The use of WorldView-2 satellite data in urban tree species mapping by object-based image analysis technique

The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling their growth and decline. This study focused on the detection of urban tree species by considering three types of tree species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of maximum likelihood classification and support vector machines. These methods were then compared with object-based image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy

The role of remote sensing in invasive alien plant species detection and the assessment of removal programs in two selected reserves in the eThekwini Municipality, KwaZulu-Natal Province.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Durban 2016.One of the major current concerns by conservationists is alien invasive plants due to their rapid spread and threat to biodiversity. The detection of Invasive Alien Plant Species (IAPs) can aid in monitoring and managing their invasion on ecosystems. In South Africa approximately 10 million hectares of land have been invaded. To combat this invasion, the Working for Water program was initiated in 1995 aimed at manually removing them. Multispectral imagery can facilitate identification, assess removal initiatives and improve efficiency of IAP removal. The aim of this study is to determine the most appropriate sensor to detect three IAPs (Acacia podalyriifolia, Chromolaena odorata and Litsea glutinosa) and assess clearing programs of these species in two protected areas (Paradise Valley and Roosfontein Nature Reserves) within the eThekwini municipality, in KwaZulu-Natal province, South Africa using remote sensing. The three satellite sensors examined in this study included Landsat 7 ETM+, SPOT 5 and WorldView-2. The study also assessed four image classifiers (Parallelepiped, Maximum Likelihood, Spectral Angle Mapper and Iterative Self Organising Data Analysis Technique) in the detection of the selected IAPs. These sensors and techniques were compared based on their level of accuracy at detecting selected IAPs. The results of the study showed that WorldView-2 imagery and the Maximum Likelihood classifier had the highest overall accuracy (66.67%) , resulting in the successful classification of two (Acacia podalyriifolia and Chromolaena odorata) out of the three target species. This is due to the high spatial resolution of WorldView-2 imagery. This combination was then used to asses clearing of the selected IAPs by examining species distribution and density before and after clearing. Here the overall accuracies for the Paradise Valley and Roosfontein Nature Reserves were successful with accuracies above 85%. The density and distribution of all three IAPs decreased substantially in both sites except for the L. glutinosa species located in the Paradise Valley Nature Reserve which showed no significant decrease. These results show that geospatial data (especially remote sensing data) can be successfully used in both the detection of IAPs and the assessment of their removal

Influence of Lonicera maackii on leaf litter decomposition and macroinvertebrate communities in an urban stream.

Lonicera maackii (amur honeysuckle) is an aggressive alien shrub that invades many habitats in the Eastern United States, including along streambanks. This study investigated the direct and indirect effects of L. maackii invasion on leaf litter decomposition in an urban stream by placing leaf litter packs of L. maackii and the native Acer saccharum (sugar maple) in stream segments invaded by or managed for L. maackii. We found L. maackii litter decomposed two times faster than native A. saccharum, and A. saccharum leaf litter supported a higher abundance of macroinvertebrates than L. maackii. Functional feeding groups of macroinvertebrates were also affected by the invasive species; significantly more scraper-gatherers were associated with A. saccharum litter, and predators were positively associated with both A. saccharum and invaded sites. Additional indirect effects of L. maackii presence along streambanks on leaf decomposition and macroinvertebrate communities were negligible, possibly due to overriding effects of urbanization

Aliens, Aircraft, and Accuracies: Surveying for Understory Invasive Plants Using Unmanned Aerial Systems

Invasive (alien) plants are introduced species that can cause harm to native ecosystems, industries, or human health. Managing invasive species requires knowing where they are, and early detection of new populations increases the likelihood of local eradication. Unmanned aerial systems (UAS) are an emerging remote sensing technology that can capture very high spatial resolution imagery, are easily deployed, and may offer a more efficient alternative to extensive ground surveys to locate invasive plants. Imagery collected with UAS has been used to map invasive plants in open canopy habitats, but has yet to be tested for mapping invasive plants in forest understories. My aim was to explore the feasibility of UAS as an understory invasion monitoring tool, including tests of season, sensor type, and image classification method for reliable invasive detection. I collected imagery from a 21-hectare mixed and deciduous New Hampshire forest during spring and fall periods of phenology mismatch between native vegetation and two focal invasive plants, Berberis thunbergii (Japanese barberry) and Rosa multiflora (multiflora rose). I achieved up to 82% classification accuracy by grouping B. thunbergii and R. multiflora as an Invasive class. There were no significant differences in invasive detectability between sensors or classification methods, but spring imagery yielded the highest accuracies overall. Simpler pixel-based classifications are sufficient for achieving over 70% classification accuracy, though object-based segmentation can improve accuracy. UAS are promising technology with potential to reduce and target invasive plant ground surveys for temperate forest management

Analyzing the phenologic dynamics of kudzu (Pueraria montana) infestations using remote sensing and the normalized difference vegetation index.

Non-native invasive species are one of the major threats to worldwide ecosystems. Kudzu (Pueraria montana) is a fast-growing vine native to Asia that has invaded regions in the United States making management of this species an important issue. Estimated normalized difference vegetation index (NDVI) values for the years 2000 to 2015 were calculated using data collected by Landsat and MODIS platforms for three infestation sites in Kentucky. The STARFM image-fusing algorithm was used to combine Landsat- and MODIS-derived NDVI into time series with a 30 m spatial resolution and 16 day temporal resolution. The fused time series was decomposed using the Breaks for Additive Season and Trend (BFAST) algorithm. Results showed that fused NDVI could be estimated for the three sites but could not detect changes over time. Combining this method with field data collection and other types of analyses may be useful for kudzu monitoring and management

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Cartographie récente et écologie du nerprun bourdaine en Estrie

Le nerprun bourdaine (Rhamnus frangula L.) est une espèce exotique qui envahit plusieurs régions du sud du Québec, et plus particulièrement la région administrative de l'Estrie. Actuellement, on connaît encore peu l'écologie de l'espèce dans le contexte québécois et il n’existe pas de portrait d’ensemble de sa distribution dans les forêts tempérées de cette région. Dans ce contexte, le premier objectif du projet était de cartographier par télédétection la distribution du nerprun bourdaine dans deux secteurs de l'Estrie. Un second objectif était d'évaluer les variables environnementales déterminantes pour expliquer le recouvrement de nerprun bourdaine. La phénologie du nerprun bourdaine diffère de celle de la plupart des espèces indigènes arborescentes puisque ses feuilles tombent plus tard en automne. Cette caractéristique a permis de cartographier, par démixage spectral, la probabilité d'occurrence du nerprun bourdaine grâce à une série temporelle d'images du capteur OLI de Landsat 8. Le recouvrement du nerprun bourdaine a été calculé dans 119 placettes sur le terrain. La cartographie résultante a montré un accord de 69% avec les données terrain. Une image SPOT-7, dont la résolution spatiale est plus fine, a ensuite été utilisée, mais n’a pas permis d'améliorer la cartographie, puisque la date d’acquisition de l’image n’était pas optimale dû à un manque de disponibilité. Concernant le second objectif de la recherche, la variable la plus significative pour expliquer la présence de nerprun bourdaine était la densité du peuplement, ce qui suggère que l’ouverture de la couverture forestière pourrait favoriser l’envahissement. Néanmoins, les résultats tendent à démontrer que le nerprun bourdaine est une espèce «généraliste» qui s’adapte bien à plusieurs conditions environnementales.Glossy buckthorn (Rhamnus frangula L.) is an exotic species invading many areas in southern of Quebec, particularly in the Eastern Townships. Currently, we do not know very much about the species ecology and no thorough study of its distribution in temperate forest has been performed. Therefore, the first objective of the project was to map the spatial distribution of glossy buckthorn in two areas of the Eastern Townships, using remote sensing techniques. The second objective was to evaluate the environmental variables, or predictors, best explaining the presence of glossy buckthorn. The phenology of glossy buckthorn differs from most of the indigenous tree species found in this area because its leaves fall later in autumn. This characteristic allowed to map, using spectral unmixing, the probability of occurrence of glossy buckthorn, with temporal Landsat 8 (OLI) imagery data series. Glossy buckthorn coverage was calculated on 119 plots on the field. The resulting maps showed an agreement of 69% with field data. A SPOT-7 image, which has a finer resolution than Landsat 8 (OLI), was then used but it did not improve the quality of the map, since its acquisition date was not optimal, due to a lack of availability. Concerning the second objective of the research, the best variable explaining the presence of glossy buckthorn was stand density, which leads to believe that forest cover openings could ease the establishment of buckthorn. However, the results tend to show that glossy buckthorn is a generalist species, easily adapting to various environmental conditions

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale