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Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. [ABSTRACT FROM AUTHOR]

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation. [ABSTRACT FROM AUTHOR]

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine ( R2=0.75 , RMSE=5.33 m 2 /ha), spruce ( R2=0.75,RMSE=6.73 m 2 /ha) and birch ( R2=0.71,RMSE=4.98 m 2 /ha), with total relative RMSE(%)=29.66% . In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine ( R2=0.88 , RMSE=32.46 m 3 /ha) and total stem volume ( R2=0.87 , RMSE=35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations. [ABSTRACT FROM AUTHOR]

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis. [ABSTRACT FROM AUTHOR]

• Forest changes were automatically modelled from multitemporal Sentinel-2 images. • Errors were evaluated based on visually interpreted VHR images. • Extraction of clear-cuts was accurate whereas thinnings had more variation. • Image quality and translucent clouds had most significant effect on errors. • Results were regarded applicable for operational change monitoring. The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) collected in a needle-leaf forest site near Ruokolahti, Finland, during a field campaign in June 14–21, 2000, was used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI map. The analysis of empirical approaches indicates that preliminary segmentation of the image followed by empirical modeling with the resulting patches, was an effective approach to developing an LAI validation surface. Comparison of the aggregated high-resolution LAI map and corresponding MODIS LAI retrievals suggests satisfactory behavior of the MODIS LAI algorithm although variation in MODIS LAI product is higher than expected. The MODIS algorithm, adjusted to high resolution, generally overestimates the LAI due to the influence of the understory vegetation. This indicates the need for improvements in the algorithm. An improved correlation between field measurements and the reduced simple ratio (RSR) suggests that the shortwave infrared (SWIR) band may provide valuable information for needle-leaf forests. [Copyright &y& Elsevier]