Errors related to the automatized satellite-based change detection of boreal forests in Finland

Pitkänen, Timo; Sirro, Laura; Häme, Lauri; Häme, Tuomas; Törmä, Markus; Kangas, Annika

doi:10.1016/j.jag.2019.102011

Cited by 14 publications

(12 citation statements)

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“…Residual cuts tended to be confused with clear-cutting, showing no differences in their spectral response. Reference [18] interpreted similar forest changes using Sentinel-2 images and reached similar conclusions. Efforts to characterize partial cuts and clear-cuts have also been conducted using airborne laser scanning (ALS) data [3,19].…”

Section: Introductionsupporting

confidence: 58%

“…Clear-cutting was, as expected, the type of change best characterized, with omission and commission errors similar to those achieved by other authors (8.11% and 15% for scenario A and 5.38% and 31.25% for scenario B). Reference [16] reported an omission error of 16.2% for clear-cutting detection in a boreal area in Canada, and clear-cutting was classified in Finland with commission errors ranging between 7-11.6% [18]. Clear-cutting in France was detected with 19% and 54% of omission and commission error, respectively [17], while in Italy, reference [40] achieved omission errors ranging between 16-55% and commission errors smaller than 15%.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies aimed at detecting changes driven by forest management practices have been conducted in boreal forests [16,18,19,36,37] or tropical forests [22,27,38]. In Mediterranean environments, there are also some examples of clear-cutting detection [15,39,40], but these experiences are isolated examples compared to the large number of studies conducted in other environments.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Esteban¹,

Fernández-Landa²,

Tomé³

et al. 2021

Remote Sensing

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Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.

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Section: Introductionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Esteban¹,

Fernández-Landa²,

Tomé³

et al. 2021

Remote Sensing

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“…The prediction of the harvests is also likely to increase the error variance of the change estimates, so that improved accuracy would be obtained if the harvests were directly observed from the differences between remote sensing materials and used as known control actions rather than predicted using a model as was done here. This is possible for clearcuts, which can be accurately delineated from differences between two satellite images (Pitkänen et al 2020). Moreover, if the change model would reflect purely growth, it would be possible to utilize relative errors (as in Ehlers et al 2013) rather than absolute errors.…”

Section: Discussionmentioning

confidence: 99%

Benefits of past inventory data as prior information for the current inventory

2020

Self Cite

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Background: When auxiliary information in the form of airborne laser scanning (ALS) is used to assist in estimating the population parameters of interest, the benefits of prior information from previous inventories are not selfevident. In a simulation study, we compared three different approaches: 1) using only current data, 2) using nonupdated old data and current data in a composite estimator and 3) using updated old data and current data with a Kalman filter. We also tested three different estimators, namely i) Horwitz-Thompson for a case of no auxiliary information, ii) model-assisted estimation and iii) model-based estimation. We compared these methods in terms of bias, precision and accuracy, as estimators utilizing prior information are not guaranteed to be unbiased. Results: The largest standard errors were obtained when neither prior information nor auxiliary information were used. If a growth model was not applied to update the old data, the resulting composite estimators were biased. Largest RMSEs were obtained using non-updated prior information in a composite estimator. Using the ALS data as auxiliary information produced smaller RMSE than using prior information from the old inventory. The smallest RMSEs were obtained when both the auxiliary data and updated old data were used. With growth updating the bias can be substantially reduced, although design-unbiasedness of the estimator cannot be guaranteed. Conclusions: Prior information from old inventory data can be useful also when combined with highly accurate auxiliary information, when both data sources are efficiently used. The benefits obtained from using the old data will increase if the past harvests can be detected without errors from changes in the auxiliary data instead of being predicted with models.

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“…There has been a huge increase in studies using Landsat time series and many of them have focused on change detection methods (Zhu 2017). Abrupt changes can be detected by comparing images from two different time points (Pitkänen et al 2020). Long-term decline or growth, however, requires more images and poses more challenges (Coops et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessing local trends in indicators of ecosystem services with a time series of forest resource maps

Katila¹,

Rajala²,

Kangas³

2020

Silva Fenn.

Self Cite

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Since the 1990âs, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric -nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 Ã 16 m pixel size and units of 240 Ã 240 m, 1200 Ã 1200 m and 12â000 Ã 12â000 m aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12â000 Ã 12â000 m. The multiple testing corrections decreased the amount of significant -values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.k22222p

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Errors related to the automatized satellite-based change detection of boreal forests in Finland

Cited by 14 publications

References 50 publications

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Benefits of past inventory data as prior information for the current inventory

Assessing local trends in indicators of ecosystem services with a time series of forest resource maps

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