Changes in land use and land cover are associated with many environmental issues observed on the earth’s surface. In the last decades, these changes were unprece-dented, mainly in tropical forest areas. The Brazilian Amazon, the world’s largest tropical forest, lost around 200.000 km² of primary forest in the last ten years (INPE, 2005). Considering this, and the consequences caused by this deforestation, it is important to know and define correctly the responsible agents, aiming at better pub-lic policies that can help preserve the forest. Searching for indicators that could help to identify the deforestation agents, some studies, such as Mertens and Lambin (1997), suggest that every deforestation process shapes the forest land in a specific way, producing a spatial pattern that can be interpreted as indicative of the agents with specific economic activities. Based on this hypothesis, the objective of this study was to contribute to a better understanding of land change processes in the Amazon forest, investigating the linkages between spatial patterns of deforestation, as visualized in satellite images, and different agents and their specific economic activities. To reach this objective, our methodological approach was based on socio-economic data acquired at a household level combined with data from satellite im-ages. First, different spatial patterns of deforestation were identified on the satellite images, based on the typologies proposed by Husson et al. (1995). Then, some of the identified spatial patterns were isolated and analyzed for specific aspects, such as, the deforestation rate calculated through satellite images. socio-economic character-istics based on household survey data and evolution of land use and land cover based on thematic maps derived from satellite images. In addition, cluster analysis was applied using the socio-economic data (household survey) and land use and land cover data (satellite images) in a search for homogeneous groups related to the spa-tial pattern. In the end, an Analysis of Variance (ANOVA) was applied to confirm the differences between spatial patterns. The results suggested that the different spatial patterns of deforestation found in the study area can be related to specific economic activities. Nevertheless, the re-sults have indicated that the spatial configuration is not a consequence of its main economic activity. They suggest that the spatial configuration is linked to the settle-ment project, and the main economic activity in the spatial patterns is a consequence of a set of factors such as: size of property, location and disposition of the property, presence or absence of infrastructure (road, market, transportation, economic and technical).

Woody plant invasion of grasslands is prevalent worldwide, but the biogeochemical consequences of this vegetation shift remain largely unquantified. In the Rio Grande Plains, TX, grasslands and savannas dominated by C4 grasses have undergone succession over the past century to subtropical thorn woodlands dominated by C3 trees/shrubs. To elucidate mechanisms of soil organic carbon (SOC) and soil total N (STN) storage and dynamics in this ecosystem, I measured the mass and isotopic composition (δ13C, δ15N) of C and N in whole-soil and soil size/density fractions in chronosequences consisting of remnant grasslands (Time 0) and woody plant stands ranging in age from 10-130 years. Rates of SOC and STN storage averaged 10-30 g C m-2yr-1 and 1-3 g N m-2yr-1, respectively. These accumulation rates increased soil C and N pools 80-200% following woody encroachment. Soil microbial biomass (SMB-C) also increased after woody invasion. Decreasing Cmic/C org and higher qCO2 in woodlands relative to grasslands suggests that woody litter is of poorer quality than grassland litter. Greater SOC and STN following woody invasion may also be due to increased protection of organic matter by stable soil structure. Soil aggregation increased following woody encroachment; however, most of the C and N accumulated in free particulate organic matter (POM) fractions not protected within aggregates. Mean residence times (MRTs) of soil fractions were calculated based on changes in their δ13C with time after woody encroachment. Free POM had the shortest average MRTs (30 years) and silt+clay the longest (360 years). Fine POM had MRTs of about 60 years, reflecting protection by location within aggregates. δ15N values of soil fractions were positively correlated with their MRTs, suggesting that higher δ15N values reflect an increased degree of humification. Increases in SOC and STN are probably being sustained by greater inputs, slower turnover of POM (some biochemical recalcitrance), and protection of organic matter in aggregates and association with silt and clay. Grassland-to-woodland conversion during the past century has been geographically extensive in grassland ecosystems worldwide, suggesting that changes in soil C and N dynamics and storage documented here could have significance for global C and N cycles.

<p>Land use and land cover changes significantly affect C storage in terrestrial ecosystems. Programs intended to compensate land owners for the maintenance or enhancement to C stocks are promising, but require detailed and spatially explicit C distribution estimates to monitor the effectiveness of management interventions. Savanna ecosystems are significant components of the global C cycle, however, they have not received much attention for the development of C monitoring approaches. In this dissertation I have investigated three of the aspects related to woody plant cover dynamics in semiarid savannas of central Argentina: spatio-temporal dynamics, precise field surveying and scaling from field to region with the use of freely available remotely sense data. </p><p>To examine the long term changes in woody plant cover, I first carefully extracted information from historical maps of the Caldenal savannas of central Argentina (190,000 km2) in the 1880s to generate a woody cover map that was compared to a 2000s dataset. Over the last ~120 years, woody cover increased across ~12,200 km2 (14.2 % of the area). During the same period, ~5,000 km2 of the original woody area was converted to croplands and ~7,000 km2 to pastures, about the same total land area as was affected by woody plant encroachment. A smaller area, fine scale analysis between the 1960s and the 2000s revealed that tree cover increased overall by 27%, shifting from open savannas to a mosaic of dense woodlands along with additional agricultural clearings. Statistical models indicate that woody cover dynamics in this region were affected by a combination of environmental and human factors.</p><p>To assess the consequences of woody cover dynamics on C, we also measured ecosystem C stocks along a gradient of woody plant density. I characterized changes in C stocks in live biomass (woody and herbaceous, above- and belowground), litter, and soil organic carbon (to 1.5 m depth) pools along a woody plant cover gradient (0 to 94 %). I found a significant increase in ecosystem C stocks with increasing woody cover, with mean values of 4.5, 8.4, 12.4, and 16.5 kg C m-2 for grasslands, shrublands, open and closed forests, respectively. Woody plant cover and soil silt content were the two primary factors accounting for the variability of ecosystem C. I developed simple regression models that reliably predict soil, tree and ecosystem C stocks from basic field measurements of woody plant cover and soil silt content. These models are valuable tools for broad scale estimation if linked to regional soil maps and remotely sensed data, allowing for precise and spatially explicit estimation of C stocks and change at regional scales.</p><p>Finally, I used the field survey data and high resolution panchromatic images (2.5 m resolution) to identify tree canopies and train a regional tree percent cover model using the Random Forests (RF) algorithm. I found that a model with summer and winter tasseled cap spectral indices, climate and topography performed best. Sample spatial distribution highly affected the performance of the RF models. The regression model built to predict tree C stocks from percent tree cover explained 83 % of the variability, and the spatially explicit tree C model prediction presented an root mean squared error (RMSE) of 8.2 tC/ha which represented ~30% of the mean C stock for areas with tree cover. Our analysis indicates that regionally over the last ~120 years, increases in woody plant cover have stored significant amounts of C (95.9 TgC), but not enough to compensate for in C generated by the conversions of woodlands and natural grasslands to croplands and pastures (166.7 TgC), generating a regional net loss of 70.9 TgC. C losses could be even larger in the future if, as predicted, energy crops would trigger a new land cover change phase in this region.</p> / Dissertation

Changing land cover from prairie grasslands to intensive, primarily cereal agriculture, over the North American Great Plains since the mid-19th century, has had a hydrological and climatological impact on that ecosystem (Pielke, Sr., et al., 2011). Agriculture has introduced timed harvest seasons, irrigation, and C3 photosynthesizing crops with poorer water efficiency than the grasses it replaced. All of these changes have been linked to exacerbated drought conditions and warmer temperatures; however, few studies have quantified this relationship at the continental scale. In order to evaluate the change imposed by this shift in land use and land cover, the observation based 20th Century Reanalysis Project (20CR) was used to quantify the climatological differences in temperature and humidity between areas of natural prairie and agriculture over the 20th century. An additional analysis used the Observation Minus Reanalysis (OMR) technique to isolate the surface climate signal found in the 20CR. We find indications that changing land cover had an impact on climate. However, using observation based data returned no evidence of a statistically significant change due to the small land use and land cover change (LULCC) signal within the larger climate noise. Therefore, an idealised modelling experiment was undertaken using the Geophysical Fluid Dynamics Laboratory (GFDL) AM2-LM2 atmosphere-land model to remove these other influences. This experiment compared the results of two model simulations: one where the entirety of the prairie was preserved as grassland (GRASS), and another where the entire prairies had been converted into an agricultural area (AGRIC). Relative to GRASS, the AGRIC simulation has reduced surface albedo and root zone depth, and increased roughness length over the prairies, which collectively cause a significant summer drying. This occurs when the shallower rooting zone limited potential evapotranspiration (PET) forcing the additional energy created by turbulent mixing and a lower surface albedo to warm the air, surpassing PET and reaching drier conditions faster. While not conclusive, the results presented in this thesis represent a step towards filling the gaps in understanding land-atmosphere interactions and connecting LULCC to climate.

This thesis brings new information about development of five chosen snad-pits in CHKO Třeboňsko. It represents future state of this sand-pits with different rate of near ? natural restoration used during the reclamation. The results are accompanied by comparison of surface temperature of each category of land cover.

abstract: This dissertation research studies long-term spatio-temporal patterns of surface urban heat island (SUHI) intensity, urban evapotranspiration (ET), and urban outdoor water use (OWU) using Phoenix metropolitan area (PMA), Arizona as the case study. This dissertation is composed of three chapters. The first chapter evaluates the SUHI intensity for PMA using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product and a time-series trend analysis to discover areas that experienced significant changes of SUHI intensity between 2000 and 2017. The heating and cooling effects of different urban land use land cover (LULC) types was also examined using classified Landsat satellite images. The second chapter is focused on urban ET and the impacts of urban LULC change on ET. An empirical model of urban ET for PMA was built using flux tower data and MODIS land products using multivariate regression analysis. A time-series trend analysis was then performed to discover areas in PMA that experienced significant changes of ET between 2001 and 2015. The impact of urban LULC change on ET was examined using classified LULC maps. The third chapter models urban OWU in PMA using a surface energy balance model named METRIC (Mapping Evapotranspiration at high spatial Resolution with Internalized Calibration) and time-series Landsat Thematic Mapper 5 imagery for 2010. The relationship between urban LULC types and OWU was examined with the use of very high-resolution land cover classification data generated from the National Agriculture Imagery Program (NAIP) imagery and regression analysis. Socio-demographic variables were selected from census data at the census track level and analyzed against OWU to study their relationship using correlation analysis. This dissertation makes significant contributions and expands the knowledge of long-term urban climate dynamics for PMA and the influence of urban expansion and LULC change on regional climate. Research findings and results can be used to provide constructive suggestions to urban planners, decision-makers, and city managers to formulate new policies and regulations when planning new constructions for the purpose of sustainable development for a desert city. / Dissertation/Thesis / Doctoral Dissertation Geography 2018

<p>Tropical ecosystems play a key role in regulating the global climate and the carbon cycle thanks to the large amounts of water and carbon exchanged with the atmosphere. These biogeochemical fluxes are largely the result of high photosynthetic rates. Photosynthetic activity is highly dependent on climate and vegetation, and therefore can be easily modified along with changes in those two factors. A better understanding of what drives or alters photosynthetic activity in the tropics will lead to more accurate predictions of climate and subsequent effects on ecosystems. The seasonal pattern of photosynthetic activity is one of the main uncertainties that we still have about tropical ecosystems. However, this seasonality of tropical vegetation and its relationship to climate change and land cover is key to understanding how these ecosystems could be affected and have an effect on climate.</p><p>In this dissertation, I present three projects to improve our understanding about tropical ecosystems and how their photosynthetic activity is affected by climate and land cover change. The lack of field-based data has been one of the main limiting factors in our study of tropical ecosystems. Therefore, in these projects I extensively use remote sensing-derived data to analyze large scale and long term patterns. In the first study, I looked at the seasonal relationship between photosynthetic activity and climate, and how model simulations represent it. Vegetation in most of the tropics is either positively correlated with both water and light, or positively correlated with one of them and negatively with the other. Ecosystem models largely underestimate positive correlations with light and overestimate positive correlations with water. In the second study, I focus on the effect of land cover change in photosynthetic activity and transpiration in a highly deforested region in the Amazon. I find that land cover change decreases tropical forests photosynthetic activity and transpiration during the dry season. Also, land cover change increases the range of photosynthetic activity and transpiration in forests and shrublands. These effects are intensified with increasing land cover change. In the last project, I quantify the amount of change in evapotranspiration due to land cover change in the entire Amazon basin. Our remote sensing-derived estimates are well aligned with model predictions published in the past three decades. These results increase our confidence in climate models representation of evapotranspiration in the Amazon.</p><p>Findings from this dissertation highlight (1) the importance of the close relationship between climate and photosynthetic activity and (2) how land cover change is altering that relationship. We hope our results can build on our knowledge about tropical ecosystems and how they could change in the future. We also expect our analysis to be used for model benchmarking and tropical ecosystem monitoring.</p>

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