Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV-based aerial imagery

The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra-high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100 km latitudinal gradient in Norway (60–69°N). Before classification, we performed a superpixel segmentation of the UAV-derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree-covered area and five classes within the ridge-snowbed gradient. We calculated features providing spectral, textural, three-dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal-based, geographical regional-based and seasonal-regional-based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridge-snowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red-edge and near-infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field-season or low-resolution remote sensing data.publishedVersio

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

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

UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).publishedVersio

Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

publishedVersio

Lidar sampling for large-area forest characterization: A review

The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation. © 2012

Use of remote sensing for mapping of non-native conifer species

Serien het tidligere INA fagrapportNon-native species are by many considered a threat to local biodiversity. In Norway, conifer species have been introduced in order to find species with better timber production than the native species. Several of these introduced species have been considered to be invasive, and put on an official “blacklist”. Thus, from a management perspective, more information about the extent, occurrences and potential dispersal are important information. To gather such information solely based on field surveys are time-consuming and costly, and it has therefore been suggested to develop methods based on remote sensing. In this report we review different types of remote sensing data and how these can be used to map and monitor non-native species. Natural species distributions of Norway spruce and Scots pine were created based on available literature and existing remote sensing-based forest maps. The same maps were used to create a non-native species map, i.e. a map of areas where spruce occur outside its natural distribution. We evaluated the accuracy of the map by photo-interpretation, and assessed the consistency with other occurrence data. We further estimated the area of non-native species on a county and national level in Norway. The area covered by non-native species outside the natural distribution of spruce was estimated to be 1200 km2, with a standard error of 275 km2. A specific challenge when using remote sensing for mapping of non-native species in Norway is to separate species of the same genera. We therefore conducted a study in Fusa and Tysnes municipalities where we evaluated the ability to discriminate between Norway spruce and Sitka spruce using different types of remote sensing data. Data from Landsat 8 satellite images, aerial imagery and airborne laser scanning were tested. Slight to moderate ability to separate between the two species were found, with a best overall accuracy of 78%. The results suggest that Landsat 8 imagery can be used to discriminate between stands dominated by Norway spruce and Sitka spruce. Additional data from airborne sensors contributed not substantially in this case. Based on our own analyses and a review of relevant literature we discuss a possible establishment of a national mapping and monitoring programme for non-native tree species.Fremmede arter blir av mange betraktet som en trussel mot det biologiske mangfoldet. I Norge har flere bartrearter blitt innført med tanke på å bedre produksjonspotensialet i skogen, og flere av disse artene finnes nå på den offisielle «svartelista». For forvaltningen er det derfor et økende behov for kunnskap om utbredelse og potensiell spredning av disse artene. Det er både tidkrevende og kostbart å samle denne informasjonen utelukkende basert på feltundersøkelser, og det er derfor foreslått å utvikle metoder basert på fjernmåling for kartlegging og overvåkning. I denne rapporten har vi gjennomgått ulike typer fjernmålingsdata med hensyn på potensiale for kartlegging og overvåking av fremmede bartrær. Vi har videre etablert utbredelsekart for vanlig gran og furu basert på gjennomgang av eksisterende litteratur samt nasjonale skogkart fra fjernmålingsdata. De eksisterende skogkartene ble også bruk til å etablere et kart over fremmede bartrær, dvs. grantrær utenfor sin naturlige utbredelse. Nøyaktigheten av utbredelseskartet ble evaluert ved hjelp av fototolkning. Videre undersøkte vi hvordan kartet stemte overens med andre tilgjengelige kilder om lokaliteter av fremmede treslag, og estimerte arealet med fremmede bartrær på fylkes- og landsnivå. Arealet av fremmede bartrær utenfor den naturlige utbredelsen til gran i Norge ble estimert til 1200 km2, med en standardfeil på 275 km2. En spesifikk utfordring i fjernmåling av fremmede bartrær er å skille mellom arter av samme slekt. Vi etablerte en test i Fusa og Tysnes dere vi vurderte potensialet for å skille mellom vanlig gran og sitkagran med ulike typer fjernmålingdata. Fjernmålingsdata som ble testet var satellittbilder fra Landsat 8, flyfoto fra omløpsfotograferingen og flybåren laserskanning. Vi fant en svak til moderat evne til skille mellom de to artene. Den beste totale nøyaktigheten var på 78%, dvs. at 78% av lokalitetene var riktig bestemt. Testen indikerer at Landsat 8 bilder kan brukes til å skille mellom bestand med vanlig gran og sitkagran og at resultatene ikke bedres vesentlig ved bruk av flybårne sensorer. Basert på en litteraturgjennomgangen og våre analyser diskuterer vi en mulig etablering av et kartleggings- og overvåkingopplegg for fremmede treslag