Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR

Peer reviewe

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.Peer reviewe

Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery ­­ : Case study in Norway Spruce-dominated stands in Southern Finland

The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254 m2 (9-m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery.Peer reviewe

Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.JRC.H.3-Forest Resources and Climat

Retrieval of forest aboveground biomass and volume with airbone scanning LiDAR

Peer reviewe

Stand Delineation of Pinus sylvestris L. Plantations Suffering Decline Processes Based on Biophysical Tree Crown Variables: A Necessary Tool for Adaptive Silviculture

Many planted Pinus forests are severely affected by defoliation and mortality processes caused by pests and droughts. The mapping of forest tree crown variables (e.g., leaf area index and pigments) is particularly useful in stand delineation for the management of declining forests. This work explores the potential of integrating multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data for stand delineation based on selected tree crown variables in Pinus sylvestris plantations in southern Spain. Needle pigments (chlorophyll and carotenes) and leaf area index (LAI) were quantified. Eight vegetation indices and ALS-derived metrics were produced, and seven predictors were selected to estimate and map tree crown variables using a Random Forest method and Gini index. Chlorophylls a and b (Chla and Chlb) were significantly higher in the non-defoliated and moderately defoliated trees than in severely defoliated trees (F = 14.02, p < 0.001 for Chla; F = 13.09, p < 0.001 for Chlb). A similar response was observed for carotenoids (Car) (F = 14.13, p < 0.001). The LAI also showed significant differences among the defoliation levels (F = 26.5, p < 0.001). The model for the chlorophyll a pigment used two vegetation indices, Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI); three WV-2 band metrics, and three ALS metrics. The model built to describe the tree Chlb content used similar variables. The defoliation classification model was established with a single vegetation index, Green Normalized Difference Vegetation Index (GNDVI); two metrics of the blue band, and two ALS metrics. The pigment contents models provided R2 values of 0.87 (Chla, RMSE = 12.98%), 0.74 (Chlb, RMSE = 10.39%), and 0.88 (Car, RMSE = 10.05%). The cross-validated confusion matrix achieved a high overall classification accuracy (84.05%) and Kappa index (0.76). Defoliation and Chla showed the validation values for segmentations and, therefore, in the generation of the stand delineation. A total of 104 stands were delineated, ranging from 6.96 to 54.62 ha (average stand area = 16.26 ha). The distribution map of the predicted severity values in the P. sylvestris plantations showed a mosaic of severity patterns at the stand and individual tree scales. Overall, the findings of this work underscore the potential of WV-2 and ALS data integration for the assessment of stand delineation based on tree health status. The derived cartography is a relevant tool for developing adaptive silvicultural practices to reduce Pinus sylvestris mortality in planted forests at risk due to climate change

Estimation of scots pine defoliation by the common pine sawfly (\u3ci\u3eDiprion pini\u3c/i\u3e L.) using multi-temporal radar data

In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multi-temporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine (Pinus sylvestris L.) defoliation. Three methods were tested: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, I assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, \u3e20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates

Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, dispersal and geographic distributions. The outbreak pattern of forest pests in Finland has evidently changed during the last decade. Projection of shifts in distributions of insect-caused forest damages has become a critical issue in the field of forest research. The Common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini has resulted in severe growth loss and mortality of Scots pine (Pinus sylvestris L.) (Pinaceae) in eastern Finland. In this study, tree-wise defoliation was estimated for five different needle loss category classification schemes and for 10 different simulated airborne laser scanning (ALS) pulse densities. The nearest neighbor (NN) approach, a nonparametric estimation method, was used for estimating needle loss of 701 Scots pines, using the means of individual tree features derived from ALS data. The Random Forest (RF) method was applied in NN-search. For the full dense data (~20 pulses/m2), the overall estimation accuracies for tree-wise defoliation level varied between 71.0% and 86.5% (kappa-values of 0.56 and 0.57, respectively), depending on the classification scheme. The overall classification accuracies for two class estimation with different ALS pulse densities varied between 82.8% and 83.7% (kappa-values of 0.62 and 0.67, respectively). We conclude that ALS-based estimation of needle losses may be of acceptable accuracy for individual trees. Our method did not appear sensitive to the applied pulse densities.Peer reviewe

Effect of forest health and structure to the relative surface temperature captured by airborne thermal imagery : case study in Norway Spruce-dominated stands in Southern Finland

Metsän terveydentilan ja rakenteen vaikutusta ilmalämpökuvauksella hankittuun suhteelliseen pintalämpötilaan tutkittiin kuusikoissa Etelä-Suomessa. Latvuksen pintalämpötilan on tiedetty jo pitkään olevan hyödyllinen kasvillisuuden vesitasapainon tarkkailussa. Viimeaikaiset tutkimukset ovat osoittaneet sen potentiaalin myös kasvillisuuden terveydentilan tarkkailussa. Aineistona olivat ilmalämpökuvasto, ilmalaserkeilausaineisto ja kenttämittaukset tutkimusalueelta. Suhteellinen pintalämpötila korreloi vahvimmin negatiivisesti runkotilavuuden logaritmin, keskipituuden ja pohjapintaalan logaritmin kanssa 254m2 (9-m ympyräkoeala) resoluutiolla. Toisin sanoen, pidemmät ja vanhemmat metsiköt olivat kylmempiä pintalämpötilaltaan. Lisäksi laserkeilauspiirteitä, kuten korkeusprosenttiosuuksia ja latvuston peittävyyttä, verrattiin pintalämpötilaan. Latvuston pintamallin keskihajonta, korkeuspiirteet ja latvuston peittävyys korreloivat vahvimmin negatiivisesti pintalämpötilan kanssa. Korkeampia pintalämpötiloja havaittiin harsuuntuneissa latvuksissa keskimäärin osoittaen, että lämpökuvat voisivat tuoda lisäinformaatiota metsän terveydentilan luokitteluun. Harsuuntuneiden koealojen pintalämpötilat vaihtelivat kuitenkin merkittävästi. Huomattiin myös, että pintalämpötilojen erotus latvuston ja maan välillä oli suurempi harsuuntuneissa koealoissa. Tulosten perusteella voidaan todeta, että metsän terveydentila ja rakenne vaikuttavat ilmalämpökuvauksella hankittuun pintalämpötilaan ja että nämä vaikutukset tulisivat ottaa huomioon metsänterveydentilaa kartoittaessa lämpökuvien avulla.The effect of forest health and structure to the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Canopy surface temperature has long been recognized useful in monitoring vegetation water status. Recent studies have shown also its potential in monitoring vegetation health. Airborne thermal imagery, Airborne Light Detection and Ranging (LiDAR) and field measurements were acquired from the area of interest (AOI). The relative surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and logarithm of basal area at resolution of 254m2 (9-m radius). In other words, taller and older stands had colder surface temperatures. In addition, LiDAR metrics, such as height percentiles and canopy cover percentage, were compared with surface temperature. Standard deviation of canopy height model, height features (H90, CHM_max) and canopy cover percentage were most strongly negatively correlated with the surface temperature. On average, higher surface temperatures were detected in defoliated canopies indicating that thermal images may provide some additional information for classifying forests health status. However, the surface temperature of defoliated plots varied considerably. It was also found that surface temperature differences between canopy and ground responses were higher in defoliated plots. Based on the results, forest health and structure affect to the surface temperature captured by airborne thermal imagery and these effects should be taken into account when developing forest health mapping applications using thermal imagery

Mapping the risk of forest wind damage using airborne scanning LiDAR

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts. The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage. To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. The probability of wind-induced forest damage (PDAM) in southern Finland (61°N, 23°E) was modelled for a 173 km2 study area of mainly managed boreal forests (dominated by Norway spruce and Scots pine) and agricultural fields. Wind damage occurred in the study area in December 2011. LiDAR data were acquired prior to the damage in 2008. High spatial resolution aerial imagery, acquired after the damage event (January, 2012) provided a source of model calibration via expert interpretation. A systematic grid (16 m x 16 m) was established and 430 sample grid cells were identified systematically and classified as damaged or undamaged based on visual interpretation using the aerial images. Potential drivers associated with PDAM were examined using a multivariate logistic regression model. Risk model predictors were extracted from the LiDAR-derived surface models. Geographic information systems (GIS) supported spatial mapping and identification of areas of high PDAM across the study area. The risk model based on LiDAR data provided good agreement with detected risk areas (73 % with kappa-value 0,47). The strongest predictors in the risk model were mean canopy height and mean elevation. Our results indicate that open-access LiDAR data sets can be used to map the probability of wind damage risk without field data, providing valuable information for forest management planning