Climate-Triggered Drought as Causes for Different Degradation Types of Natural Forests: A Multitemporal Remote Sensing Analysis in NE Iran

Climate-triggered forest disturbances are increasing either by drought or by other climate extremes. Droughts can change the structure and function of forests in long-term or cause large-scale disturbances such as tree mortality, forest fires and insect outbreaks in short-term. Traditional approaches such as dendroclimatological surveys could retrieve the long-term responses of forest trees to drought conditions; however, they are restricted to individual trees or local forest stands. Therefore, multitemporal satellite-based approaches are progressing for holistic assessment of climate-induced forest responses from regional to global scales. However, little information exists on the efficiency of satellite data for analyzing the effects of droughts in different forest biomes and further studies on the analysis of approaches and large-scale disturbances of droughts are required. This research was accomplished for assessing satellite-derived physiological responses of the Caspian Hyrcanian broadleaves forests to climate-triggered droughts from regional to large scales in northeast Iran. The 16-day physiological anomalies of rangelands and forests were analysed using MODIS-derived indices concerning water content deficit and greenness loss, and their variations were spatially assessed with monthly and inter-seasonal precipitation anomalies from 2000 to 2016. Specifically, dimensions of forest droughts were evaluated in relations with the dimensions of meteorological and hydrological droughts. Large-scale effects of droughts were explored in terms of tree mortality, insect outbreaks, and forest fires using field observations, multitemporal Landsat and TerraClimate data. Various approaches were evaluated to explore forest responses to climate hazards such as traditional regression models, spatial autocorrelations, spatial regression models, and panel data models. Key findings revealed that rangelands’ anomalies did show positive responses to monthly and inter-seasonal precipitation anomalies. However, forests’ droughts were highly associated with increases in temperatures and evapotranspiration and were slightly associated with the decreases in precipitation and surface water level. The hazard intensity of droughts has affected the water content of forests higher than their greenness properties. The stages of moderate to extreme dieback of trees were significantly associated with the hazard intensity of the deficit of forests’ water content. However, the stage of severe defoliation was only associated with the hazard intensity of forests’ greenness loss. Climate hazards significantly triggered insect outbreaks and forest fires. Although maximum temperatures, precipitation deficit, availability of soil moisture and forest fires of the previous year could significantly trigger insect outbreaks, the maximum temperatures were the only significant triggers of forest fires from 2010‒2017. In addition to climate factors, environmental and anthropogenic factors could control fire severity during a dry season. The overall evaluation indicated the evidence of spatial associations between satellite-derived forest disturbances and climate hazards. Future studies are required to apply the approaches that could handle big-data, use the satellite data that have finer wavelengths for large-scale mapping of forest disturbances, and discriminate climate-induced forest disturbances from those that induced by other biotic and abiotic agents.Klimagbedingte Waldstörungen nehmen entweder durch Dürre oder durch andere Klimaextreme zu. Dürren können langfristig die Struktur und Funktion der Wälder verändern oder kurzfristig große Störungen wie Baumsterben, Waldbrände und Insektenausbrüche verursachen. Traditionelle Ansätze wie dendroklimatologische Untersuchungen könnten die langfristigen Reaktionen von Waldbäumen auf Dürrebedingungen aufzeigen, sie sind aber auf einzelne Bäume oder lokale Waldbestände beschränkt. Daher werden multitemporale satellitengestützte Ansätze zur ganzheitlichen Bewertung von klimabedingten Waldreaktionen auf regionaler bis globaler Ebene weiterentwickelt. Es gibt jedoch nur wenige Informationen über die Effizienz von Satellitendaten zur Analyse der Auswirkungen von Dürren in verschiedenen Waldbiotopen. Daher sind weitere Studien zur Analyse von Ansätzen und großräumigen Störungen von Dürren erforderlich. Diese Forschung wurde durchgeführt, um die aus Satellitendaten gewonnenen physiologischen Reaktionen der im Nordosten Irans gelegenen kaspischen hyrkanischen Laubwälder auf klimabedingte Dürren auf lokaler und regionaler Ebene zu bewerten. Auf der Grundlage der aus MODIS-Daten abgeleiteten Indizes wurden die 16-tägigen physiologischen Anomalien von Weideland und Wäldern in Bezug auf Wassergehaltsdefizit und Grünverlust analysiert und ihre Variationen räumlich mit monatlichen und intersaisonalen Niederschlagsanomalien von 2000 bis 2016 bewertet. Insbesondere wurden die Dimensionen der Walddürre in Verbindung mit den Dimensionen der meteorologischen und hydrologischen Dürre bewertet. Großräumige Auswirkungen von Dürren wurden in Bezug auf Baumsterblichkeit, Insektenausbrüche und Waldbrände mit Hilfe von Feldbeobachtungen, multitemporalen Landsat- und TerraClimate Daten untersucht. Verschiedene Ansätze wurden ausgewertet, um Waldreaktionen auf Klimagefahren wie traditionelle Regressionsmodelle, räumliche Autokorrelationen, räumliche Regressionsmodelle und Paneldatenmodelle zu untersuchen. Die wichtigsten Ergebnisse zeigten, dass die Anomalien von Weideland positive Reaktionen auf monatliche und intersaisonale Niederschlagsanomalien aufweisen. Die Dürren in den Wäldern waren jedoch in hohem Maße mit Temperaturerhöhungen und Evapotranspiration verbunden und standen in geringem Zusammenhang mit dem Rückgang von Niederschlägen und des Oberflächenwasserspiegels. Die Gefährdungsintensität von Dürren hat den Wassergehalt von Wäldern stärker beeinflusst als die Eigenschaften ihres Blattgrüns. Die Stufen mittlerer bis extremer Baumsterblichkeit waren signifikant mit der Gefährdungsintensität des Defizits des Wassergehalts der Wälder verbunden. Das Ausmaß der starken Entlaubung hing jedoch nur mit der Gefährdungsintensität des Grünverlustes der Wälder zusammen. Die Klimagefahren haben zu deutlichen Insektenausbrüchen und Waldbränden geführt. Obwohl Maximaltemperaturen, Niederschlagsdefizite, fehlende Bodenfeuchte und Waldbrände des Vorjahres deutlich Insektenausbrüche auslösen konnten, waren die Maximaltemperaturen die einzigen signifikanten Auslöser von Waldbränden von 2010 bis 2017. Neben den Klimafaktoren können auch umweltbedingte und anthropogene Faktoren den Schweregrad eines Brandes während einer Trockenzeit beeinflussen. Die Gesamtbewertung zeigt Hinweise auf räumliche Zusammenhänge zwischen aus Satellitendaten abgeleiteten Waldstörungen und Klimagefahren. Weitere Untersuchungen sind erforderlich, um Ansätze anzuwenden, die mit großen Datenmengen umgehen können, die Satellitendaten in einer hohen spektralen Auflösung für die großmaßstäbige Kartierung von Waldstörungen verwenden und die klimabedingte Waldstörungen von denen zu unterscheiden, die durch andere biotische und abiotische Faktoren verursacht werden

Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

Influence of Bark-Beetle-Induced Tree Mortality on Slope Erosion in the San Juan Mountains of Ouray, CO, USA

Degradation of the natural environment has resulted from the destruction by bark-beetles at various geographic locations around Earth. Presently, widespread tree mortality is occurring in the San Juan Mountains of southwestern Colorado. Methods of remote sensing for classifying mountain pine beetle-induced tree mortality have been developed, which will be applied to study the destruction of forest caused by the Douglas-fir beetle (Dendroctonus pseudotsugae), and fir-engraver beetles (Scolytus ventralis) that has been occurring in the Ouray, Colorado, area over the last several years. Infestations of bark-beetles result in wide-spread tree mortality, and the loss of vegetation can result in increased rates of surface runoff and slope erosion. This presents the problem identified: Does a causal relationship exist between the destruction of trees by bark-beetles and increased rates of surface runoff and erosion on the slopes in the Ouray, Colorado area? The question posed was answered by accomplishing the following three objectives: 1. Determine if a link exists between rate of tree mortality and rate of erosion. 2. Model the rate of tree mortality and rate of erosion in the study area. 3. Provide a first approximation of surface runoff and sediment production rate. Potential soil erosion, calculated by the model, ranged from ~0 Mg/ha/yr projected erosion in areas of low slope to a projected erosion value of ~2,300 Mg/ha/yr in areas with extremely steep slopes and areas of high drainage output conducive to flowing water. The movement of materials down slope poses a potential hazard for the town of Ouray, CO, which is situated at the bottom of the valley. Calculated changes in the normalized difference vegetation index (NDVI) from 2005 to 2016 showed a maximum negative change -0.53 and the maximum increase of 0.57 with a mean change of 0.08 and standard deviation of 0.09. A chi-squared test yielded a p-value of 0.00, allowing me to reject the null hypothesis and accept the alternative hypothesis, HA, that NDVI changed over the course of this time series. This study also provided a preliminary method for predicting surface runoff using an NDVI time series as a classification threshold input. Areas above 0.38 NDVI had a range of surface runoff values between -390,000,000 mm to -0.2 mm per tree within each pixel with a mean value between -146,916,924 mm and -5.2 mm and standard deviation of 12.6 mm – 83,674,617 mm. Runoff in areas below 0.38 NDVI had a range between 0.05 to 8.53 mm of runoff per tree with a mean of 2.15 mm and a standard deviation of 1.66 mm within each 900 m^2 pixel. Because of limitations, I was unable to conclude whether or not the null hypothesis can be rejected in that bark-beetle-induced tree mortality does not lead to an increase in surface runoff

Detection of susceptible Norway spruce to bark beetle attack using PlanetScope multispectral imagery

Climate change-related acute or long-term drought stress can weaken forest ecosystems and result in widespread bark beetle infestations. Eurasian spruce bark beetle (Ips typographus L.) infestations have been occurring in Norway spruce [Picea abies (L.) Karst.]-dominated forests in central Europe including the Czechia. These infestations appear regularly, especially in homogeneous spruce stands, and the impact varies with the climate-induced water stress conditions. The removal of infected trees before the beetles leave the bark is an important step in forest pest management. Early identification of susceptible trees to infestations is also very important but quite challenging since stressed tree-tops show no sign of discolouration in the visible spectrum. We investigated if individual spectral bandwidths or developed spectral vegetation indices (SVIs), can be used to differentiate non-attacked trees, assumed to be healthy, from trees susceptible to attacks in the later stages of a growing season. And, how the temporal-scale patterns of individual bands and developed SVIs of susceptible trees to attacks, driven by changes in spectral characteristics of trees, behave differently than those patterns observed for healthy trees. The multispectral imagery from the PlanetScope satellite coupled with field data were used to statistically test the competency of the individual band and/or developed SVIs to differentiate two designated classes of healthy and susceptible trees. We found significant differences between SVIs of the susceptible and healthy spruce forests using the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI). The accuracy for both indices ranged from 0.7 to 0.78; the highest among all examined indices. The results indicated that the spectral differences between the healthy and susceptible trees were present at the beginning of the growing season before the attacks. The existing spectral differences, likely caused by water-stress stimuli such as droughts, may be a key to detecting forests susceptible to early infestations. Our introduced methodology can also be applied in future research, using new generations of the PlanetScope imagery, to assess forests susceptibility to bark beetle infestations early in the growing season

Development of a method for monitoring of insect induced forest defoliation - limitation of MODIS data in Fennoscandian forest landscapes

We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N. I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e. g. from Sentinel-2.Peer reviewe

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation ('green-attacks', May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the 'green-attack' stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the 'green-attack' symptoms.In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at 'green-attack' detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season

Table 2: Example applications of the use of remote sensing technologies to detect change in vegetation.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide