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

Rossi, Fernando Saragosa; Breidenbach, Johannes; Puliti, Stefano; Astrup, Rasmus; Talbot, Bruce

doi:10.3390/rs11050543

Cited by 18 publications

(14 citation statements)

References 32 publications

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“…The SR16 maps are regularly updated for growth using linking models with updated parameter estimates using current NFI data. Changes due to larger removals such as harvests are updated by setting the predicted forest attributes in pixels with a Global Forest Watch change (Hansen et al 2013 ; Rossi et al 2019 ) to zero. This also provides harvested volume as a new map product.…”

Section: Current Developments and The Foreseeable Futurementioning

confidence: 99%

A century of National Forest Inventory in Norway – informing past, present, and future decisions

et al. 2020

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Past In the early twentieth century, forestry was one of the most important sectors in Norway and an agitated discussion about the perceived decline of forest resources due to over-exploitation was ongoing. To base the discussion on facts, the young state of Norway established Landsskogtakseringen – the world’s first National Forest Inventory (NFI). Field work started in 1919 and was carried out by county. Trees were recorded on 10 m wide strips with 1–5 km interspaces. Site quality and land cover categories were recorded along each strip. Results for the first county were published in 1920, and by 1930 most forests below the coniferous tree line were inventoried. The 2nd to 5th inventories followed in the years 1937–1986. As of 1954, temporary sample plot clusters on a 3 km × 3 km grid were used as sampling units. Present The current NFI grid was implemented in the 6th NFI from 1986 to 1993, when permanent plots on a 3 km × 3 km grid were established below the coniferous tree line. As of the 7th inventory in 1994, the NFI is continuous, and 1/5 of the plots are measured annually. All trees with a diameter ≥ 5 cm are recorded on circular, 250 m 2 plots. The NFI grid was expanded in 2005 to cover alpine regions with 3 km × 9 km and 9 km × 9 km grids. In 2012, the NFI grid within forest reserves was doubled along the cardinal directions. Clustered temporary plots are used periodically to facilitate county-level estimates. As of today, more than 120 variables are recorded in the NFI including bilberry cover, drainage status, deadwood, and forest health. Land-use changes are monitored and trees outside forests are recorded. Future Considerable research efforts towards the integration of remote sensing technologies enable the publication of the Norwegian Forest Resource Map since 2015, which is also used for small area estimation at the municipality level. On the analysis side, capacity and software for long term growth and yield prognosis are being developed. Furthermore, we foresee the inclusion of further variables for monitoring ecosystem services, and an increasing demand for mapped information. The relatively simple NFI design has proven to be a robust choice for satisfying steadily increasing information needs and concurrently providing consistent time series.

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Section: Current Developments and The Foreseeable Futurementioning

confidence: 99%

A century of National Forest Inventory in Norway – informing past, present, and future decisions

et al. 2020

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“…Our rebuttal already explained that the change of algorithm in 2015 was totally undocumented: the websites by GFC [2] and GFW [3] include warnings on combining two time series, 2001-2010and 2011-2019(and this was clearly acknowledged in Ceccherini et al 2020), but do not include any warning against trend analyses after 2012.…”

Section: On the Correct Use Of The Global Forest Change Datasetmentioning

confidence: 99%

“…• Rossi et al (2019), assessing forest harvesting in Norway, concluded that "Overall, [GFC] proved to be a useful dataset for the purpose of assessing harvesting activity under the given conditions". A marked increase in harvest is shown in 2016 (their figure 12), but it is not discussed as a possible inconsistency in the dataset.…”

Section: On the Correct Use Of The Global Forest Change Datasetmentioning

confidence: 99%

JRC study on harvested forest area: resolving key misunderstandings

Grassi¹,

Cescatti²,

Ceccherini³

2021

iForest

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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 clearcut 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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“…Intense forest loss is a well studied process in Europe (Senf et al, 2018;Ceccherini et al, 2020;Senf and Seidl, 2021). Discrepancies between national forest inventories and remote sensing technique has led to disagreements in Sweden (Paulsson et al, 2020), Finland (Korhonen, 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 (Palahí et al, 2021).…”

Section: Time-series Analysis Interpretations and Challengesmentioning

confidence: 99%

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

Witjes¹,

Parente²,

Diemen³

et al. 2021

Preprint

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A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.

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

Cited by 18 publications

References 32 publications

A century of National Forest Inventory in Norway – informing past, present, and future decisions

A century of National Forest Inventory in Norway – informing past, present, and future decisions

JRC study on harvested forest area: resolving key misunderstandings

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

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