Remote Sensing Methods and Applications for Detecting Change in Forest Ecosystems

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast

Fire models and methods to map fuel types: The role of remote sensing.

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information andmethods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping

Spectral Response and Spatial Pattern of Fraser Fir Mortality and Regeneration, Great Smoky Mountains, USA

High elevation Fraser fir (Abies fraseri) forests of the Southern Appalachians have undergone widespread mortality since the introduction of the balsam woolly adelgid in the 1950s. Resulting changes in ecosystem pattern and process (e.g., stand dynamic processes) have greatly affected floral and faunal communities. In this project, we integrated field observations, geographic information system topographic models, and 1988–1998 satellite imagery to analyze spatial and temporal conditions of Fraser fir and spruce-fir ecosystems in Great Smoky Mountains National Park. Tasseled cap indices (brightness, greenness, and wetness) and associated spectral changes for Landsat TM digital data were statistically modeled by topographic variables. Spectral changes were recorded using change vector analysis (CVA) and spherical geometry at multiple scales: individual sites, local ridges, and across the east-west gradient of the study area. Significant relationships were found between elevation and observed spectral changes and among mountain sites representing the east-west chronosequence of adelgid infestation. Topographic derivatives were related to tasseled cap and CVA measures in summary statistics, regression, and correlation analysis, revealing significantly different mortality and regeneration pathways that were a function of topographic settings. Geographic variations of these vectors also detail the scope of east-west and localized upslope progression of fir mortality. The application of CVA provided the ability to summarize variation in spectral changes (magnitude and direction) and to ascribe measures to mortality and regeneration processes

Center for Research on Sustainable Forests 2019 Annual Report

The Center for Research on Sustainable Forests (CRSF) continued its evolution as University of Maine research center in FY21 with several new and ongoing initiatives. Despite the continual challenges created by the global pandemic, dedicated CRSF faculty, staff and students have furthered our collaborations and generated numerous outcomes for our stakeholders. Of particular note this past FY, the Northeastern States Research Cooperative (NSRC) awarded 13 new projects across the region, including three involving the University of Maine; the Forest Climate Change Initiative’s Science and Practice monthly webinar series organized with the Forest Stewardship Guild attracted strong participation both internal/external to Maine; and release of th Natural Climate Solutions for Forestry & Agriculture Final Report outlining the potential of alternative management strategies for increasing carbon sequestration. In addition, several external grants were received in FY21 from NASA Carbon Monitoring Systems, a NASA GEDI, several from the USDA, and one from the Maine Department of Inland Fisheries & Wildlife, which help to continue grow the CRSF research program and build capacity within the center

Geostatistical approach to spatial analysis of forest damage

Background and Purpose: Significantly increased forest damage has recently been observed in the Republic of Croatia, as well as increased proportion of unplanned felling in prescribed cuts, which has negative repercussions for sustainable management. The objective of this study was is to explore the possibilities of simple and reliable detection, inventorying (mapping) and monitoring forest health condition by means of color infrared (CIR) imagery and geostatistical methods. Materials and Methods: Four trees (crowns) closest to the point of the raster (100 ´ 100 m) which was set up in the digital orthophoto for the area, were interpreted in CIR images. Forest damage indicators, mean damage and damage index were calculated for the whole area under observation. The assessment and identification of spatial distribution of these damage indicators were performed using raster point data, from which a random (966 points) and a systematic (445 points) sample were created. The results on forest damage acquired by interpreting CIR images were used for geostatistical analysis. A model of theoretical semivariograms provided parameters which were used for interpolation of both damage indicators with ordinary kriging. Continuous maps of damage degree distribution were then constructed. The results of interpolation were tested with the cross-validation method. Results and discussion: Damage indicator maps are the result of the following: data variability, sampling intensity and method, form of experimental and theoretical semivariograms which were subsequently used to compute kriging matrices, method of selecting a particular semivariogram, assessment accuracy, the choice of interpolation methods (kriging, cokriging, stochastic simulation, inverse distance, etc.). Tree damage generally does not have regular, but rather random spatial distribution. This is why the primary aim in identifying forest damage is to incorporate the whole area of interest into sampling. Sampling intensity should be adapted to the required accuracy and to the time and funds at our disposal. Conclusions: This research relies on the application of CIR aerial photographs and geostatistical tools in spatial analysis of forest damage. Continuous maps of damage indicators acquired with kriging provide a better insight into the spatial distribution of damage than do thematic maps obtained by interpreting CIR aerial imagery on the basis of a systematic sample (the raster method). Integration of interpretation results of CIR aerial images and geostatistical approach ensures a more precise distribution of damage indicators, and consequently, the possibility of better spatial analysis of the occurrence, trends and development of damage in the study area

Center for Research on Sustainable Forests 2020 Annual Report

FY20 saw exciting changes in CRSF with several new initiatives launched, while progress continues on many other ongoing efforts. In particular, FY20 saw the start of two National Science Foundation funded and CRSF-led research projects. The first is the INSPIRES project, a multi-year research collaboration between Maine, New Hampshire, and Vermont focused on harnessing Big Data to better understand and forecast the region’s forest given current as well as future uncertainties. The other effort was a successful Phase 3 reboot of the National Science Foundation Industry-University Collaborative Research Center, Center for Advanced Forestry System (CAFS), for which I have served as Director since 2016. CAFS provides direct connections among several additional universities across the United States, including North Carolina State University, Oregon State University, Purdue University, University of Georgia, University of Idaho, and University of Washington, as well as to forest industry partners. Phase 3 of CAFS will be a five-year effort and, I hope, will lead to the successful graduation of the IUCRC

ADVANCING THE TERRESTRIAL ECOLOGICAL UNIT INVENTORY WITHIN THE WHITE MOUNTAIN NATIONAL FOREST USING LiDAR

Forest land managers need ecological classification to assess and describe resource conditions, vegetation conditions, outcomes resulting from various management prescription scenarios, and communicate environmental effects of land management planning alternatives. However, there is a need to incorporate more ecological classification into the land management plans. The U.S. Forest Service’s approach, the Terrestrial Ecological Unit Inventory (TEUI), relies heavily on field data collection and verification of map unit delineations that is time-consuming and costly. Traditional mapping methods far exceed the current financial capacity of the U.S. Forest Service. In order to justify new ecological classification mapping approaches, there needs to be significant evidence that new approaches will create equivalent or superior map products, reduce costs, improve efficiencies and maybe improve accuracy. Therefore the objectives of chapter 2 were to use the Soil Inference Engine (SIE) to partition the areal extent of a project area watershed in the White Mountain National Forest (WMNF) using on topographic metrics derived from Light Detection and Ranging (LiDAR) data including both timber managed and un-managed timber production lands. A total of 189 plots were randomly generated within strata, based on parent material, and topographic metrics using a stratified random sampling approach. The number of plots calculated for stratified random sampling was predominately determined by the number of strata, the acres of timber-managed areas, and budget. 172 of those plots had both vegetation and soils information recorded. The results from chapter 2 showed that stratified random sampling using LiDAR-derived topographic metrics as SIE data inputs were sufficient in capturing the environmental gradients required by the U.S. Forest Service ecological classification requirements. Additionally, 10 New Hampshire Natural sensitive indicator species were located and recorded in 16% of plots stratified by topographic metrics and parent material. These results suggest this new approach to ecological classification on the WMNF improved the accuracy and efficiency in delineating ecological areas as well as locating the presence of nutrient rich areas. The objectives of chapter 3 used nonmetric multidimensional scaling (NMDS) to assess the relationship between understory species and environmental variables, including parent material, slope, aspect, elevation, and wetness. The results from chapter 3 showed how both soil properties and topographic metrics correlated with understory species in ordination space. NMDS ordination explained 81.1% of the cumulative variation of understory species presence in three dimensions using soil properties and topographic metrics with a final stress value of 17.3 and a p-value of 0.04. NMDS results also suggested that understory species clustered distinctly within New Hampshire Natural Community types. These results also support the idea that LiDAR-derived topographic metrics could assist in determining where community types are positioned across a landscape. Additional NMDS analysis also showed either soil chemistry or topographic metrics explained nearly equal amounts of cumulative understory species variation. The results from this objective highlights the use of topographic metrics as predictors of understory vegetation, and likely community types, which could be validated in other WMNF watersheds. Finally, the primary challenge for ecological classification is reducing the cost of traditional unit mapping. Therefore, the objectives of chapter 4 was a conceptual synthesis of the reasoning behind doing ecological classification. Information from the WMNF management plans of 1985 and 2005, and current National and Regional land management direction of the US Forest Service were reviewed. A cost review of ecological classification by stratified random sampling using LiDAR-derived topographic metrics was compared to traditional TEUI mapping methods. In both approaches, the mapping of the plots averaged approximately $989.00 per plot including soil chemistry analysis from U.S. Forest Service Laboratory. This yielded a total cost of approximately$623,000 for the traditional TEUI compared to approximately $221,000 including the LiDAR acquisition required for stratified random sampling using topographic metrics. This chapter showed that stratified random sampling using LiDAR-derived topographic metrics reduced costs by approximately$402,000, including the additional LiDAR acquisition costs, compared to the traditional TEUI approach