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JRC study on harvested forest area: Resolving key misunderstandings

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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.
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i
iF o r e s t
F o r e s t
Biogeosciences and Forestry
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 #   
"  !
;<"'!#!
© SISEF https://iforest.sisef.org/ 231 iForest 14: 231-235
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
Commentaries & Perspectives
doi:
doi: 10.3832/ifor0059-014
10.3832/ifor0059-014
vol. 14, pp. 231-235
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+$;"U! ="Q
0  @  R!(  )      !  #
#+!;<
;<2387;$%!DS#;!
<E! !+J7 ,J6
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  O!" E 
="Q%==!
"#";!#
3$!372J78,JJJ,27.
8376827
  O!" E 
=" Q %  =  
'(@T/%&R$!3G,
;7;2,27.83762G8G
$O+! O$'%0
%  %(+    H  R  R  =
= #+# +
## +
+    )+    ,  JG   ,
22G.JG
=$
$!"
+ #   ;<
@ Q= @  7 BC <E, ,..#
L!!..*V!.38.7.
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VV!V#
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" )= !(" =  #
* ! #  M!  $
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7.8337GJ66
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)  )  %"% 0 H
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=(=0!!0O-
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RRR!YX( )OZR
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"    ;!
#$!3G,;3;J,2
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Notes
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iForest 14: 231-235 235
iForest Biogeosciences and Forestry
... This method of data collection will become increasingly important in the future. However, one has to look closely, as a recent example about the alleged abrupt increase in deforested area in Europe shows (Ceccherini et al., 2020;Grassi et al., 2021;Palahí et al., 2021;Picard et al., 2021;Wernick et al., 2021). Their analysis overlooked the fact that the satellite data collection method had changed and therefore inconsistencies in the time series occurred after 2015, so the results are not directly comparable to the period before 2015. ...
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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.
... The undocumented change in the Global Forest Change -GFC (Hansen et al. 2013) algorithm between 2015 and 2016 has been already confirmed in Palahì et al. (2021) and more recently in a GFC blog (GFC, 2021). The impact of that change on harvest statistics has been assessed and reported for the first time in our rebuttal and accompanying documents (i.e., Grassi et al. 2021a;Ceccherini et al. 2021). ...
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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.
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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. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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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
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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.
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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.
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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.
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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
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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
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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