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

Katila, Matti; Rajala, Tiina; Kangas, Annika

doi:10.14214/sf.10347

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…The most reliable change estimates on biomass for segments were achieved on clear-cut sites. In case of smaller changes in biomass or tree volumes, time series analyses would improve the reliability of the change estimates (Katila et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Ideally, losses are detected in each stand when stands are harvested or affected by disturbances. Detecting biomass gains, on the other hand, may require aggregation of several stands, in time-series analyses significant trends have been found with a relatively large unit size, 1200 × 1200 m 2 (Katila et al 2020). In NFI11, mean volumes on mature stands on forest land were 251 m 3 ha -1 and 134 m 3 ha -1 in southern and northern Finland, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Spatial patterns of biomass change across Finland in 2009–2015

Haakana

Tuominen

Heikkinen

et al. 2022

Preprint

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Forest characteristics vary largely at the regional level and in smaller geographic areas in Finland. The amount of greenhouse gas emissions is related to changes in biomass and the soil type (e.g. upland soils vs. peatlands). Forest characteristics affect forest management practices and how vulnerable single stands are to disturbances. Spatially accurate map data of forests and biomass changes could improve the ability to suggest optimal management alternatives for any patch of land, e.g. in terms of climate change mitigation. In our study, estimating and explaining spatial patterns of biomass change across Finland was the main interest. We analysed biomass changes on different soil and site types on forested land using the Finnish Multi-Source National Forest Inventory (MS-NFI) map layers of the 2009 and 2015 datasets. Silvicultural management and treatment units larger than individual pixels were created by automatic segmentation of the thematic maps. The segmented MS-NFI biomass maps of growing stocks, including above and below ground biomass resulted an average estimate of 77.7 tons ha-1 compared to the national forest inventory estimate of 76.5 tons ha-1 in 2015 for Finland. Forest soil type had a similar effect on average biomass in segmented MS-NFI and NFI data. Despite good regional and country-level results, at the segment level the biomass distributions were narrowed and averaging of biomass estimates was observed. Hence, biomass changes on segments can be considered only approximate values; also, those small differences in average biomass may accumulate when map layers from more than one time point are compared. MS-NFI classification results depend on the satellite images and field data used, causing variation in successive inventories. In addition, to avoid false biomass change observations due to the low growth rate of boreal forests, a six-year study period was set. A kappa of 0.44 was achieved for precision when comparing undisturbed and disturbed forest stands in the Global Forest Cover layer and MS-NFI segmented map, indicating the low ability of the global forest map to identify land cover changes for Finland. The segmented biomass maps provide a useful tool for forest owners to analyse carbon stock changes in their forests and how to affect the amount of carbon by forest management.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatial patterns of biomass change across Finland in 2009–2015

Haakana

Tuominen

Heikkinen

et al. 2022

Preprint

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“…The maps provided values for every target variable except the LAI. The maps had been computed with the k-NN method using Landsat, Spot, and other optical data with similar spatial resolutions and national forest inventory field plot data for reference [36,37]. In addition, a Finnish high-resolution version of the CORINE 2012 map on land cover classification and visual interpretations of Google Earth was used to support the labeling for the land cover classes.…”

Section: Study Area and Reference Datamentioning

confidence: 99%

Forest Area and Structural Variable Estimation in Boreal Forest Using Suomi NPP VIIRS Data and a Sample from VHR Imagery

et al. 2023

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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.

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“…In general, maps are needed for local decision making (Kangas et al 2018), as well as local monitoring of the state of environment, for instance for sustainability considerations (Katila et al 2020). In Finland, the first national-level thematic maps describing the status of forests were published already in the 1990's (Tomppo 1996), and currently such maps are produced in many other countries as well (Baccini et al 2011;Saatchi et al 2011;Freeman and Moisen 2015;Nord-Larsen et al 2017;Nilsson et al 2017;Hauglin et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Understanding uncertainty in forest resources maps

Kangas¹,

Myllymäki²,

Mehtätalo³

2023

Silva Fenn

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Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

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

Cited by 7 publications

References 31 publications

Spatial patterns of biomass change across Finland in 2009–2015

Spatial patterns of biomass change across Finland in 2009–2015

Forest Area and Structural Variable Estimation in Boreal Forest Using Suomi NPP VIIRS Data and a Sample from VHR Imagery

Understanding uncertainty in forest resources maps

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