Harvested area did not increase abruptly—how advancements in satellite-based mapping led to erroneous conclusions

Abstract

Key message Using satellite-based maps, Ceccherini et al. (Nature 583:72-77, 2020) report abruptly increasing harvested area estimates in several EU countries beginning in 2015. Using more than 120,000 National Forest Inventory observations to analyze the satellite-based map, we show that it is not harvested area but the map’s ability to detect harvested areas that abruptly increases after 2015 in Finland and Sweden.

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat

“…Forest loss in Europe is currently highly debated in academia ( Senf et al, 2018 ; Ceccherini et al, 2020 ; Senf & Seidl, 2021 ; Palahi et al, 2021 ; Picard et al, 2021 ). Discrepancies between national forest inventories and remote sensing techniques has led to disagreements in Sweden ( Paulsson et al, 2020 ), Finland ( Breidenbach et al, 2020 ), and Norway ( Rossi et al, 2019 ). For instance, it was found that existing remote sensing products are deemed not fit for these types of analysis ( Palahi et al, 2021 ).…”

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

“…Remote sensing-based assessment of European harvest from clear-cuts (Ceccherini et al 2020) has triggered extensive debate. Here we discuss the limitations and potentials of remote sensing and NFI-based methods in the assessment of harvest rates while addressing the three main criticisms on the Ceccherini paper moved by Breidenbach et al (2022), and we suggest constructive ways forward to ensure complementary of the different approaches.…”

“…Harvest or stable forest classes were determined exclusively with aerial photographs. Furthermore, the statements by Breidenbach et al (2022) are incorrect because it was not Landsat that became more sensitive in 2016, but the classification algorithm used in the production of the GFC maps. In fact, the most recent sensor (i.e., Landsat 8) has been operational since 2013 and, therefore, its sensitivity did not change in 2016.…”

“…Inconsistencies between remote sensing and NFIs validation. With this claim (3), Breidenbach et al (2022) question the validity of the corrected estimates reported in Ceccherini et al (2021), based on the results of a validation exercise that relies on NFIs' plots to correct the GFC dataset. While we fully recognize the key value of NFIs in providing high-quality in situ observations, it should be stressed that the current sampling design of NFIs is typically targeted towards the collection of country-wide statistics on forest area and biomass, and not towards the validation of remote sensing products of land cover change such as the GFC.…”

“…In this light of thoughts, the main objective of this opinion paper is to respond to a recent criticism made by Breidenbach et al (2022) regarding the use of Landsat remote sensing imagery to estimate recent harvest rates across EU countries (Ceccherini et al 2020), in the hope that constructive dialog can effectively lead to progress in forest monitoring. More specifically, the three arguments brought forward by Breidenbach et al (2022)-based on the sole use of NFIs plots-are that (1) the change in forest loss reported for Sweden and Finland in Ceccherini et al (2020) is overestimated due to the increased sensitivity of the Global Forest Change-GFC (Hansen et al 2013)-product after 2015; (2) the sample-based validation we performed in Ceccherini et al (2021) is flawed because it uses Landsat data; and (3) estimates from Ceccherini et al (2020) are inconsistent with those derived from a GFC validation based on plot data of NFIs. We conclude by suggesting possible venues for further research.…”