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JRC study on harvested forest area: Resolving key misunderstandings

May 2021
iForest - Biogeosciences and Forestry 14(3)

DOI:10.3832/ifor0059-014

Authors:

Giacomo Grassi

European Commission

Alessandro Cescatti

European Commission

Guido Ceccherini

European Commission

A recent study on forest harvest in the EU (Ceccherini et al. 2020) reported a strong increase in clear-cut harvested area in recent years, based on remote sensing information. This triggered a heated debate and many critical comments. Apart from several fair and constructive criticisms, which were welcome, we found that some comments have been either not based on evidence or affected by serious misunderstandings. Here we clarify some technical aspects that were omitted or misrepresented in the public debate. Overall, the original study used in a scientifically correct way the best information available at that time. After the study was published, a previously undocumented inconsistency in the time series emerged in the original dataset used. After correcting for this inconsistency, updated results confirm an increase in clear-cut harvested area, but not as abrupt as originally reported. Contrary to what many critics say, this information should be seen as complementing and not necessarily contradicting country statistics, because the latter typically refer to total harvest (including thinning, etc.) and not clear-cut only. Finally, it should not be overlooked that the main aim of the original study was to offer a vision for integrating satellite data into the monitoring of forest resources. This was achieved: The JRC study showed the potential (and limitations) for high-resolution satellite maps to track the temporal evolution of clear-cut forest harvest in EU.

a) Estimates of harvested forest area in the EU, from the original JRC study (Ceccherini et al. 2020), compared to (b) gross forest-related emissions associated to stand-replacement disturbances (natural and anthropogenic) observable in Landsat imagery, 2001-2019 (Harris et al. 2021 -Extended data fig. 9). Both studies use the GFC dataset and show similar discontinuities in the time series between 2015 and 2016 (red ovals). See the respective references for details.

…

Time series of forest biomass loss associated with "salvage logging" from our original study (Ceccherini et al. 2020, in black) and country reports (in orange) for 14 European countries (based on Camia et al. 2021). Salvage loggings dataset has been collected in 14 EU Member States by searching national datasets and/or consulting with national experts. Through the Standing Forestry Committee under the EC, the following Member States validated and acknowledged data on salvage loggings and/ or provided additional information: Austria, Bulgaria, Croatia, Czechia, Estonia, France, Finland, Germany, Hungary, Lithuania, Poland, Slovakia, Slovenia and Sweden. It is important to note that the time series where annual data on salvage loggings are available varies among Member States.

…

a) Estimates of harvested area and biomass as originally reported by Ceccherini et al. (2020); (b) results from Ceccherini et al. (2020) as wrongly "perceived" by many critics, compared to country statistics on total harvest (FAO-STAT); (c) correct like-withlike comparison between results from Ceccherini et al. (2020) on clear-cut felling and country statistics on total fellings, including a preliminary adjustment of Ceccherini et al. (2020) due to the undocumented change in algorithm in 2015 (this correction reduces the increases in harvest in 2016-2018 vs. 2011-2015 by about one third, based on a validation exercise performed for Sweden and Finland illustrated by Ceccherini et al. (2021), and here extrapolated to the entire EU). The fellings from Ceccherini et al. (2020) were obtained simply multiplying the values of biomass (Fig. 3a) by 2. The JRC Bioenergy report (Camia et al. 2021) includes 2020 updates of country statistics and considers a min-max based on the statistics on wood use; the original data are till 2015, which here we extrapolated till 2018 (dotted blue lines). See text for details.

…

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iF o r e s t

F o r e s t

Biogeosciences and Forestry

JRC study on harvested forest area: resolving key misunderstandings

Giacomo Grassi,

Alessandro Cescatti,

Guido Ceccherini

A recent study on forest harvest in the EU (Ceccherini et al. 2020) reported a

strong increase in clear-cut harvested area in recent years, based on remote

sensing information. This triggered a heated debate and many critical com-

ments. Apart from several fair and constructive criticisms, which were wel-

come, we found that some comments have been either not based on evidence

or affected by serious misunderstandings. Here we clarify some technical as-

pects that were omitted or misrepresented in the public debate. Overall, the

original study used in a scientifically correct way the best information avail -

able at that time. After the study was published, a previously undocumented

inconsistency in the time series emerged in the original dataset used. After

correcting for this inconsistency, updated results confirm an increase in clear-

cut harvested area, but not as abrupt as originally reported. Contrary to what

many critics say, this information should be seen as complementing and not

necessarily contradicting country statistics, because the latter typically refer

to total harvest (including thinning, etc.) and not clear-cut only. Finally, it

should not be overlooked that the main aim of the original study was to offer a

vision for integrating satellite data into the monitoring of forest resources.

This was achieved: the JRC study showed the potential (and limitations) for

high-resolution satellite maps to track the temporal evolution of clear-cut for-

est harvest in EU.

Keywords: Harvested Forest Area, Remote Sensed Datasets, Global Forest

Change (GFC), High-Resolution Satellite Maps

        



 !"!#

!"$!

 %&

     '(     

)* !

 *+!  ,      !  #

+!

 "  - +

-  .    - '  -'  

/  # ! 

 !  !!

/          #  !

!"

(+++#!

!,

 0   ""  

    !  #  )    -

!+!

*,!"

#!

 i.e.  1234  564   67

vs.3"1384+

#)

 -     

!  #  (    9!:

+!' 

       ;<  " 

*+ !  !  +

!#!34

!24#="

  

!##"#

- 

+  !    !    

!! +    + 

!3 -

>   

*!#

  %&      * 

?!#   !

  !!+   +

  ++  - !

9+:

@"!#!

#- # 

  3    !!     #

#  !      A  # 

!!

 "   #       

*+A BC!

(#

!!#!

      "    

!+"

!+        

! # -  

+(



 =!+!   

  #  !  !    

"#!""

!



2 D!        !+  

!!!

#E 2F2

  #"!

+"+++

+#"

;<! = "

#  *+!      +  

      +    

"!"!

!    34  #    

"  !

;<"'!#!

European Commission, Joint Research Centre, Directorate D - Sustainable Resources - Bio-

Economy Unit, Ispra, VA (Italy)

@ Giacomo Grassi (giacomo.grassi@ec.europa.eu)

Received: May 05, 2021 - Accepted: May 07, 2021

Citation: Grassi G, Cescatti A, Ceccherini G (2021). JRC study on harvested forest area:

resolving key misunderstandings. iForest 14: 231-235. – doi: 10.3832/ifor0059-014 [online

2021-05-07]

Communicated by: Marco Borghetti

Commentaries & Perspectives

doi:

doi: 10.3832/ifor0059-014

10.3832/ifor0059-014

vol. 14, pp. 231-235

Grassi G et al. - iForest 14: 231-235

 ?!++ 

+!

#!! 

!A+

#! !+  #!(+ *+

!

"9":

    !  !  

!+!!

+    

2D!!!

    +      

    !  "    

            

!

0    !  !  !    

*    +    +!  

"!#!"

  ;!  @       "  

!!#

 +;"

+#+!

+##

!

1. On the correct use of the Global

Forest Change dataset

%&

+      -

+- 

!  9major enhancement” #  

+ 3  @   9the

Global Forest Watch (GFW) website warns

about these inconsistencies and advises

against using the GFC product for temporal

trend analyses:      !  

9The abrupt + B   

Care largely an artefact stemming from

incorrect use of the GFC data time-series".

+9incorrect use of GFC data:

(%&

(

D! !       

+#+3!

!,      - BC  

-'B2C!++

     G

      (+   

!!

++#

H  "#!   ! "

!"-

         " !  

*!

##"!

)* #!

       -    

 @ ! 

#  "        

36!!! 

+ ! !# 



•    G+ #  

"+$!9Over-

all, [GFC] proved to be a useful dataset for

the purpose of assessing harvesting activ-

ity under the given conditions:=(

"6

*+!!!



•     + -

!!9when

suitably calibrated for percentage tree

cover, the Global Forest Change datasets

give a good 'rst approximation of forest

loss (and, probably, gains): !

9in countries with large ar-

eas of forest cover and low levels of defor-

estation, these data should not be relied

upon to provide a precise annual loss/gain

or rate of change estimate for audit pur-

poses without using independent high-

quality reference data: I "  

#

 # #?!  

+    #  "    

 +  ! D!

 #! # "  

-!       

    !      "  

6 *+ J!  !

!#



•)A!!

9the Global Forest Change map can be

used to detect larger forest disturbances,

but it should be used cautiously because of

the substantial commission error for small-

scale disturbances:@!    !

-+!

##!+

2   @  +   !   

!K2!

   #  "  # 

     I ; 

 -+3 )A!

    !      "  

6 *+ 3!   !



-!-+

!#!+-+

 +"  L!  H  

 I -+    #!      

!#-

=            !  

+    +    3

6"*

!@!H

       *  #!  

 -+; 

#

-       >

+*+ # $"

+ H

 !  "

#    -    !   

9one algorithm covers 2011-2019:    

9Gross emissions can be estimated annual-

ly:(+!

#  ! 

!+!+ !!

M++9:!#

   %    

    !    "  #  

-!

@(+!

  > " ! 

>

=   $N(O

-@  +  

,..#+.+.+

232 iForest 14: 231-235

Fig. 1  ;#" # ;<#+ !

+#

 ! ! + " E

+GHI; *+G0!!-

!36

")"##

iForest – Biogeosciences and Forestry

Resolving key misunderstandings on harvested forest area in Europe

#++"

   # +  #  "   

(6#

" A=    !

#3

D"!

    +    +    

!!!  !   !

  (  

9+:

@#(!

!    P  +  

     ! !A#

+"     

"!

+!

"      +  !

!"    + "  #

!++

!  H"      (      9we

should have worn masks in January 2020:@#

( #    ! DQ@O    

!  "    (  #  0!  

  (    =    *  !

!-#!(

+#3

2. Comparing our data with recent

country statistics on natural

disturbances

%    A  

!       

!  !  +      #

##"

+!= !!

          

"8 ;!!

(

=   -+  !  # 

"    !    !  !

!

  !"!  #"+

+++# ! 

    !          

+-+    )!

#"

    L  "  !    

9(+!:  "  #  !  !

"!#

    !       

!! !!

  H"  #    ! #  !

!  +         



-!

+!#!

!"" +*  

!;!

=!A%

+    +  ! #  (    #

 D  + 

  #  "    #  "+

+++      +  "

!    !  ! #

  +  e.g.  "+  +++  

(    #    #    !

(    !     #

  #         #  

!

D"       "  "

!!

#!!!

   ! #   +  !

-! ! 

!"

      !  "  #

!   ;<"  !

!

3. Comparing our data with

country statistics on harvest

=+    +     

"  !! 

 !    " 

  !      (  



-+2      *+!      +

  +

      "  

6G467"3

R#! !!

!      "  

!!!

 -+  2 i.e.    9":  +

!"#"

 !!8J

R2-=D)=

+!! !    9:  

!    "  "  #  JG  R 2 

8J R2  1 6G4   (  

"+;<

H" -+ 2  9 

:,tonnes of clear-cut fellings over-

bark!!vs.m2 of total removals un-

derbark !    @  

"(!

#  "       y 

F # -+ 2  " 0 

"+!#2I

* 

!+ B8C I !

"2 

 ! !    !

!

   -+ 2 = 9:

    !    

"?!#!

!!

 "      #

#

+"(i.e.""

 ! !!   +

  -+  2+    

" ! (!  ! 

  " #+ @ 

      #! 

#"

;< !! !

4!

-+2i.e.#+!

+   !  # ! 

+    +    .  "!

    S        !

"3#0

+ I   I 

       S

""!7

'          #

    "     !   

#864#!

+I-+2

@#L!#  !

  "   

 ;<"

"#3!

!+

+I-+2'

iForest 14: 231-235 233

Fig. 2##9"++++:#!

+!(!+#8

;!!)"++++  

 8;<  R)  +  .!+

  !+)+ - !;

#+R)"(+"++++.

  "    #,  =!  0!+    A  ;

--H!+E!%)"()")

@!"++++

""+R)

iForest – Biogeosciences and Forestry

Grassi G et al. - iForest 14: 231-235

 !  

  !  834  #  !  

+vs!-+2

=!+    "    

!! #!9: !

  "        

!P

  !! #!

!)#"!

  +    "  +++  

      #  #  "  

  !      !    !

!'   +    #

+#+

 ! 9

:#   # $! I 

"!!

+I *"

 B3C

@    !*   

  #      #  

! * #+  "

"    +  !  #

B6C ##

#!

0+##

  "  #

! 

  8334  #    ;<    "

 #  ! '!

!             

!>  !

+

#  +!  #  !  

!"-+2

=+!

!  "           

   "+ +++

  #+ ?! 

!  G  R2    "+  !+  

383R2!+67I 



D"    "      

    !  !    9:

  ! 

+  !    !+  

D! ! !

+

    !  "  "  

I?!

"+I

   ! 



D  ! 

      ++ 

- %DE@@D+A BJC 

9Using the same data set as the

JRC, the researchers Bi.e. %    

C found that timber harvesting in-

creased by about 6 percent between 2016

and 2018 compared to the period between

2011 and 2015:!+!

(+ !+- 

  >  !   64  

      !  S  

-=D

@!*

  "        

 #+  (+ 

! #   , >    !

        # 

+      +  #  #  

!  ;!         

I I!

    #

234 iForest 14: 231-235

Fig. 3;#

"

+

/

!#

+9

":

!

"-=D

)=/(

(

!#

!

#+!

#+!

+L!

#

!!!

++

3

!

"67vs.

3!

"

 ##

)-!



 

;<

#+#



!+"

!#-+2

0+



!!#

!

 

!/

+

3 

7

!) #



iForest – Biogeosciences and Forestry

Resolving key misunderstandings on harvested forest area in Europe
  +            
      $  -  @"
 #  - 
(         D!  !
!!
      *    +  !
    #    "
H" ""
"(
D #
#+
!+E((
+  +  #    !#!
*  +        #  
+! #! *
*+
!+">#+
   D         !
#!!!
        !  #!
(+   "+  ! 
!
- !"!
! "
AH""
  +!    !
!+ !*
   !    

Acknowledgements
!#  ( 
!#+ 
!+O!"! E
Q="%=!
T!#!#!
 "   ! #
              !
 +    +S
#;!
References
=  !      $A
A+$;"U! ="Q
0  @  R!(  )      !  #
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;<2387;$%!DS#;!
<E! !+J7 ,J6
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="Q%==!
"#";!#
3$!372J78,JJJ,27.
8376827
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=" Q  %   =    
'(@T/%&R$!3G,
;7;2,27.83762G8G
$O+! O$'%0
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22G.JG
=$
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+ #   ;<
@ Q= @  7 BC <E, ,..#
L!!..*V!.38.7.
387#68G7#87676#887.LV
VV!V#
H $E   O=  0  =  0  =  
0!)-R-EHRH
 RH!+  =%" %Q )!A O
!R ))) )R!!
" )= !(" =   #
* !  #    M!   $
!+2,288,2
7.8337GJ66
%&RQ!)# =$%!+=R
)   )    %"% 0  H
W( E   -  ) HX  E(
0'DHAXAD=O
=(=0!!0O-
    H(  R    H  E  R
RRR!YX( )OZR
=Q((%TR$!!
     ,  !  
"    ;!
#$!3G,;3;J,2
7.83762G 
-0 %!)=!
0  G  =+  "        #
    !    !+
  -  '    )+    3,
382,22G.3382
)A!TD RA!$ =!
  #      +  #  +
    !  !    
#)+3,827
,22G.3827
'(@T%-H[++%T
T$=T!%;\!#+
#+     ;!  <  $!
3G,;2;8,27.83762G2
Notes
BC,..+#+.
+.."
.
BC,..+
.2+#.
V"J
B2C,..+#+.
B8C=!+"+
32
88#@%6!#$
H"
B3C-,9'
-"
!#!
"
+
:9D!
#
"!!:9=!+
-?!#!!
#"
!"#
!#+
"+++:9R+!
+
!
+#!!
#!":9)
"!!!
+"+++
";<!!
##!:
B6C,..+.
.,27.8376827.R
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iForest 14: 231-235 235
iForest – Biogeosciences and Forestry

The role of forests in the EU climate policy: are we on the right track?

Article

Full-text available

Jul 2023
Carbon Bal Manag

Background The European Union (EU) has committed to achieve climate neutrality by 2050. This requires a rapid reduction of greenhouse gas (GHG) emissions and ensuring that any remaining emissions are balanced through CO 2 removals. Forests play a crucial role in this plan: they are currently the main option for removing CO 2 from the atmosphere and additionally, wood use can store carbon durably and help reduce fossil emissions. To stop and reverse the decline of the forest carbon sink, the EU has recently revised the regulation on land use, land-use change and forestry (LULUCF), and set a target of − 310 Mt CO 2 e net removals for the LULUCF sector in 2030. Results In this study, we clarify the role of common concepts in forest management – net annual increment, harvest and mortality – in determining the forest sink. We then evaluate to what extent the forest sink is on track to meet the climate goals of the EU. For this assessment we use data from the latest national GHG inventories and a forest model (Carbon Budget Model). Our findings indicate that on the EU level, the recent decrease in increment and the increase in harvest and mortality are causing a rapid drop in the forest sink. Furthermore, continuing the past forest management practices is projected to further decrease the sink. Finally, we discuss options for enhancing the sinks through forest management while taking into account adaptation and resilience. Conclusions Our findings show that the EU forest sink is quickly developing away from the EU climate targets. Stopping and reversing this trend requires rapid implementation of climate-smart forest management, with improved and more timely monitoring of GHG fluxes. This enhancement is crucial for tracking progress towards the EU’s climate targets, where the role of forests has become – and is expected to remain – more prominent than ever before.

The timber footprint of German bioeconomy scenarios compared to the planetary boundaries for sustainable roundwood supply

Article

Full-text available

Aug 2022

Assessments of the past development of the German timber footprint of consumption have shown that a) the security of supply of roundwood from own territory was guaranteed in the past, b) Germany is strongly dependent on imports and c) Germany consumes wood from countries and regions that are highly likely to overexploit their roundwood resources. The pressure on global forests is increasing due to rising demands on the one hand and increasing climate-related damage on the other. This paper examines the future development of Germany's timber consumption footprint until 2030 from Germany's own territory and abroad and evaluates it in terms of sustainability. To put the footprint into the perspective of planetary boundaries, we calculate a sustainable roundwood supply potential. For this purpose, high resolution land-use and land-cover maps are combined with international and national forest inventory surveys to determine the global forest available for wood supply. We show that currently this area adds up to about 46 % (1.87 billion ha) of the total global forest area. In combination with country-specific net annual increment data, the annual sustainable roundwood potential is calculated based on a potential harvest rate of maximum 80 %. This leads to a planetary boundary for global roundwood removals of 4.2 billion m³ in 2020. In comparison, global roundwood consumption was already higher than this in 2020, indicating overshoot, and it is likely to increase further. German roundwood production is currently beyond or around the upper limit of sustainable harvesting potential—depending on the scenario criteria, while roundwood consumption is already well above this level. Altogether, results show that a reduction in consumption in high-consuming countries like Germany is necessary to avoid further exceeding the range of sustainable roundwood supply and thus prevent overexploitation.

Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al

Article

Full-text available

Jul 2022
ANN FOR SCI

The timely and accurate monitoring of forest resources is becoming of increasing importance in light of the multi-functionality of these ecosystems and their increasing vulnerability to climate change. Remote sensing observations of tree cover and systematic ground observations from National Forest Inventories (NFIs) represent the two major sources of information to assess forest area and use. The specificity of two methods is calling for an in-depth analysis of their strengths and weaknesses and for the design of novel methods emerging from the integration of satellite and surface data. On this specific debate, a recent paper by Breidenbach et al. published in this journal suggests that the detection of a recent increase in EU forest harvest rate-as reported in Nature by Ceccherini et al.-is largely due to technical limitations of satellite-based mapping. The article centers on the difficulty of the approaches to estimate wood harvest based on remote sensing. However, it does not discuss issues with the robustness of validation approaches solely based on NFIs. Here we discuss the use of plot data as a validation set for remote sensing products, discussing potentials and limitations of both NFIs and remote sensing, and how they can be used synergistically. Finally, we highlight the need to collect in situ data that is both relevant and compatible with remote sensing products within the European Union.

Combining post-disturbance land cover and tree canopy cover from Landsat time series data for mapping deforestation, forest degradation, and recovery across Cambodia

Article

Full-text available

May 2022

Mapping of deforestation, forest degradation, and recovery is essential to characterize country-level forest change and formulate mitigation actions. Previous studies have mainly used a simple forest/non-forest classification after forest disturbance to identify deforestation and forest degradation. However, a more flexible approach that is applicable to different forest conditions is desirable. In this study, we examined an approach for mapping deforestation, forest degradation, and recovery using disturbance types and tree canopy cover estimates from annual Landsat time-series data from 1988 to 2020 across Cambodia. We developed models to estimate both disturbance types and tree canopy cover based on a random forest algorithm using predictor variables derived from a trajectory-based temporal segmentation approach. The estimated disturbance types and canopy cover in each year were then used in a rule-based classification of deforestation, forest degradation, and recovery. The producer’s and user’s accuracies ranged from 59.1% to 72.9% and 60.8% to 91.6%, respectively, for the forest change classes of mapping deforestation, forest degradation, and recovery. The approach developed here can be adjusted for different definitions of deforestation, forest degradation, and recovery according to research objectives and thus has the potential to be applied to other study areas.

Concerns about the boreal forest in Europe

Article

Full-text available

Jun 2021
Forest@

Marco Borghetti

This note reports some concerns about the conservation of the boreal forest in Europe. In the Fennoscandia, there has been a significant increase in forest clearcutting in recent years, likely affecting even the remnants of natural forest. In Sweden, due to the long-term application of plantation forestry, a small fraction of the original boreal forest remains: its further fragmentation may jeopardize forest biodiversity and forest’s ability to cope with ongoing climatic changes. Outside protected areas, clearcutting followed by soil scarification, plantation, forest vegetation management, etc. grants financial profitability and large volumes of wood products, but not the conservation of biodiversity. Negative effects might be also expected on the ecosystem carbon balance due to large carbon-dioxide emissions for long years after clearcutting. It is good that these issues are being brought to the forefront of the environmental and scientific debate.

Area Estimation and Accuracy Assessment for Forest Change Maps Derived from Satellite Data衛星データを用いた森林変化マップにおける面積推定と精度評価の手法

Article

Full-text available

May 2023

Katsuto Shimizu

Thematic maps, such as forest change and land cover change maps, are generally inaccurate. The assessment of accuracy, defined as the correctness of a map, is critical for understanding the quality and utility of thematic maps. This review shows the fundamental principles in area estimation and accuracy assessment for forest change maps based on three components: sampling design, response design, and analysis, and then reveals the criterion and recommended practices. Several special cases of accuracy assessment are also discussed. A probability sampling design is implemented in the statistically rigorous accuracy assessment to estimate accuracy based on the comparison of a map and reference data. Population error matrix is crucial in assessing the accuracy and estimating area. Further, the use of unbiased or consistent estimators that correspond to the sampling design is critical for deriving accuracy metrics and area estimates with associated uncertainty. Although the fundamental principles of accuracy assessment are well established, methods for addressing issues have also recently been developed. Practitioners are required to choose the optimal protocols to achieve their objectives of the accuracy assessment because no single protocol or approach can completely address all situations.

Prelievi forestali in Europa: un sano dibattito scientifico

Article

Full-text available

Jun 2021
Forest@

Forest harvesting in Europe: a healthy scientific debate Is forest harvesting increasing in Europe? There is scientific debate about methodological approach and data regarding clearcut increment in Europe but, besides the discussion, there is a general agreement about the need to collect reliable scientifically robust remote sensing data for sound forest policy-making. Quantificare l'intensità dei prelievi forestali è essenziale per valutare la sostenibilità della gestione forestale di un Paese. Il primo indicatore di gestione forestale sostenibile (anche se non l'unico) è il rapporto tra la ripresa e l'incre-mento, cioè il rapporto tra quanto legno viene prelevato annualmente e quanto ne cresce nello stesso periodo. La regola aurea, dettata per la prima volta da Von Carlowitz nel suo trattato Sylvicultura oeconomica del 1713, stabili-sce che i prelievi non dovrebbero mai superare l'incre-mento. Per misurare l'incremento quasi tutti i Paesi dispongono di inventari forestali nazionali (NFI). Questi sono però rea-lizzati in ogni Paese con frequenza e metodi diversi e non esiste un sistema di aggregazione a livello internazionale-nonostante il sostegno dei ricercatori, degli operatori e della stessa Unione Europea verso una maggiore armoniz-zazione non sia mai mancato. Tener traccia dei prelievi è un problema ancora più spi-noso. Gli NFI producono stime di un grande numero di va-riabili forestali, ma la valutazione delle utilizzazioni fore-stali è problematica, sia in termini di estensione, sia di massa asportata. Il problema nasce dal fatto che gli NFI misurano le nostre foreste solo periodicamente (in Italia solo dal 1985 e ogni 10-15 anni o più) e quindi non sono in grado di produrre informazioni aggiornate su base annua. Diversi Stati membri dell'Unione Europea si sono attrez-zati con sistemi di tracciamento delle utilizzazioni foresta-li, comprese diverse regioni d'Italia (dove le statistiche fo-restali nazionali sono state sospese dall'ISTAT dal 2015). Tuttavia, questi sistemi non registrano tutti i prelievi ma solo quelli che, in ogni regione o provincia autonoma, sono soggetti a dichiarazione o autorizzazione di fatto concentrandosi su quelli di maggiore entità. Per ovviare al problema, un gruppo di ricercatori coordi-nato da Alessandro Cescatti del Centro Comune di Ricerca della Commissione Europea (JRC-Joint Research Centre) ha utilizzato immagini satellitari per monitorare i prelievi di legno in Europa. I satelliti Landsat possono infatti "ve-dere" cambiamenti di superficie forestale con un detta-glio a terra di 900 m 2 : sono quindi in grado di individuare, confrontando immagini acquisite in diversi momenti, mo-difiche della copertura forestale come tagli rasi e diversi tipi di disturbi naturali, come schianti da vento e incendi boschivi. Per individuare questi cambiamenti sono stati recentemente sviluppati sistemi automatici di analisi di lunghe serie temporali di immagini satellitari, come quelli messi a disposizione sulla piattaforma Google Earth Engine. Il gruppo di Matthew Hansen dell'Università del Mary-land pubblica a partire dal 2013 il Global Forest Change (GFC-Hansen et al. 2013): una carta globale delle varia-zioni di superficie arborea basata su immagini Landsat e disponibile open access online (https://earthenginepartn ers.appspot.com/science-2013-global-forest). Lo studio, pubblicato su Nature nel 2020 di cui è primo autore Ceccherini, utilizza la mappa di Hansen per con-frontare i prelievi forestali in Europa avvenuti nel periodo 2016-2018 rispetto al 2011-2015, utilizzando i dati globali sulle variazioni di copertura arborea forniti da Hansen e integrandoli con una carta della massa forestale rilevata dalla missione satellitare GlobBiomass (GlobBiomass 2017). I risultati sono stati pubblicati nel luglio 2020 sulla rivista Nature in uno studio dal titolo "Abrupt increase in harvested forest area over Europe after 2015" (Ceccherini et al. 2020). Dopo aver cercato di escludere dalle analisi incendi e schianti da vento, seppure sulla base dei dati esistenti che non sono esaustivi, i ricercatori hanno mostrato che nell'ultimo triennio la superficie forestale soggetta a tagli rasi (clear cut) è aumentata del 49%, la massa legnosa pre-levata del 69%, e la dimensione mediana delle tagliate del 34% (Fig. 1). Per l'Italia, lo studio riporta un aumento del 121% della superficie interessata da clear cut, e un aumen-to superiore al 100% delle dimensioni delle tagliate (che restano comunque di poco superiori a un ettaro di super-ficie media) negli ultimi tre anni analizzati. Per quanto ri-guarda la situazione italiana occorre evidenziare che il ta-© SISEF https://foresta.sisef.org/ 35 Forest@ (2021) 18: 35-37 (1) Dipartimento di Scienze Agrarie, Forestali e Alimentari

Reply to Wernick, I. K. et al.; Palahí, M. et al.

Article

Full-text available

Apr 2021
NATURE

Quantifying forest change in the European Union

Article

Full-text available

Apr 2021
NATURE

Global maps of twenty-first century forest carbon fluxes

Article

Full-text available

Mar 2021
Nat. Clim. Change.

Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency. Forest management for climate mitigation plans requires accurate data on carbon fluxes to monitor policy impacts. Between 2001 and 2019, forests were a net sink of carbon globally, although emissions from disturbances highlight the need to reduce deforestation in tropical countries.

Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests

Article

Full-text available

Jul 2020

Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledge. However, the accuracy differences between global and local maps have still not been fully investigated; therefore, their optimal use for estimating forest disturbances has not been clarified. This study assesses the annual forest disturbance detection of an available Global Forest Change map and a local disturbance map based on a Landsat temporal segmentation algorithm in areas dominated by harvest disturbances. We assess the forest disturbance detection accuracies based on two reference datasets in each year. We also use a polygon-based assessment to investigate the thematic accuracy based on each disturbance patch. As a result, we found that the producer’s and user’s accuracies of disturbances in the Global Forest Change map were 30.1–76.8% and 50.5–90.2%, respectively, for 2001–2017, which corresponded to 78.3–92.5% and 88.8–97.1%, respectively in the local disturbance map. These values indicate that the local disturbance map achieved more stable and higher accuracies. The polygon-based assessment showed that larger disturbances were likely to be accurately detected in both maps; however, more small-scale disturbances were at least partially detected by the Global Forest Change map with a higher commission error. Overall, the local disturbance map had higher forest disturbance detection accuracies. However, for forest disturbances larger than 3 ha, the Global Forest Change map achieved comparable accuracies. In conclusion, the Global Forest Change map can be used to detect larger forest disturbances, but it should be used cautiously because of the substantial commission error for small-scale disturbances and yearly variations in estimated areas and accuracies.

Abrupt increase in harvested forest area over Europe after 2015

Article

Full-text available

Jul 2020
NATURE

Forests provide a series of ecosystem services that are crucial to our society. In the European Union (EU), forests account for approximately 38% of the total land surface1. These forests are important carbon sinks, and their conservation efforts are vital for the EU’s vision of achieving climate neutrality by 20502. However, the increasing demand for forest services and products, driven by the bioeconomy, poses challenges for sustainable forest management. Here we use fine-scale satellite data to observe an increase in the harvested forest area (49 per cent) and an increase in biomass loss (69 per cent) over Europe for the period of 2016–2018 relative to 2011–2015, with large losses occurring on the Iberian Peninsula and in the Nordic and Baltic countries. Satellite imagery further reveals that the average patch size of harvested area increased by 34 per cent across Europe, with potential effects on biodiversity, soil erosion and water regulation. The increase in the rate of forest harvest is the result of the recent expansion of wood markets, as suggested by econometric indicators on forestry, wood-based bioenergy and international trade. If such a high rate of forest harvest continues, the post-2020 EU vision of forest-based climate mitigation may be hampered, and the additional carbon losses from forests would require extra emission reductions in other sectors in order to reach climate neutrality by 20503. Fine-scale satellite data are used to quantify forest harvest rates in 26 European countries, finding an increase in harvested forest area of 49% and an increase in biomass loss of 69% between 2011–2015 and 2016–2018.

An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting

Article

Full-text available

Jun 2020

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R2 = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R2 = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data.

Assessing Harvested Sites in a Forested Boreal Mountain Catchment through Global Forest Watch

Article

Full-text available

Mar 2019

Global Forest Watch (GFW) provides a global map of annual forest cover loss (FCL) produced from Landsat imagery, offering a potentially powerful tool for monitoring changes in forest cover. In managed forests, FCL primarily provides information on commercial harvesting. A semi-autonomous method for providing data on the location and attributes of harvested sites at a landscape level was developed which could significantly improve the basis for catchment management, including risk mitigation. FCL in combination with aerial images was used for detecting and characterising harvested sites in a 1607 km 2 mountainous boreal forest catchment in south-central Norway. Firstly, the forest cover loss map was enhanced (FCLE) by removing small isolated forest cover loss patches that had a high probability of representing commission errors. The FCLE map was then used to locate and assess sites representing annual harvesting activity over a 17-year period. Despite an overall accuracy of >98%, a kappa of 0.66 suggested only a moderate quality for detecting harvested sites. While errors of commission were negligible, errors of omission were more considerable and at least partially attributed to the presence of residual seed trees on the site after harvesting. The systematic analysis of harvested sites against aerial images showed a detection rate of 94%, but the area of the individual harvested site was underestimated by 29% on average. None of the site attributes tested, including slope, area, altitude, or site shape index, had any effect on the accuracy of the area estimate. The annual harvest estimate was 0.6% (standard error 12%) of the productive forest area. On average, 96% of the harvest was carried out on flat to moderately steep terrain (<40% slope), 3% on steep terrain (40% to 60% slope), and 1% on very steep terrain (>60% slope). The mean area of FCLE within each slope category was 1.7 ha, 0.9 ha, and 0.5 ha, respectively. The mean FCLE area increased from 1.0 ha to 3.2 ha on flat to moderate terrain over the studied period, while the frequency of harvesting increased from 249 to 495 sites per year. On the steep terrain, 35% of the harvesting was done with cable yarding, and 62% with harvester-forwarder systems. On the very steep terrain (>60% slope), 88% of the area was harvested using cable yarding technology while harvesters and forwarders were used on 12% of the area. Overall, FCL proved to be a useful dataset for the purpose of assessing harvesting activity under the given conditions.

The use of woody biomass for energy purposes in the EU. EUR 30548 EN, Publications Office of the European Union

Jan 2021

A Camia
J Giuntoli
R Jonsson
N Robert
N E Cazzaniga
G Jasinevičius
V Avitabile
G Grassi
J I Barredo
S Mubareka

Camia A, Giuntoli J, Jonsson R, Robert N, Cazzaniga NE, Jasinevičius G, Avitabile V, Grassi G, Barredo JI, Mubareka S (2021). The use of woody biomass for energy purposes in the EU. EUR 30548 EN, Publications Office of the European Union, Luxembourg, pp. 178. -doi: 10.276 0/831621

Note on the JRC Nature paper on harvest, complementing the reply of

Jan 2021

G Grassi
A Cescatti
G Ceccherini

Grassi G, Cescatti A, Ceccherini G (2021). Note on the JRC Nature paper on harvest, complementing the reply of Ceccherini et al. 2021. JRC-EU, Ispra, VA, Italy, pp. 8. [online] URL: http://forest.jrc.ec.europa.eu/media/filer_public/54/d8/ 54d8eca1-a0f6-4e19-8fc1-48c686fc448a/jrc_not e_on_nature_paper.pdf

Ceccherini et al: Concerns about reported harvested area and biomass loss in European forests

Jan 2021
NATURE
15-17

M Palahí
R Valbuena
C Senf
N Acil
Tam Pugh
J Sadler
R Seidl
P Potapov
B Gardiner
L Hetemäki
G Chirici
S Francini
T Hlásny
Bjw Lerink
H Olsson
González Olabarria
J R Ascoli
D Asikainen
A Bauhus
J Berndes
G Donis
J Fridman
J Hanewinkel
M Jactel
H Lindner
M Marchetti
M Marušák
R Sheil
D Tomé
M Trasobares
A Verkerk
P J Korhonen
M Nabuurs

Palahí M, Valbuena R, Senf C, Acil N, Pugh TAM, Sadler J, Seidl R, Potapov P, Gardiner B, Hetemäki L, Chirici G, Francini S, Hlásny T, Lerink BJW, Olsson H, González Olabarria JR, Ascoli D, Asikainen A, Bauhus J, Berndes G, Donis J, Fridman J, Hanewinkel M, Jactel H, Lindner M, Marchetti M, Marušák R, Sheil D, Tomé M, Trasobares A, Verkerk PJ, Korhonen M, Nabuurs G-J (2021). Ceccherini et al: Concerns about reported harvested area and biomass loss in European forests. Nature 592: E15-E17. -doi: 10.103 8/s41586-021-03292-x