This thesis examines the use of multi-spectral remotely sensed data in the management of the North York Moors, an upland area of heather moorland in northern England. A series of ground radiometer surveys and airborne simulations are analysed to determine the relative importance of spatial, spectral and temporal resolution as characteristics of earth resources satellites in this environment. Particular reference is made to the potential for selecting and combining data from the Landsat MSS, TM and the SPOT HRV sensors. The results show that spectral resolution can be critical in isolating and recognising elements of the moorland community by their spectral response, especially at the most detailed levels of vegetational description. Temporal resolution has little effect on the discrimination of targets within the heather dominated areas but affects the separability of the major communities of heather, bracken and sedges. Change in spatial resolution has no clear effect on the spectral uniformity and spectral separation of the elements of the heather dominated areas. The interaction between spectral and spatial resolution is more important in isolating the major communities, where the requirement for spatial precision is balanced against the need to suppress spectral variation within the moorland. The hypothesis that multi-spectral remotely sensed data can provide critical information on the distribution and status of moorland vegetation is not refuted in this thesis. Remotely sensed data would make the greatest contribution to management if linked to other spatial data as part of a Geographical Information System. In the absence of such a formal structure satellite imagery can still provide a regular and unique inventory of the moorland habitat which will increase the efficiency of management.

Thesis (M.S.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: Theodore W. Weaver. Includes bibliographical references

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<p dir="ltr">This study introduces a methodology wherein daily values are linearly interpolated to achieve uniform temporal resolution across various data sets, including spectral and environmental information. This approach facilitates further analysis using machine learning techniques to estimate biomass. The proposed Best Friend Frame (B.F.F.) data set integrates Unmanned Aerial Systems (UAS) data, weather data, weather indices, soil hydrological group classifications, and topographic information. Two different biomass estimations were created to enhance versatility: one averaged per management practice and another averaged per physical experimental plot size. Additionally, SuperDove satellite data were combined with identical environmental data as that of the UAS.</p><p dir="ltr">UAS flights were conducted at the ACRE field in 2022 and 2023. The UAS data were captured at a height of 30 meters, yielding a ground sample distance of 2 cm/pixel per flight. Satellite data were sourced from the Planet SuperDove product. The images were processed using Crop Image Extraction (CIE) and calibrated with Vegetation Index Derivation (VID). Spatial resolution was defined as the experimental plot size per year per crop type (soybean or corn). Topographic data were derived from Indiana LiDAR data, and soil information was obtained from the USDA SSURGO dataset.</p><p dir="ltr">The B.F.F. framework can be utilized with various models to identify which environmental inputs influence biomass accumulation throughout the growing season.</p>

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Made available in DSpace on 2014-07-29T15:31:59Z (GMT). No. of bitstreams: 1 iZA cARLA DE oLIVEIRA.pdf: 4854779 bytes, checksum: f358f6403c6890098ade27a8d2864c95 (MD5) Previous issue date: 2010-03-11 / The Cerrado biome has the national and international eminence, because it dimensions, around 205 millions ha. in the Brazil, beyond to emphasize like an areas of the global importance, call hotspots. With 9,2% of it is area in the parks and ecological reservation, studs of the mapping of the vegetation indicate a scenery of the conversion estimated between 40 % and 55 %, for areas of the pasture and agricultural. This work has the objective to evaluated the potential of discrimination of the natural and human target from the images with high resolution and high spectral resolution, accentuated amount the spectral dominion like temporal, to identify the better technique and protocols, which results in mapping semi-automatic of the landscape cover in the Cerrado biome, more accurate. It was selected an area test for this analyze, the municipality of Mineiros, the definition of the are was base in the environment importance, because inside of this municipality there is the fountainhead of the Araguaia - Tocantins River basin, the Prata basin and the Paraguai basin, beyond the strategic areas for conservation (Parque Nacional das Emas) and the biodiversity corridors (Cerrado Pantanal). The models of spectral enhancement NDVI, MLME and CP were apply in a LANDASAT TM (orbit/point: 224/73) from march 30 of 2008 (rain season) and august 21 of 2008 (dry season). The statistic analyses show the existence of one spectral confusion more intensify between Campo Sujo and Pasture and Cerrado Sensu Stricto, in all categories of the analyze data. Other results showing that the capacity of discriminate is bigger in the august, the dry season, when there is more distinction in the vegetation. And finally, between the spectral model, which presents better potential to distinction the class was observed by the images, component main, with 96 % of the sample classify correctly. Before the MLME presents results of 91 % in relation to NDVI, with 84 % of the capacity to define the class, this difference is because the number of the patchy in NDVI is less ( i.e NDVI use just two bands, red and infra-red) when compare to MLME and CP. Thus the work consider that application of the spectral enhancement in the image of the original digital values contribution in the distinction potential of the natural and anthropic, concluding that the analyze of the main components increase with the precision of the semi-automatic mapping of use land cover. / O bioma Cerrado tem destaque nacional e internacional em virtude de sua dimensão, cerca de 205 milhões de hectares do território, além de se destacar como uma das áreas de importância global para a biodiversidade. Com 9,2% de sua área assegurada na forma de parque ou reserva ecológica, estudos do mapeamento da cobertura vegetal apontam um cenário de conversão estimado entre 40% a 55% de seu território, em áreas de pastagens e culturas anuais. Desta forma, este trabalho teve como objetivo avaliar o potencial de discriminação de alvos naturais e antrópicos a partir de imagens de alta resolução espacial e espectral, realçadas tanto no domínio espectral quanto temporal, com vistas a identificar técnicas e protocolos mais adequados, os quais resultem em mapeamentos semi-automáticos da cobertura e uso da terra no bioma Cerrado mais acurados. Foi selecionado uma área de estudo, no caso o município de Mineiros, a definição da área foi baseada na importância ambiental ao sediar em seu território parte das nascentes da bacia hidrográfica do Rio Araguaia - Tocantins, bacia do Prata e bacia do Paraguai, além de às áreas estratégicas de conservação (Parque Nacional das Emas) e Corredores de Biodiversidade (Cerrado pantanal). Os modelos de realce espectral NDVI, MLME e CP foram aplicados sobre uma cena do sensor Landsat TM (órbita/ponto:224/72 e 73) de 30 de março de 2008 (estação de chuvas) e 21 de agosto de 2008 (estação seca). As análises estatísticas demonstraram a existência de uma confusão espectral mais intensa entre o Campo Sujo e Pastagem e Cerrado Sensu Stricto, em todas as categorias de dados analisadas. Outros resultados demonstraram que a capacidade discriminante é significativamente maior em agosto, no período de seca, quando há uma maior diferenciação na cobertura verde. E por fim, entre os modelos de realce espectral, o que apresentou melhor potencial de discriminar classes foi observado pelas imagens componentes principais, com 96% de amostras classificadas corretamente. Em seguida o MLME apresentou resultados de 91%, em relação ao NDVI, com 84% de capacidade de definir classes, essa diferença se deve ao fato do numero de variáveis no NDVI serem menor (i.e. NDVI usa apenas duas bandas, vermelho e infra-vermelho) quando comprado ao MLME e CP. Assim o trabalho considera que a aplicação de realce espectral em imagem de valores digitais originais, auxilia no potencial de discriminação de alvos naturais e antrópicos, concluindo que a análise das componentes principais aumenta a precisão no mapeamento semi-automático de uso e cobertura da terra.