Bayesian estimation of group sizes for a coastal cetacean using aerial survey data

Many small cetacean, sirenian, and pinniped species aggregate in groups of large or variable size. Accurate estimation of group sizes is essential for estimating the abundance and distribution of these species, but is challenging as individuals are highly mobile and only partially visible. We developed a Bayesian approach for estimating group sizes using wide‐angle aerial photographic or video imagery. Our approach accounts for both availability and perception bias, including a new method (analogous to distance sampling) for estimating perception bias due to small image size in wide‐angle images. We demonstrate our approach through an application to aerial survey data for an endangered population of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska. Our results strengthen understanding of variation in group size estimates and allow for probabilistic statements about the size of detected groups. Aerial surveys are a standard tool for estimating the abundance and distribution of various marine mammal species. The role of aerial photographic and video data in wildlife assessment is expected to increase substantially with the widespread uptake of unmanned aerial vehicle technology. Key aspects of our approach are relevant to group size estimation for a broad range of marine mammal, seabird, other waterfowl, and terrestrial ungulate species.     

Bloom forming cyanobacterial complexes co-occurring in a subtropical large reservoir: validation of dominant eco-strategies

In this study, we analyse the spatial distribution of cyanobacterial summer blooms in a large subtropical reservoir located in the Uruguay River, from 2007 to 2011; these extraordinary algal growth events are mainly represented by scum-forming and nitrogen-fixing eco-strategists of the Dolichospermum and Microcystis genera. The use of the eco-strategists approach, based on ecophysiological work and field observations, allowed us to explain the differences in the distribution pattern and temporal dynamics of both cyanobacterial complexes. Spatial differences were produced due to much higher and fluctuating cyanobacterial abundances at the right margin of the reservoir and at the littoral areas closer to the dam. Satellite imagery (LANDSAT 5 TM) clearly depicted the stronger algal development in the reservoir arms and in the section closer to the dam. The Microcystis spp. complex achieved higher density than the Dolichospermum spp. complex. We hypothesise that the hydrological cycle explains the inter-annual fluctuations of the intensity and frequency of cyanobacterial blooms, and that spatial differences in cyanobacterial presence between the reservoir arms, its margins and the main channel is mainly a response to morphometrical and hydrological characteristics.     

Have we been successful? Monitoring horizontal forest complexity for forest restoration projects

Forest management today often seeks to restore ecological integrity and enhance human well‐being by increasing forest complexity, resilience, and functionality. However, effective and financially expedient monitoring of forest complexity is challenging. In this study, we developed a practical and inexpensive technique to measure horizontal forest complexity. This monitoring method uses intuitively understandable data (imagery) and facilitates stakeholder participation in the adaptive management process within collaborative projects. We used this technique to determine if current restoration projects are successfully achieving their spatial restoration goals. We focused on the Colorado Front Range Landscape Restoration Initiative (CFRLRI) as a representative of the typical collaborative restoration projects underway in formerly fire‐dependent dry conifer forests. The developed monitoring method is practical and cost‐effective by using free aerial imagery to map, quantify, and analyze the distribution of canopy cover pre‐ and post‐treatment. We found the CFRLRI has successfully reduced canopy cover (from 44 to 26% on average) and increased some aspects of horizontal forest complexity. The application of these monitoring techniques has allowed the CFRLRI collaborative group to objectively quantify changes to horizontal forest complexity, and has facilitated stakeholder communication about forest spatial patterns. These methods could be adapted for use by other similar forest restoration projects around the world by utilizing increasingly available satellite or aerial imagery.     

Random Forest characterization of upland vegetation and management burning from aerial imagery

Aim The upland moorlands of Great Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create gamebird habitat and there is concern over whether this is driving long‐term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high‐resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors. Location  Peak District National Park, England, UK. Methods Colour and infrared aerial photographs were classified into seven dominant land‐cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of the vegetation classes and managed burning using detrended correspondence analysis. Results Classification accuracy was c. 95% and produced a 5‐m resolution map for 514 km² of moorland. Cover classes were highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt. Main conclusions Random Forest classification of aerial imagery is an efficient method for producing high‐resolution maps of upland vegetation. These may be used to monitor long‐term changes in vegetation and management burning and infer species–environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.     

Riparian monitoring using 2-cm GSD aerial photography

Riparian monitoring is a key aspect of sustainable resource management and is mandated by US federal law for federal land management agencies. However, it is an endeavor hampered by rising manpower costs and time-consuming travel and methods. These limitations tend to reduce sampling intensity per reach of stream and limit monitoring to the larger waterways of management units—limitations that reduce the accuracy of inferences derived from resulting data with consequential reductions in the effectiveness of landscape-level resource management. We tested the utility of low-altitude, high-resolution, intermittent aerial digital imagery for relatively inexpensive, high-intensity sampling in a watershed inhabited by the Lahonton Cutthroat trout, a species listed as threatened under provisions of the US Endangered Species Act. Measurements gleaned from the aerial imagery included late-summer open water width, number and location of late-summer dry channels, widths of riparian areas and willow coverage. All measurements were georeferenced to allow spatial data display. Riparian proper functioning condition (PFC) was assessed from the imagery by a USDI, Bureau of Land Management team. These assessments were compared to similar on-the-ground assessments made during the preceding year. PFC assessments from aerial photography were made using an average 4 staff hours per stream compared to an estimated 36 staff hours per stream for ground PFC assessments. The two assessment methods yielded roughly comparable results. We conclude that riparian-condition assessments from 2-cm GSD digital aerial imagery allowed increased sample intensity (and thus increased inference accuracy) and that it did so in our study at a cost less than half that of conventional ground-based methods. We recommend the acquisition and analysis of 2-cm GSD digital aerial imagery be further trialed for its utility and cost efficiency in ecological monitoring of riparian systems.     

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.     

Co-occurrence Patterns of the Dengue Vector Aedes aegypti and Aedes mediovitattus, a Dengue Competent Mosquito in Puerto Rico

Aedes aegypti is implicated in dengue transmission in tropical and subtropical urban areas around the world. Ae. aegypti populations are controlled through integrative vector management. However, the efficacy of vector control may be undermined by the presence of alternative, competent species. In Puerto Rico, a native mosquito, Ae. mediovittatus, is a competent dengue vector in laboratory settings and spatially overlaps with Ae. aegypti. It has been proposed that Ae. mediovittatus may act as a dengue reservoir during inter-epidemic periods, perpetuating endemic dengue transmission in rural Puerto Rico. Dengue transmission dynamics may therefore be influenced by the spatial overlap of Ae. mediovittatus, Ae. aegypti, dengue viruses, and humans. We take a landscape epidemiology approach to examine the association between landscape composition and configuration and the distribution of each of these Aedes species and their co-occurrence. We used remotely sensed imagery from a newly launched satellite to map landscape features at very high spatial resolution. We found that the distribution of Ae. aegypti is positively predicted by urban density and by the number of tree patches, Ae. mediovittatus is positively predicted by the number of tree patches, but negatively predicted by large contiguous urban areas, and both species are predicted by urban density and the number of tree patches. This analysis provides evidence that landscape composition and configuration is a surrogate for mosquito community composition, and suggests that mapping landscape structure can be used to inform vector control efforts as well as to inform urban planning.     

A Neural Network-Based Optimal Spatial Filter Design Method for Motor Imagery Classification

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.     

Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: implications for understanding forest regeneration and carbon storage

Treefall gaps play an important role in tropical forest dynamics and in determining above-ground biomass (AGB). However, our understanding of gap disturbance regimes is largely based either on surveys of forest plots that are small relative to spatial variation in gap disturbance, or on satellite imagery, which cannot accurately detect small gaps. We used high-resolution light detection and ranging data from a 1500 ha forest in Panama to: (i) determine how gap disturbance parameters are influenced by study area size, and the criteria used to define gaps; and (ii) to evaluate how accurately previous ground-based canopy height sampling can determine the size and location of gaps. We found that plot-scale disturbance parameters frequently differed significantly from those measured at the landscape-level, and that canopy height thresholds used to define gaps strongly influenced the gap-size distribution, an important metric influencing AGB. Furthermore, simulated ground surveys of canopy height frequently misrepresented the true location of gaps, which may affect conclusions about how relatively small canopy gaps affect successional processes and contribute to the maintenance of diversity. Across site comparisons need to consider how gap definition, scale and spatial resolution affect characterizations of gap disturbance, and its inferred importance for carbon storage and community composition.     

Evaluating performance of aerial survey data in elephant habitat modelling

Aerial survey data are widely used to model distribution of wildlife. However, their performance in habitat modelling remains largely untested. We used aerial survey and satellite‐linked Global Positioning System (GPS) collar data for elephants, to test (i) whether there is an optimal spatial resolution of predictor variables at which habitat models based on aerial survey data that are uncorrected for locational error can accurately predict elephant habitat and (ii) whether habitat models based on these data sets can accurately predict the presence of elephants in closed woodland habitats. We applied maximum entropy modelling (Maxent) to these data sets and used the Normalised Difference Vegetation Index (NDVI) as well as distance from water points as the habitat predictors to answer these questions. Our results demonstrate better ability of aerial survey data to predict elephant presence at the coarser spatial resolution of 1000 m of both predictor variables. Habitat models derived from aerial survey data underpredicted elephant presence in more closed woodland habitats than those derived from GPS collar data. This result implies that elephants located under dense tree canopies are likely missed during an aerial survey. Our study is one of the first to empirically test and report results on the poor performance of aerial survey data in habitat modelling especially in dense woodlands.     

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint

Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly using utilization distributions (UDs), including objective assessment of the spatially explicit overlap between wildlife and disturbance, and the ability to track trends in this relationship over time. Here, we examined how central mountain woodland caribou (Rangifer tarandus caribou) distribution changed over time in relation to (i) anthropogenic disturbance, baseline range (defined using telemetry data from 1998 to 2005), and alpine habitat; and (ii) interannual climate variation (North Pacific Index; NPI). We developed seasonal UDs for caribou in west‐central Alberta and east‐central British Columbia, Canada, monitored with GPS collars between 1998 and 2013. We mapped the cumulative annual density of disturbance features within caribou range and used indices of overlap to determine the spatial relationship and trend between caribou UDs, anthropogenic disturbance, baseline range, alpine habitat, and the NPI. Anthropogenic disturbance increased over time, but the overlap between caribou UDs and disturbance did not. Caribou use of alpine habitat during spring, fall, and late winter increased over time, concurrent with a decrease in use of baseline range. Overlap between caribou UDs and disturbance increased during spring and fall following relatively cold, snowy winters (high NPI), but overall, climate did not explain changes in caribou distribution over time. We provide evidence supporting the hypothesis that caribou populations adjust their spatial distribution in relation to anthropogenic landscape change. Our findings could have implications for population persistence if distributional shifts result in greater use of alpine habitat during winter. Monitoring long‐term changes in the distribution of populations is a valuable component of conservation planning for species at risk in disturbed landscapes.     

Density-dependent habitat selection alters drivers of population distribution in northern Yellowstone elk

Although it is well established that density dependence drives changes in organismal abundance over time, relatively little is known about how density dependence affects variation in abundance over space. We tested the hypothesis that spatial trade-offs between food and safety can change the drivers of population distribution, caused by opposing patterns of density-dependent habitat selection (DDHS) that are predicted by the multidimensional ideal free distribution. We addressed this using winter aerial survey data of northern Yellowstone elk (Cervus canadensis) spanning four decades. Supporting our hypothesis, we found positive DDHS for food (herbaceous biomass) and negative DDHS for safety (openness and roughness), such that the primary driver of habitat selection switched from food to safety as elk density decreased from 9.3 to 2.0 elk/km². Our results demonstrate how population density can drive landscape-level shifts in population distribution, confounding habitat selection inference and prediction and potentially affecting community-level interactions.     

Large Mammal Distribution in a Transfrontier Landscape: Trade‐offs Between Resource Availability and Human Disturbance

Understanding factors that affect the persistence of charismatic megafauna in human‐dominated landscapes is crucial to inform conservation decision‐making and reduce human‐wildlife conflict. We assessed the effect of environmental and anthropogenic factors at different landscape and management scales in predicting the distribution of African elephant (Loxodonta africana) within the Greater Mapungubwe Transfrontier Conservation Area in Southern Africa. We combined aerial distribution counts over a 12‐yr period with 14 variables, representing food availability, landscape, and anthropogenic effects, into generalized linear models. Generalized linear models were run for the broader landscape, as well as three separate management units within the broader landscape, namely ecotourism, trophy hunting, and a combination of hunting and ecotourism. Human activities within different management units forced elephant to trade‐off between disturbance avoidance, and good food and water availability. In addition, the important predictors of elephant distribution within each of the management units differed from the predictors at the broader landscape. Overall, our results suggest that at the fine scale, elephant are constraint by factors that may be masked at the broader landscape scale. We suggest that accounting for anthropogenic disturbance is important in determining the distribution of large, wide‐ranging, mammal species in increasingly human‐dominated landscapes, and that modeling needs to be done at the spatial scales at which conservation decisions are made.     

Unlocking the potential of Google Earth as a tool in invasion science

Distribution data are central to many invasion science applications. The shortage of good information on the distribution of alien species and their spatial dynamics is largely attributable to the cost, effort and expertise required to monitor these species over large areas. Virtual globes, particularly Google Earth, are free and user-friendly software which provide high-resolution aerial imagery for the entire globe. We suggest this has enormous potential for invasion science. We provide suggestions and tools for gathering data on the distribution and abundance of invasive alien trees using visual interpretation of Google Earth imagery, and propose how these data may be used for a number of purposes, including calculating useful metrics of invasions, prioritising species or areas for management and predicting potential distributions of species. We also suggest various practical uses of Google Earth, such as providing a tool for early detection of emerging invasions, monitoring invasions over time, and to help researchers and managers identify suitable field study sites. Virtual globes such as Google Earth are not without limitations and we provide guidance on how some of these can be overcome, or when imagery from Google Earth may not be fit for invasion science purposes. Because of Google Earth’s huge popularity and ease of use, we also highlight possibilities for awareness-raising and information sharing that it provides. Finally, we provide the foundations and guidelines for a virtual global network of sentinel sites for early detection, monitoring and data gathering of invasive alien trees, which we propose should be developed as part of a “citizen science” effort. There has been limited use of virtual globes by invasion scientists and managers; it is our hope that this paper will stimulate their greater use, both within the field of invasion science and within ecology generally.     

Challenges and opportunities in synthesizing historical geospatial data using statistical models

We classified land cover types from 1940s historical aerial imagery using Object Based Image Analysis (OBIA) and compared these maps with data on recent cover. Few studies have used these kinds of maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. We studied the change in woody cover at four sites in California's North Coast using historical (1948) and recent (2009) high spatial resolution imagery. We classified the imagery using eCognition Developer and aggregated the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. We used Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. We explored the relative influences on current percent woody cover of topographic variables (grouped using principal component analysis) reflecting water retention capacity, exposure, and within-site context, as well as historical percent woody cover and geographical coordinates. We estimated these models for pixel sizes of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. We found that historical woody cover had a consistent positive effect on current woody cover, and that the spatial autoregressive term in the model was significant even after controlling for historical cover. Specific topographic variables emerged as important for different sites at different scales, but no overall pattern emerged across sites or scales for any of the topographic variables we tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.     

Application of QuickBird and aerial imagery to detect Pinus radiata in remnant vegetation

The invasion of Pinus radiata from long‐term established plantations is contributing to the degradation of fragmented and isolated remnants of native vegetation. Within the south‐east of South Australia, the 20 vegetation communities that occur within 500 m of a plantation edge are at risk, including nine state threatened communities. To plan effective mitigation strategies, the current extent and distribution of P. radiata needs to be ascertained. High spatial resolution, multispectral QuickBird imagery and aerial photography were used to classify P. radiata within eucalypt and acacia woodlands, melaleuca shrubland, modified pasture and an Eucalyptus globulus plantation. Unsupervised classification of aerial photography gave the best result showing reasonable conformity with the observed distribution of P. radiata at the site scale. However, the 9.4 ± 13.5 (SD) cover classified in the quadrats sampled for the accuracy assessment exceeded the 1.4 ± 2.4 (SD) P. radiata cover determined from an independent dataset. Only 30.1 ± 37.9% (SD) of trees within the quadrats and 9.40 ± 13.49% (SD) of their foliage cover were classified. Trees detected by partial classification of canopy were positively correlated with both tree height and canopy diameter. Overall, the low detection rates were attributed to insufficient spectral resolution. Using higher resolution imagery, together with an object‐based image analysis or combination of multispectral and airborne digital image classification, restricted to large emergent adult trees using LiDAR analysis, is likely to improve adult P. radiata detection accuracy.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

A remote sensing approach to estimating harvestable kelp biomass

Regulations of the Alaska Department of Fish and Game require that all fisheries in the state have a harvest management plan. In southeast Alaska two species of floating kelps, Nereocystis luetkeana and Alaria fistulosa, have been commercially harvested since 1992 for use as agrochemicals by the Alaska Kelp Company. However, there is currently no harvest management plan for this fishery. The lack of a formalized management plan is one factor that has kept the kelp industry from expanding in the state. We have employed an aerial digital multispectral imaging system (DMSC) calibrated with ground truthing for performing such an assessment. The system can be flown at varying altitudes to achieve spatial resolutions ranging from 0.5 to 2 m. Rapid ground truthing techniques were developed using morphometric measurements to predict biomass. Analysis of the DMSC imagery showed that good correlations could be developed between the multispectral imagery and kelp biomass estimates collected at the ground-truth sites. Repeatable estimates of kelp bed area derived from the multispectral imagery could be made at varying tidal levels. However, broad scale maps of kelp biomass suitable for estimating harvest rates could not be made at different tide levels. Multispectral imagery suitable for this purpose must be collected at a standard tidal level.     

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.     

Anomalous outbreaks of an invasive defoliator and native bark beetle facilitated by warm temperatures,changes in precipitation and interspecific interactions

Biotic disturbance agents such as insects can be highly responsive to climatic change and have widespread ecological and economic impacts on forests. Quantifying the responses of introduced and native insects to climate, including how dynamics of one agent may mediate those of another, is important for forecasting disturbance and associated impacts on forest structure and function. We investigated drivers of outbreaks by larch casebearer Coleophora laricella, an invasive defoliator, and eastern larch beetle Dendroctonus simplex, a native, tree‐killing bark beetle, on tamarack Larix laricina from 2000 to in Minnesota, USA. We evaluated the utility of temporal, spatial and climatic variables in predicting the presence/absence of outbreaks of each insect in cells of rasterized aerial survey data. The role of defoliation by larch casebearer in outbreaks of eastern larch beetle was also investigated. For both species, the most important predictors of outbreak occurrence were proximity of conspecific outbreaks in space and time. For larch casebearer, outbreak occurrence was positively associated with spring precipitation and warmer growing seasons. Outbreak occurrence of eastern larch beetle was positively associated with warmer and dryer years and was more likely in cells with prior defoliation by larch casebearer. Our results demonstrate that climate can drive large scale outbreaks of introduced and non‐native disturbance agents on a single host species, and that interactions at the tree level between such agents may scale up to manifest across large temporal and spatial scales.