31 research outputs found

    Mapping Chestnut Stands Using Bi-Temporal VHR Data

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    This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

    Crown-level mapping of tree species and health from remote sensing of rural and urban forests

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    Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

    Aplicación de técnicas de teledetección en ecosistemas macaronésicos.

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    The climatic and orographic conditions of Tenerife Island (Canaray Islands, Spain) have allowed the development of a rich biodiversity and different ecosystems. However, such a fragmented territory, the spectral similarity and complex spatial distribution of the species of vegetation, complicates the task of maintaining updated cartography using the traditional, costly and time-consuming fieldwork. Because of these limitations, this thesis proposed the use of remote sensing techniques from satellites and unmanned aerial vehicles in conjunction with selective field spectroradiometry, to map two important ecosystems situated in Tenerife: an agro-ecosystem dominated by chestnut trees (Castanea sativa Mill.) in the north slope of the island and a natural ecosystem (Malpaís de Güímar) in the southeast, with vegetation of beekeeping interest where the Cardonal-Tabaibal prevails. We performed thee different studies. In the first, the potential of very high- resolution (VHR) WorldView imagery and extended morphological profiles for mapping chestnut trees in the agroecosystem was analysed. Secondly, we spectrally characterized the vegetation of beekeeping interest in the Malpaís de Güímar and determined the wavelengths that best discriminated the vegetation species using a spectral separability analysis. Finally, we analysed the potential of 10 cm spatial resolution hyperspectral images to obtain cartography of the selected plant species in the Malpaís de Güímar. All remote sensing datasets (images) were classified with the Random Forest (RF) machine-learning algorithm to obtain the thematic maps of the ecosystems. RF is an adequate algorithm for mapping classes with complex characteristics, such as many of the plant species considered in this study. It demonstrated its ability to classify, with overall accuracies greater than 85%, both high-dimensional dataset (WorldView and hyperspectral images). In relation to the agro-ecosystem, two VHR WorldView images (March and May) to cover different phenological phases of chestnut trees were used. Moreover, was included spatial information in the classification process by extended morphological profiles (EMPs). The detailed accuracy assessment clearly reveals the benefits of multitemporal images in terms of mapping accuracy. The overall accuracies of the mono-temporal classifications are increased between 2% and 15%, when compared to the results achieved on the multitemporal data set. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements in the final map. During years 2017 and 2018, an intense fieldwork was carried out to measure, with the ASD FieldSpec 3 spectroradiometer, the characteristic spectral signatures of the plant species with the greatest pollination potential in the Malpaís de Güímar. A spectral separability analysis, with the Jeffries-Matusita distance, made it possible to find the fourteen best spectral bands to discriminate the species. This analysis was customized for a Resonon Pika L camera that captures hyperspectral data from 400 nm to 1000 nm from a UAV. The results show that only eleven plant species show spectral separability with values higher than 1.9. The red-edge interval (705.5 nm - 757.5 nm) is particularly noteworthy. For the mapping of the Malpais de Güímar, a hyperspectral image covering approximately 6 ha was selected. Eight thematic classes were identified and defined: Aulaga, Balo, Cardón, Salado, Tabaiba amarga, Suelo, Sombra and Colmenas. From the original image of 120 spectral bands (ORIGINAL), two other reduced dimension datasets were constructed: PCA, which included the first five principal components; and SPECTRAL with the fourteen spectral bands of the previous spectral separability analysis. The ORIGINAL and PCA images showed the best results, with overall accuracies of 91.5% and 91.3%, respectively. However, no significant differences were found between them. The best-classified plant species was Cardón with a commission error of 1.1% and an omission error of 4%. In general, the thesis shows a methodology to generate accurate maps and monitor changes in the two relevant macaronesic ecosystems under study. The maps obtained in this work, increase and improve the information available so far, and could help reduce the vulnerability of these ecosystems to global climate change, strengthen their adaptation processes to prevent the loss of biological diversity and promote the development of sustainable activities to achieve their conservation

    Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

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    With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

    Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

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    During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

    Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

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    Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

    Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

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    This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

    Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

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    Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

    The global tree carrying capacity (keynote)

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