composition: downy oak,

pedunculated oak, Turkey oak, and sessile oak. With beech and chestnut. Coniferous species mainly from artificial plantations (maritime pine, black pine, white fir, douglas fir).

Complex structure: high forests and coppice (88%)

Study area

GSV distribution in INFC2005 plots

NUMBER OF PLOT 1350

Poor in GSV!

“Italy is rich of poor forests” (Ciancio, 2000)

Landsat 7 ETM+ vs. IMAGE2006 (IRS+SPOT)

CHM from LiDAR GLONASS-ICESAT

PALSAR JASA

DTM

Climate Variables

Soil Variables

Available Predictors

projectionFUSO_1 zone (Est=East, Ovest=West), Gauss-BoagaprojectionCODISTAT05 administrative region, Italian National Statistics codeNOMEDIST05 administrative region, nameCODCFOR forest category, codeLAND_B1: feature variable Landsat 7 ETM+ B1 from the single pixelLAND_B2: feature variable Landsat 7 ETM+ B2 from the single pixelLAND_B3: feature variable Landsat 7 ETM+ B3 from the single pixelLAND_B4: feature variable Landsat 7 ETM+ B4 from the single pixelLAND_B5: feature variable Landsat 7 ETM+ B5 from the single pixelLAND_B6: feature variable Landsat 7 ETM+ B6 from the single pixelLAND_B7: feature variable Landsat 7 ETM+ B7 from the single pixelLAND_B1_DF: feature variable Landsat 7 ETM+ B1 from 3x3 windowLAND_B2_DF: feature variable Landsat 7 ETM+ B2 from 3x3 windowLAND_B3_DF: feature variable Landsat 7 ETM+ B3 from 3x3 windowLAND_B4_DF: feature variable Landsat 7 ETM+ B4 from 3x3 windowLAND_B5_DF: feature variable Landsat 7 ETM+ B5 from 3x3 windowLAND_B6_DF: feature variable Landsat 7 ETM+ B6 from 3x3 windowLAND_B7_DF: feature variable Landsat 7 ETM+ B7 from 3x3 windowIMAGE2006_: feature variable IRS/SPOT B1 from the single pixelIMAGE20061: feature variable IRS/SPOT B2 from the single pixelIMAGE20062: feature variable IRS/SPOT B3 from the single pixelIMAGE20063: feature variable IRS/SPOT B4 from the single pixelIMAGE20064: feature variable IRS/SPOT B1 from 3x3 windowIMAGE20065: feature variable IRS/SPOT B2 from 3x3 windowIMAGE20066: feature variable IRS/SPOT B3 from 3x3 windowIMAGE20067: feature variable IRS/SPOT B4 from 3x3 windowDTM: feature variable elevationCOPERNICUS: feature variable percent crown coverradar_HV: feature variable radar PALSAR HV polarizationradar_HH: feature variable radar PALSAR HH polarizationSLOPE: feature variable slopeh_prec: feature variable annual total precipitation (average past 30 years)temp_med: feature variable mean annual temperature (average past 30 years)tmax_med: feature variable average of maximum temperatures (average past 30 years)tmin_med: feature variable average of minimum temperatures (average past 30 years)AWC_SUB_P: feature variable subsoil available water capacityAWC_TOP_P: feature variable topsoil available water capacityVS_P: feature variable volume of stonesDR_P Depth to rockCEC_SUB_P Subsoil cation exchange capacity.CEC_TOP_P Topsoil cation exchange capacity.DIMP_P

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Pre

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IMAGE2006IRS LISS IIISPOT HRG20 mcloud freenormalized

Landsat 7 ETM+200530 mcloud freenormalized

PALSAR ALOSHV, HH200625 m

4 Different predicting models:

+ k-NN+Random Forest

Non-parametrics

Parametrics

+ Multivariate Linear Regression+ Locally spatially weighted regression

Leave one out accuracy evaluation at plot levelR2, RMSE, RMSE%, BIAS, BIAS%

Most accurate for spatial estimation GSV

GSV INFC PLOTS Remote Sensing data

AREA BASED APPROCH

Accuracy assessment of GSV map. With independent dataset (stand level information derived by forest management plans)

Model assisted estimation (GREG). Small-scale estimation at province level of GSV

MET

HO

DS

Result – selected predictors by

different imputation

Results – different imputation methods

The performance of the different imputations

were evaluated using the leave-one-out

(LOO) cross validation. We calculate for each

model the coefficient of determination (R2)

between the measured and predicted values,

and the root main square error (RMSE) :

𝑅𝑀𝑆𝐸 = 𝑖=1

𝑛 (𝑦𝑖 − 𝑦𝑖)2

𝑛

Predicted vs observed Leave one out

Random Forest wall-to-wall prediction

The independent error was calculated on the basis of forest management data R2 = 0.62 and RMSE40 m3ha-1

Independent validation

LOO R2 = 0.61 and RMSE 52 m3ha-1

Random Forest wall-to-wall prediction

National application

848 Lansat 5 TM images: 312 2004, 282 2005, 254 2006Best Available Pixel in Google Earth Engine

Wall-to-wall spatial prediction as

a bridge between National Forest Inventory and local forest management

Small-area estimations: model-assisted generalized regression (GREG) estimators

The map-based estimate of GSV area was:

𝑆𝐸 𝜇𝐺𝑅𝐸𝐺 = 𝑉 𝑎𝑟 𝜇𝐺𝑅𝐸𝐺 =1

𝑛(𝑛−1) 𝑖=1

𝑛 (𝑒𝑖 − 𝑒)2 were 𝑒𝑖 = ( 𝑦𝑖−𝑦𝑖) and 𝑒 =1

𝑛 𝑖=1

𝑛 𝑒𝑖

Forest pixel

N= total number of population units

𝑦𝑖 = the model prediction of GSV for the j-th population unit

𝐵 𝑖𝑎𝑠( 𝜇𝑚𝑎𝑝) =1

𝑛

𝑖=1

𝑛

( 𝑦𝑖−𝑦𝑖)

𝑦𝑖 = the observed value of GSV for the i-th plot

n = number of population units observed

𝜇𝐺𝑅𝐸𝐺 = 𝜇𝑚𝑎𝑝 − 𝐵 𝑖𝑎𝑠( 𝜇𝑚𝑎𝑝)

Forest pixels with field observation

No Forest pixel 𝜇𝑚𝑎𝑝 =1

𝑁

𝑗=1

𝑁

𝑦𝑖

(Breidt and Opsomer, 2009; McRoberts et al., 2016)

23m

23

m

model-assisted, generalized regression (GREG) estimators

Region Province Province Area(Km2)

Total Forest Area (ha)(INFC, 2005)

SE Total Forest Area (%) (INFC, 2005)

ni GSV 𝜇𝐺𝑅𝐸𝐺

GSV𝑆𝐸 𝜇𝐺𝑅𝐸𝐺

Tuscany Arezzo 323300 179219 4.2 127 111.13 8.5Firenze 351369 178500 4.2 117 151.89 12.3Grosseto 450312 197961 4.0 116 98.35 8.6Livorno 121371 47364 8.6 23 108.88 16.7Lucca 177322 121044 5.2 64 198.86 13.1Massa Carrara 115468 86713 6.2 30 148.69 14.69Pisa 244472 95053 6.0 54 98.82 11.28Pistoia 96412 50640 8.3 32 214.30 43.91Prato 36572 23334 12.3 13 186.87 24.1Siena 38298 171710 4.3 115 85.81 6.09

Emilia-Romagna

Bologna 370232 100761 5.6 56 112.60 11.12Forlì-Cesena 237840 106621 5.5 70 86.08 15.24Modena 268802 68695 7.0 49 123.97 14.97Parma 344748 152542 4.4 85 170.81 9.94Piacenza 258586 84837 6.2 51 111.66 13.07Ravenna 185944 21332 13.0 19 80.55 12.39Reggio Emilia 229126 63518 7.3 58 126.75 11.26

Liguria La Spezia 88135 54229 7.6 46 144.55 16.34

SMALL AREA ESTIMATION

Conclusions The estimation of growing stock volume using INFC2005

plot and remote sensing auxiliary variables appears

feasible at country level also in complex

Mediterranean environments and without LiDAR data

The accuracy calculated at single pixel level is useful ONLY for in the optimization phase and to choose the estimation method

The results will be produced in aggregated form (Province, Municipality, forest parcels) -> SMALL AREA ESTIMATION

The Landsat data are confirmed the best type of

predictive variables from optical satellite for vast areas

Different methods achieve similar performance, limited commission values

Future predictions integrating LiDAR