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

Estimating the threshold of detection on tree crown defoliation using vegetation indices from UAS multispectral imagery

Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost-effectively monitor the temporal and spatial damages in pine-oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017-2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93-96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91-93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale

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

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

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

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

ResearchBackground: Black alder (Alnus glutinosa) forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex (class Oomycetes), “alder Phytopththora”. Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity. Data obtained by unmanned aerial vehicles (UAVs) may be particularly useful for such tasks due to the high resolution, flexibility of acquisition and cost efficiency of this type of data. In this study, A. glutinosa decline was assessed by considering four categories of tree health status in the field: asymptomatic, dead and defoliation above and below a 50% threshold. A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles -red green blue (RGB) data were analysed using classical random forest (RF) and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony. A total of 34 remote sensing variables were considered, including a set of vegetation indices, texture features from the normalized difference vegetation index (NDVI) and a digital surface model (DSM), topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level. Results: The four categories identified by the RF yielded an overall accuracy of 67%, while aggregation of the legend to three classes (asymptomatic, defoliated, dead) and to two classes (alive, dead) improved the overall accuracy to 72% and 91% respectively. On the other hand, the confusion matrix, computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%, 80% and 94% for four-, three- and two-level classifications, respectively. Discussion: The study findings provide forest managers with an alternative robust classification method for the rapid, effective assessment of areas affected and non-affected by the disease, thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forestsinfo:eu-repo/semantics/publishedVersio

Pilkkumäntypistiäisen (Diprion pini L.) aiheuttaman männyn neulaskadon estimointi multitemporaalisia tutkakuvia käyttäen

The intensity and frequency of insect outbreaks have increased in Finland in the last decades and they are expected to increase even further in the future due to global climate change. 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 multitemporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine defoliation. The study area, Palokangas, is located in Ilomantsi district, Eastern-Finland and consists mainly even-aged Scots pine forests on relatively dry soils. Most of the forests in the area are young or middle-aged managed forests. The study material was comprised of multi-temporal ERS-2 and ENVISAT synthetic aperture radar (SAR) data. The images had been taken between the years 2001 and 2008. The field data consisted 16 sample plots which had been measured seven times between the years 2002 and 2009. In addition, eight sample plots were added afterwards to places which were known to have had cuttings during the study period. Three methods were tested to estimate Scots pine defoliation: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, it was 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, >20% 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.Hyönteistuhojen intensiteetti ja toistumistiheys ovat kasvaneet viime vuosikymmeninä ja niiden uskotaan asvavan entisestään ilmastonmuutoksen seurauksena. Vuosina 1998-2001 Suomessa tapahtui suurin koskaan tallennettu hyönteistuho, jossa hyönteistuho koski yli 500 000 hehtaaria metsää. Hyönteistuho oli pilkkumäntypistiäisen (Diprion pini L.) aiheuttama. Hyonteistuho on jatkunut Palokankaan alueella siitä lähtien. Jotta tällaisten tapausten monitorointiin löytyisi hyvä tapa, tämän tutkimuksen tarkoituksena oli testata multitemporaalista ERS-2 ja ENVISAT aineistoa männyn neulaskadon estimoinnissa. Tutkimusalue, Palokangas, sijaitsi itäisessä Suomessa Ilomantsin kunnan alueella. Metsä koostui pääasiassa tasaikäisestä männystä, joka sijaitsi kuivalla kangasmaalla. Suurin osa alueen metsistä on joko nuorta tai keski-ikäistä hoidettua metsää. Tutkimusaineisto koostui multitemporaalisesta ERS-2 ja ENVISAT synteettisisestä apertuuri tutka (SAR) aineistosta. Tutkakuvat olivat otettu vuosien 2001 ja 2008 välillä. Maastoaineisto koostui 16 koealasta, jotka oli mitattu seitsemän kertaa vuosien 2002 ja 2009 välillä. Lisäksi kahdeksan koealaa lisättiin tutkakuville kohtiin, joissa tiedettiin olleen hakkuita tutkimusperiodin aikana. Tutkimuksessa testattiin kolme erilaista metodia estimoida männyn neulaskatoa: ohjaamaton k-means klusterointi, ohjattu lineaarinen diskriminantti analyysi (LDA) sekä logistinen regressio. Neulaskadon lisäksi tutkimuksessa testattiin, käyttäen samoja metodeja, miten harvennetut ja päätehakatut metsäalueet erottuvat neulaskadosta kärsivistä metsistä. Kaksi erilaista keskiarvoistavaa häilyntäsuodatinta käytettiin suodattamattomien kuvien lisäksi, jotta voitiin testata suodattamisen vaikutusta SAR kuviin ja tuloksiin. Logistinen regressio antoi parhaat tulokset antaen kahden luokan (ei neulaskatoa, >20% neulaskato) luokitustarkkuudeksi 81,6% (kappa 0,62). LDA luokitustarkkuus kahdella luokalla oli parhaimmillaan 77,7% (kappa 0,54) ja k-means 72,8% (kappa 0,46). Yleisesti ottaen suurin häilyntäsuodatin (5 x 5 pikseliä) tuotti parhaimmat tulokset. Kun useampia luokkia lisättiin luokitukseen luokitustarkkuus laski. Tulokset olivat yleisesti hyviä, mutta koska tutkimuksessa oli rajoittavia tekijöitä, tulokset tulisi vahvistaa itsenäiselllä aineistolla ennen kuin tulokset voidaan kokonaisuudessaan hyväksyä. Tutkimuksen rajoittavaia tekijöitä olivat muun muassa maastoaineiston pieni koko, jonka vuoksi tarkkuuden laskenta aiheutti ongelmia (ei erillistä testiaineistoa), sekä meteorologisen aineiston puuttuminen tutkakuvien kuvaus päiviltä

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