FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Contributions of LANDSAT to natural resource protection and future recreational development in the state of West Virginia

There are no author-identified significant results in this report

Object-based image analysis for forest-type mapping in New Hampshire

The use of satellite imagery to classify New England forests is inherently complicated due to high species diversity and complex spatial distributions across a landscape. The use of imagery with high spatial resolutions to classify forests has become more commonplace as new satellite technology become available. Pixel-based methods of classification have been traditionally used to identify forest cover types. However, object-based image analysis (OBIA) has been shown to provide more accurate results. This study explored the ability of OBIA to classify forest stands in New Hampshire using two methods: by identifying stands within an IKONOS satellite image, and by identifying individual trees and building them into forest stands. Forest stands were classified in the IKONOS image using OBIA. However, the spatial resolution was not high enough to distinguish individual tree crowns and therefore, individual trees could not be accurately identified to create forest stands. In addition, the accuracy of labeling forest stands using the OBIA approach was low. In the future, these results could be improved by using a modified classification approach and appropriate sampling scheme more reflective of object-based analysis

Images from unmanned aircraft systems for surveying aquatic and riparian vegetation

Aquatic and riparian vegetation in lakes, streams, and wetlands has important ecological and regulatory functions and should be monitored to detect ecosystem changes. Field surveys are often tedious and in countries with numerous lakes and streams a nationwide assessment is difficult to achieve. Remote sensing with unmanned aircraft systems (UASs) provides aerial images with high spatial resolution and offers a potential data source for detailed vegetation surveys. The overall objective of this thesis was to evaluate the potential of sub-decimetre resolution true-colour digital images acquired with a UAS for surveying non-submerged (i.e., floating-leaved and emergent) aquatic and riparian vegetation at a high level of thematic detail. At two streams and three lakes in northern Sweden we applied several image analysis methods: Visual interpretation, manual mapping, manual mapping in combination with GPS-based field surveys, and automated object-based image analysis and classification of both 2D images and 3D point data. The UAS-images allowed for high taxonomic resolution, mostly at the species level, with high taxa identification accuracy (>80%) also in mixed-taxa stands. UAS-images in combination with ground-based vegetation surveys allowed for the extrapolation of field sampling results, like biomass measurement, to areas larger than the sampled sites. In automatically produced vegetation maps some fine-scale information detectable with visual interpretation was lost, but time-efficiency increased which is important when larger areas need to be covered. Based on spectral and textural features and height data the automated classification accuracy of non-submerged aquatic vegetation was ~80% for all test sites at the growth-form level and for four out of five test sites at the dominant-taxon level. The results indicate good potential of UAS-images for operative mapping and monitoring of aquatic, riparian, and wetland vegetation. More case studies are needed to fully assess the added value of UAS-technology in terms of invested labour and costs compared to other survey methods. Especially the rapid technical development of multi- and hyperspectral lightweight sensors needs to be taken into account

A Vegetation Analysis on Horn Island, Mississippi, ca. 1940 Using Characteristic Dimensions Derived from Historical Aerial Photography

Horn Island is part of the MS/AL barrier island chain in the northern Gulf of Mexico located approximately 18kn off the coast of Mississippi. This island’s habitats have undergone many transitions over the last several decades. The goal of this study was to quantify habitat change over a seventy year period using historical black and white photography from 1940. Using present NAIP imagery from the USDA, habitat structure was estimated by using geo-statistics, and second order statistics, from a co-occurrence matrix, to characterize texture for habitat classification. Percent land cover was then calculated to determine overall land cover change over a seventy year period. The geostatistic of the horizontal spectral variation (CV) of image textures was used to estimate habitat structure using a multi scale approach if any characteristics of habitat texture could be delineated from CV histograms. The classification met with a result of an 80% habitat map of Horn Island ca. 1940, at 21x21 window size proving, that CV can be used successfully to classify text of historical black and white imagery. It was, also proven that CV can be used to characterize relative patch size for slash pine woodland habitat types, but not for habitats with smaller horizontal variations (i.e., marsh, and dune herbland)

Ecological niche modeling as a conservation tool to predict actual and potential habitat for the bog turtle, Glyptemys muhlenbergii.

The bog turtle (Glyptemys muhlenbergii) is faced with two principle threats: wetland habitat loss and, to a lesser degree, the illegal collection for pet trade demands. Current methodologies for bog turtle population discovery in the Southeast rely primarily on field surveys, which are labor intensive and fiscally exhaustive. The purpose of this research was to evaluate the role of geographic information science technologies, remote sensing and ecological niche modeling to predict potential bog turtle habitats in the Southeast. Environmental data were organized in a geographic information system. The Genetic Algorithm for Ruleset Production was used to develop an ecological niche model to identify additional habitat sites with the same signatures and potential capacity for support. The results showed the area under the curve as 97%; the model correctly predicted 98.889% of the data points; and the model predicted 1.67% of the total research area as potential habitat. Areas of highest prediction will be investigated for bog turtle occupancy by trained professionals. This information will be beneficial to researchers in setting conservation priorities for the bog turtle

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models

Developing a habitat suitability model for the spotted turtle using a hybrid-deductive approach

Knowledge of species with multiple habitat needs for conservation and species survival planning is scarce. In order to predict areas of habitat suitability and potential further research, suitability modeling is necessary. This research created a GIS-based model to predict habitat suitability potential for the spotted turtle (Clemmys guttata) in four counties of western New York. Because of scarce and conflicting information on spotted turtle habitat needs, a survey was sent out to experts that evaluated spotted turtle habitat parameters. The goal of the habitat model was to predict optimal habitat for sustainable spotted turtle populations in an area where viable populations had not been confirmed. The surveys were designed to assign relative values to various habitat parameters and derive qualitative and quantitative information for future field assessment measures. The survey and data collection were based on the Analytical Hierarchy Process (AHP). The method ultimately used for habitat selection was a Hybrid-Deductive approach because of the inductive and deductive reasoning used with scarce and conflicting information on spotted turtle habitat preference. The GIS model selected sites based on an iterative process, resulting in four sites being selected as the “best” habitat sites, combining survey, literature, and GIS data. Model results were compared to historic state sighting records from the NY DEC and fit reasonably well in two of the four counties, but the model did not account for populations that seem to prefer atypical habitats, such as ditches. The model also discarded potentially viable sites (based on land cover) minimally impacted by roads. Results also suggest the need for more detailed soil, road, and land cover data to help determine connectivity between potential habitat sites

High-resolution classification of south patagonian peat bog microforms reveals potential gaps in up-scaled CH4 fluxes by use of Unmanned Aerial System (UAS) and CIR imagery

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m-2· day-1 to 26.8 mg·m-2· day-1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled.Fil: Lehmann, Jan R. K.. Westfalische Wilhelms Universitat; AlemaniaFil: Münchberger, Wiebke. Westfalische Wilhelms Universitat; AlemaniaFil: Knoth, Christian. Westfalische Wilhelms Universitat; AlemaniaFil: Blodau, Christian. Westfalische Wilhelms Universitat; AlemaniaFil: Nieberding, Felix. Westfalische Wilhelms Universitat; AlemaniaFil: Prinz, Torsten. Westfalische Wilhelms Universitat; AlemaniaFil: Pancotto, Veronica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Kleinebecker, Till. Westfalische Wilhelms Universitat; Alemani

Landscape scale mapping of tundra vegetation structure at ultra-high resolution using UAVs and computer vision

Ilmastomuutoksella on voimakkain vaikutus suurten leveysasteiden ekosysteemeissä, jotka ovat sopeutuneet viileään ilmastoon. Jotta suurella mittakaavalla havaittuja muutoksia tundrakasvillisuudessa ja niiden takaisinkytkentävaikutuksia ilmastoon voidaan ymmärtää ja ennustaa luotettavammin, on syytä tarkastella mitä tapahtuu pienellä mittakaavalla; jopa yksittäisissä kasveissa. Lähivuosikymmenten aikana tapahtunut teknologinen kehitys on mahdollistanut kustannustehokkaiden, kevyiden ja pienikokoisten miehittämättömien ilma-alusten (UAV) yleistymisen. Erittäin korkearesoluutioisten aineistojen (pikselikoko <10cm) lisääntyessä ja tullessa yhä helpommin saataville, ympäristön tarkastelussa käytetyt kaukokartoitusmenetelmät altistuvat paradigmanmuutokselle, kun konenäköön ja -oppimiseen perustuvat algoritmit ja analyysit yleistyvät. Menetelmien käyttöönotto on houkuttelevaa, koska ne mahdollistavat joustavan ja pitkälle automatisoidun aineistonkeruun ja erittäin tarkkojen kaukokartoitustuotteiden tuottamisen vaikeasti tavoitettavilta alueilta, kuten tundralla. Luotettavien tulosten saaminen vaatii kuitenkin huolellista suunnittelua sekä prosessointialgoritmien ja -parametrien pitkäjänteistä testaamista. Tässä tutkimuksessa tarkasteltiin, kuinka tarkasti tavallisella digitaalikameralla kerätyistä ilmakuvista johdetuilla muuttujilla voidaan kartoittaa kasvillisuuden rakennetta maisemamittakaavalla. Kilpisjärvellä Pohjois-Fennoskandiassa kerättiin dronella kolmensadan hehtaarin kokoiselta alueelta yhteensä noin 10 000 ilmakuvasta koostuva aineisto. Lisäksi alueella määritettiin 1183 pisteestä dominantti putkilokasvillisuus, sekä kasvillisuuden korkeus. Ilmakuvat prosessoitiin tiheiksi kolmiulotteisiksi pistepilviksi konenäköön ja fotogrammetriaan perustuvalla SfM (Structure from Motion) menetelmällä. Pistepilvien pohjalta interpoloitiin maastomalli sekä kasvillisuuden korkeusmalli. Lisäksi tuotettiin koko alueen kattava ilmakuvamosaiikki. Näiden aineistojen pohjalta laskettiin muuttujia, joita käytettiin yhdessä maastoreferenssiaineiston kanssa kasvillisuuden objektipohjaisessa analyysissä (GEOBIA, Geographical Object-Based Image Analysis). Suodatetut maanpintapisteet vastasivat luotettavasti todellista maanpinnan korkeutta koko alueella ja tuotetut korkeusmallit korreloivat voimakkaasti maastoreferenssiaineiston kanssa. Maastomallin virhe oli suurin alueilla, joilla oli korkeaa kasvillisuutta. Valaistusolosuhteissa ja kasvillisuudessa tapahtuneet muutokset ilmakuvien keruun aikana aiheuttivat haasteita objektipohjaisen analyysin molemmissa vaiheissa: segmentoinnissa ja luokittelussa. mutta kokonaistarkkuus parani 0,27:stä 0,,54:n kun luokitteluun lisättiin topografiaa, kasvillisuuden korkeutta ja tekstuuria kuvaavia muuttujia ja kohdeluokkien lukumäärää vähennettiin. Konenäköön ja –oppimiseen perustuvat menetelmät pystyvät tuottamaan tärkeää tietoa tundran kasvillisuuden rakenteesta, erityisesti kasvillisuuden korkeudesta, maisemassa. Lisää tutkimusta kuitenkin tarvitaan parhaiden algoritmien ja parametrien määrittämiseksi tundraympäristössä, jossa ympäristöolosuhteet muuttuvat nopeasti ja kasvillisuus on heterogeenistä ja sekoittunutta, mikä aiheuttaa eroja ilmakuvien välillä ja lisää vaikeuksia analyyseissä.Climate change has the strongest impact on high-latitude ecosystems that are adapted to cool climates. In order to better understand and predict the changes in tundra vegetation observed on large scales as well as their feedbacks onto climate, it is necessary to look at what is happening at finer scales; even in individual plants. Technological developments over the past few decades have enabled the spread of cost-effective, light and small unmanned aerial vehicles (UAVs). As very high-resolution data (pixel size <10cm) becomes more and more available, the remote sensing methods used in environmental analysis become subject to a paradigm shift as algorithms and analyzes based on machine vision and learning turn out to be more common. Harnessing new methods is attractive because they allow flexible and highly automated data collection and the production of highly accurate remote sensing products from hard-to-reach areas such as the tundra. However, obtaining reliable results requires careful planning and testing of processing algorithms and parameters. This study looked at how accurately variables derived from aerial images collected with an off-the-shelf digital camera can map the vegetation structure on a landscape scale. In Kilpisjärvi, northern Fennoscandia, a total of ~ 10,000 aerial photographs were collected by drone covering an area of three hundred hectares. In addition, dominant vascular plants were identified from 1183 points in the area, as well as vegetation height. Aerial images were processed into dense three-dimensional point clouds by using SfM (Structure from Motion) method, which is based on computer vision and digital photogrammetry. From the point clouds terrain models and vegetation height models were interpolated. In addition, image mosaic covering the entire area was produced. Based on these data, predictive variables were calculated, which were used together with the terrain reference data in Geographical Object-Based Image Analysis (GEOBIA). The filtered ground points corresponded to observations throughout the region, and the produced elevation models strongly correlated with the ground reference data. The terrain model error was greatest in areas with tall vegetation. Changes in lighting conditions and vegetation during aerial image surveys posed challenges in both phases of object-based analysis: segmentation and classification. but overall accuracy improved from 0.27 to 0.54 when topography, vegetation height and texture variables were added to the classifier and the number of target classes was reduced. Methods based on machine vision and learning can produce important information about vegetation structure, vegetation height, in a landscape. However, more research is needed to determine the best algorithms and parameters in a tundra environment where environmental conditions change rapidly and vegetation is heterogeneous and mixed, causing differences between aerial images and difficulties in analyses