498 research outputs found

    Value of remote sensing in forest surveys

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    Available from British Library Document Supply Centre- DSC:D42883/82 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

    Temporal Characteristics of Boreal Forest Radar Measurements

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    Radar observations of forests are sensitive to seasonal changes, meteorological variables and variations in soil and tree water content. These phenomena cause temporal variations in radar measurements, limiting the accuracy of tree height and biomass estimates using radar data. The temporal characteristics of radar measurements of forests, especially boreal forests, are not well understood. To fill this knowledge gap, a tower-based radar experiment was established for studying temporal variations in radar measurements of a boreal forest site in southern Sweden. The work in this thesis involves the design and implementation of the experiment and the analysis of data acquired. The instrument allowed radar signatures from the forest to be monitored over timescales ranging from less than a second to years. A purpose-built, 50 m high tower was equipped with 30 antennas for tomographic imaging at microwave frequencies of P-band (420-450 MHz), L-band (1240-1375 MHz) and C-band (5250-5570 MHz) for multiple polarisation combinations. Parallel measurements using a 20-port vector network analyser resulted in significantly shorter measurement times and better tomographic image quality than previous tower-based radars. A new method was developed for suppressing mutual antenna coupling without affecting the range resolution. Algorithms were developed for compensating for phase errors using an array radar and for correcting for pixel-variant impulse responses in tomographic images. Time series results showed large freeze/thaw backscatter variations due to freezing moisture in trees. P-band canopy backscatter variations of up to 10 dB occurred near instantaneously as the air temperature crossed 0⁰C, with ground backscatter responding over longer timescales. During nonfrozen conditions, the canopy backscatter was very stable with time. Evidence of backscatter variations due to tree water content were observed during hot summer periods only. A high vapour pressure deficit and strong winds increased the rate of transpiration fast enough to reduce the tree water content, which was visible as 0.5-2 dB backscatter drops during the day. Ground backscatter for cross-polarised observations increased during strong winds due to bending tree stems. Significant temporal decorrelation was only seen at P-band during freezing, thawing and strong winds. Suitable conditions for repeat-pass L-band interferometry were only seen during the summer. C-band temporal coherence was high over timescales of seconds and occasionally for several hours for night-time observations during the summer. Decorrelation coinciding with high transpiration rates was observed at L- and C-band, suggesting sensitivity to tree water dynamics.The observations from this experiment are important for understanding, modelling and mitigating temporal variations in radar observables in forest parameter estimation algorithms. The results also are also useful in the design of spaceborne synthetic aperture radar missions with interferometric and tomographic capabilities. The results motivate the implementation of single-pass interferometric synthetic aperture radars for forest applications at P-, L- and C-band

    Mapping forest cover and forest cover change with airborne S-band radar

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    Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its change

    Understanding ‘saturation’ of radar signals over forests

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    There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points

    Multistage, multiband and sequential imagery to identify and quantify non-forest vegetation resources

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    Earth Resources photographs from Apollo 6, 7, and 9 and photographs taken during Gemini 4, were used in the research along with high altitude and conventional aerial photography. A unified land use and resource analysis system was devised and used to develop a mapping legend. The natural vegetation, land use, macrorelief, and landforms of northern Maricopa County, Arizona, were analyzed and inventoried. This inventory was interpreted in relation to the critical problem of urban expansion and agricultural production in the study area. The central thrust of the research program has been to develop methods for use of space and small-scale, high-altitude aerial photography to develop information for land use planning and resource allocation decisions

    Evaluation of Skylab (EREP) data for forest and rangeland surveys

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    The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent

    Random forest effectiveness for Bragança region mapping: comparing indices, number of the decision trees, and generalization

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    Mestrado de dupla diplomação com o Institute Agronomic and Veterinary Hassan IIRemote sensing is a domain that tends to use satellite images for classification and Land Use/Cover (LULC) mapping. For this purpose, classification algorithms are used, which are numerous and diverse, and it is necessary to establish decision criteria when choosing the algorithm. Ultimately, the main decision criterion will be the accuracy obtained in classification because the accuracy of classification may differ from one algorithm to another, even within the same algorithm, according to its variables. But there are other equally important criteria: it depends on the nature of the task, the quantity and types of data available, the type of response expected, the time and computational resources available, the depth of our knowledge about the algorithms. The methodology of each part of the work was described and the criteria for comparison were established. In this research, with the same training data, the same validation data, the same application context (7 classes), and the same image data (Sentinel-2), we tested 15 iterations with the Random Forest classification algorithm, with different tree number decision values, and 3 iterations with vegetation and soil indexes, for the production of the LULC map of the Bragança region (northeast Portugal). Finally, we evaluate the accuracy of the classification, before and after the post-classification tasks (generalization, fragmentation and removal of isolated pixels). The results obtained show that a classification with an nb-trees = 1000, including vegetation and soil indices, and after post-classification tasks, provided excellent precision results (Coefficient Kappa = 0.93, Overall accuracy = 96%, and marginal errors of omission & commission below 4%).A teledetecção é um domínio que tende a utilizar imagens de satélite para classificação e mapeamento de Uso/Cobertura da Terra (LULC). Para este fim, são utilizados algoritmos de classificação, que são numerosos e diversos, sendo necessário estabelecer critérios de decisão ao escolher o algoritmo. Em última análise, o principal critério de decisão será a precisão obtida na classificação, porque a precisão da classificação pode diferir de um algoritmo para outro, mesmo dentro do mesmo algoritmo, de acordo com as suas variáveis. Mas existem outros critérios igualmente importantes: depende da natureza da tarefa, da quantidade e tipos de dados disponíveis, do tipo de resposta esperada, do tempo e dos recursos computacionais disponíveis, da profundidade dos nossos conhecimentos sobre os algoritmos. A metodologia de cada parte do trabalho foi descrita e os critérios de comparação foram estabelecidos. Nesta investigação, com os mesmos dados de formação, os mesmos dados de validação, o mesmo contexto de aplicação (7 classes), e os mesmos dados de imagem (Sentinel-2), testámos 15 iterações com o algoritmo de classificação Random Forest, com diferentes valores de decisão de número de árvores, e 3 iterações com índices de vegetação e solo, para a produção do mapa LULC da região de Bragança (nordeste de Portugal). Finalmente, avaliámos a exactidão da classificação, antes e depois das tarefas de pós-classificação (generalização, fragmentação e remoção de pixels isolados). Os resultados obtidos mostram que uma classificação com um nb-trees = 1000, incluindo índices de vegetação e solo, e após tarefas de pós-classificação, forneceu excelentes resultados de precisão (Coeficiente Kappa = 0.93, Precisão geral =96%, e erros marginais de omissão & comissão abaixo de 4%)

    Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

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    The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas
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