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

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 self-evident. In a simulation study, we compared three different approaches: 1) using only current data, 2) using non-updated 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.202

The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass

International audienceAbstractKey messageWhen areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose.ContextRemote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase.AimsThe aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates.MethodsFor study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates.ResultsFor the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area.ConclusionsThe effects of differences in map and ground data dates were less important than temporal change in the study area

Prediction of Timber Quality Parameters of Forest Stands by Means of Small Footprint Airborne Laser Scanner Data

The aim of this study was to explore the capability of airborne laser scanner (ALS) data to explain the variation in field-measured variables representing timber quality within square 0.25 ha grid cells in a mature conifer forest in the southeast of Norway. These variables were the mean ratio between stem diameter at six m above ground and the diameter at breast height (R D6 ), the volume of saw logs (V SL ), the proportion of saw logs relative to the total volume (P SL ), the ratio between tree height and diameter at breast height (HD), mean basal area diameter (D g ), and crown height (CH). Each of these variables was modeled using a mixed modeling approach. Model fit was expressed by the Pseudo-R 2 , and were 0.85, 0.50, 0.78, 0.57, 0.74, and 0.58 for the respective quality variables. Furthermore, much of the residual error could be attributed to the different forest stands from which the grid cells originated even though we used field-observed tree species proportions as auxiliary information. It was concluded that more auxiliary information is needed to estimate models that are general across stands, but that the relationships between ALS-data and the quality variables considered here seem strong enough to be utilized for example to prioritize between stands in relation to harvest when specific quality distributions are sought

Large-area inventory of species composition using airborne laser scanning and hyperspectral data

5openInternationalInternational coauthor/editorTree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.openØrka, Hans Ole; Hansen, Endre Hofstad; Dalponte, Michele; Gobakken, Terje; Næsset, ErikØrka, H.O.; Hansen, E.H.; Dalponte, M.; Gobakken, T.; Næsset, E

Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches

Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold’s error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories

A stand level scenario model for the Norwegian forestry – a case study on forest management under climate change

Carbon sequestration and income generation are competing objectives in modern forest management. The climate commitments of many countries depend on forests as carbon sinks which must be quantified, monitored, and projected into the future. For projections we need tools to model forest development and perform scenario analyses to assess future carbon sequestration potentials under different management regimes, the expected net present value of such regimes, and possible impacts of climate change. We propose a scenario analysis software tool (GAYA 2.0) that can assist in answering these types of questions using stand level simulations, detailed carbon flow models and an optimizer. This paper has two objectives: (1) to describe GAYA 2.0, and (2) demonstrate its potential in a case study where we analyze the forest carbon balance over a region in Norway based on national forest inventory sample plots. The tool was used to map the optimality front between the carbon benefit and net present value. We observed changes in net present value for different levels of carbon benefit as well as changes in optimal management strategies. We predicted future changes in several forest carbon pools as well as albedo and illustrated the impact of gradual increase in forest productivity (i.e., due to climate warming). Having been updated and modernized from its previous version with increased attention to forest carbon and energy fluxes, GAYA 2.0 is an effective tool that offers multiple opportunities to perform various types of scenario analyses in forest management

On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests

This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data.We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development

Detection of heartwood rot in Norway spruce trees with lidar and multi-temporal satellite data

Norway spruce pathogenic fungi causing root, butt and stem rot represent a substantial problem for the forest sector in many countries. Early detection of rot presence is important for efficient management of the forest resources but due to its nature, which does not generate evident exterior signs, it is very difficult to detect without invasive measurements. Remote sensing has been widely used to monitor forest health status in relation to many pathogens and infestations. In particular, multi-temporal remotely sensed data have shown to be useful in detecting degenerative diseases. In this study, we explored the possibility of using multi-temporal and multi-spectral satellite data to detect rot presence in Norway spruce trees in Norway. Images with four bands were acquired by the Dove satellite constellation with a spatial resolution of 3 m, ranging over three years from June 2017 to September 2019. Field data were collected in 2019–2020 by a harvester during the logging: 16163 trees were recorded, classified in terms of species and presence of rot at the stump and automatically geo-located. The analysis was carried out at individual tree crown (ITC) level, and ITCs were delineated using lidar data. ITCs were classified as healthy, infested and other species using a weighted Support Vector Machine. The results showed an underestimation of the rot presence (balanced accuracy of 56.3%, producer’s accuracies of 64.3 and 48.4% and user’s accuracies of 81.0% and 32.7% respectively for healthy and rot ITCs). The method can be used to provide a tentative map of the rot presence to guide more detailed assessments in field and harvesting activitie

Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data

5openInternationalInternational coauthor/editorWood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were smallopenDalponte, Michele; Kallio, Alvar J. I.; Ørka, Hans Ole; Næsset, Erik; Gobakken, TerjeDalponte, M.; Kallio, A.J.I.; Ørka, H.O.; Næsset, E.; Gobakken, T