Made available in DSpace on 2014-07-29T15:32:05Z (GMT). No. of bitstreams: 1 DISSERTACAO_FANUEL_GEOG.pdf: 5225631 bytes, checksum: 17be1fcb54dcf706976140dca2f5dadb (MD5) Previous issue date: 2012-03-27 / Cattle ranching is extremely important for the economy of Brazil. This activity is characterized by the intensive explotation of pastures, which occupy a vast area of the Brazilian territory, i.e. approximately 150 million hectares. Currently, Brazil is the largest meat exporter in the world. Concerning its biomes, this occupation occurs mainly in the Cerrado, where the 546.251 km² of area under utilization correspond to about 37% of the total pasture area in the country. Among the states encompassed within the Cerrado limits, Goiás has the largest pasture occupation, over 38.7% of its area. In addition, it has one of the largest livestock, with about 21.3 millions of heads, producing, on average, 600.000 tons of meat a year. Within this context, arises the concern with the quality of the Goias state pastures, since several studies show that a large portion of these pastures are already degraded (i.e. low capacity), with low cattle occupation. This study, based on remote sensing data, as well as on spatial and census data, aimed at evaluating the quality of the pastures in Goias through the estimation of the net primary productivity (NPP). The distribution of pastures in relation to soil types, cattle occupation at the municipality level, infra-structure and location of the meat processing plants were considered as well. The productivity estimations were based on the MOD13Q1 vegetation index images (EVI), for the 2001 2009 period. The analysis of pasture distribution were conducted through the intersection of the derived NPP and the ancillary data mentioned above. The main conclusions of our study are: a) the highest NPP values are found in the central, southeast and extreme northeastern portions of Goias; b) the highest NPP values are related to the following soils: argissolos, cambissolos, neossolos and latossolos (Oxisols), respectively; c) the average cattle occupation in the Cerrado in Goias is usually low, around 1,07 heads per hectare; d) there are several municipalities (major cattle producers) which have the totality of their pastures severely degraded; e) there is no strict correlation between the location of meat processing plants and quality of pastures, as well as cattle occupation. Thus, the monitoring of pasture quality and the analysis of correlated factors are of great importance, as cattle ranching are responsible for the largest occupation of the Cerrado in Goias e for most of the wealth in the state. / A pecuária é uma atividade extremamente importante para a economia do Brasil. Essa atividade se caracteriza pela exploração extensiva das pastagens, ocupando vasta área do território, aproximadamente 150 milhões de hectares. Atualmente, o Brasil é o maior exportador de carne bovina no mundo. Em relação aos biomas brasileiros, essa ocupação ocorre principalmente no Cerrado, ocupando uma área de 546.251 km², o que representa cerca de 37% da área total de pastagens no país. Dentre os estados que compõem os limites do Cerrado, Goiás é o que possui maior ocupação por pastagens, com aproximadamente 38.7% de sua área. Além disso, tem um dos maiores rebanhos bovinos, com cerca de 21.3 milhões de cabeças de gado, produzindo em média, 600.000 toneladas de carne por ano. Diante desse contexto, surge a preocupação sobre a qualidade das pastagens cultivadas no estado de Goiás, uma vez que diversos estudos mostram que grandes partes dessas pastagens estão com algum nível de degradação (i.e. baixa capacidade de suporte) e baixa lotação bovina média. Esse estudo, baseado em dados de sensoriamento remoto orbital, bem como bases de dados espaciais e censitários, teve como objetivo avaliar a qualidade das pastagens em Goiás, a partir da estimativa de produtividade primária líquida da vegetação (NPP). A distribuição das pastagens em relação ao tipo de solo, lotação bovina por município, infra-estrutura e localização das plantas de processamento de carne foram considerados também. As estimativas de produtividade foram baseadas nas imagens MODIS13Q1 de índice de vegetação (EVI), para o período de 2001 - 2009. As análises da distribuição de pastagens foram conduzidas através da intersecção do NPP e os dados auxiliares mencionados acima. Os principais resultados desse trabalho são: a) os maiores valores de NPP são encontrados nas porções centrais, sudeste e extremo nordeste do estado de Goiás; b) os maiores índices de NPP estão associados aos seguintes solos: argissolos, cambissolos, neossolos e latossolos, respectivamente; c) a média de lotação bovina no Cerrado goiano em geral é baixa, gira em torno de 1,07 cabeças por hectares; d) há vários municípios (maiores produtores de gado) que estão com suas áreas de pastagens seriamente comprometidas; e) não há grandes correlações entre o local dos frigoríficos e a qualidade das pastagens, bem como a ocupação de gado. Assim, o monitoramento da qualidade das pastagens e a análise de fatores correlatos são de grande importância, pois a pecuária é responsável pela ocupação de maior parte do Cerrado goiano e geração de grandes riquezas para o estado.

自工業革命後，隨著科技的進步，人口、經濟、醫療技術皆快速發展，也因人類需求的增加而大量燃燒化石燃料，大規模的砍伐熱帶雨林，導致大氣中二氧化碳大量增加，進而衍生溫室效應的發生，甚至造成全球氣候變遷。 在全球暖化的狀態下，聯合國氣候變化綱要公約與京都議定書中都明確肯定森林可固定主要溫室氣體二氧化碳，由於森林具備吸收和儲存二氧化碳的能力，其對於生態系統中的碳循環功能扮演重要的角色。若能有效監控森林資源，便能管理溫室氣體，且能提出有效的控管方式。 而本研究將應用遙測技術於碳吸存與環境變化的監測，透過美國大地衛星影像（Landsat）進行不同時期與區域之碳吸存的評估，與以往研究之最大差異為可進行大尺度與多時期的碳吸存評估，並且達到經濟、準確、有效提升效率之目標。 本研究根據光能利用率（Light use efficiency）為基礎模型，計算2005-2010之植生淨初級生產量（Net Primary Productivity, NPP），且配合不同的研究區域：台北、高雄，進一步探討不同的氣候條件與土地利用的條件下，其差異性對於NPP之影響。 成果顯示，在不同環境條件下碳吸存能力受到氣候條件影響最大，且在資料具有缺漏狀態下，依然能反映不同區域之趨勢，雖無法有效評估年總量，但仍可供評估區域性碳吸存能力之趨勢。 / Since the industrial revolution, with the rapid progress of science and technology, population, economy, and medical technology also grow rapidly. Because of increased human demand, coupled with burning lots of fossil fuels, and large-scale felling of tropical rain forests, which result in a significant increase in atmospheric carbon dioxide, and then trigger the greenhouse effect to occur, hence causing global climate change. Under the global warming condition, the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol (KC) both clearly affirmed that forests can fix the main greenhouse gas—carbon dioxide. Because forests have the ability to absorb and store carbon dioxide, they plan an important role in carbon cycle function for ecosystem. If we can effectively monitor forest resources, we will be able to manage greenhouse gases, and can come up with effective control methods. In the present study, we will use remote sensing technology to monitor carbon sequestration and environmental changes. Using Landsat images, we assessed carbon sequestration of different time periods and areas. The biggest difference between this study and previous researches is that large-scale and multi-temporal carbon sequestration assessment can be done, and the goals of economic, accurate, and increasing efficiency can be achieved. In this study, the Net Primary Productivity (NPP) of 2005-2010 was calculated based on the light use efficiency model. By comparing the results of different research areas—Taipei and Kaohsiung, the effects of different climatic conditions and land use conditions on NPP was investigated. The results show that, under different environmental conditions, the carbon sequestration capacity is affected the most by climatic conditions. Furthermore, in the absence of data, it still can reflect the trend of different regions. Although not being able to effectively assess the total amount of a year, it still can be used to assess the trend of regional carbon sequestration capacity.

Biomass is an important ecological variable for understanding the responses of vegetation to the currently observed global change. The impact of changes in vegetation biomass on the global ecosystem is also of high relevance. The vegetation in the arid and semi-arid environments of Kazakhstan is expected to be affected particularly strongly by future climate change. Therefore, it is of great interest to observe large-scale vegetation dynamics and biomass distribution in Kazakhstan. At the beginning of this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. Remote-sensing-based Net Primary Productivity (NPP) models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass estimates based on NPP data. For validation of the results, biomass field data were collected in three study areas in Kazakhstan. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM). The second is the Biosphere Energy Transfer Hydrology Model (BETHY/DLR). Both models were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR was then used to calculate NPP for Kazakhstan for 2003–2011. The results were analysed regarding spatial, intra-annual, and inter-annual variations. In addition, the correlation between NPP and meteorological parameters was analysed. In the last part of this dissertation, a methodological concept for derivation of above-ground biomass estimates of natural vegetation from NPP time-series has been developed. The concept is based on the NPP time-series, information about fractional cover of herbaceous and woody vegetation, and plants’ relative growth rates (RGRs). It has been the first time that these parameters are combined for biomass estimation in semi-arid regions. The developed approach was finally applied to estimate biomass for the three study areas in Kazakhstan and validated with field data. The results of this dissertation provide information about the vegetation dynamics in Kazakhstan for 2003–2011. This is valuable information for a sustainable land management and the identification of regions that are potentially affected by a changing climate. Furthermore, a methodological concept for the estimation of biomass based on NPP time-series is presented. The developed method is potentially transferable. Providing that the required information regarding vegetation distribution and fractional cover is available, the method will allow for repeated and large-area biomass estimation for natural vegetation in Kazakhstan and other semi-arid environments.