Predicting the impacts of climate change on the distribution and conservation of endemic forest land snails of Madeira Island

Climate change is emerging as one of the major threats to natural communities of the world’s ecosystems; and biodiversity hotspots, such as Madeira Island, might face a challenging future in the conservation of endangered land snails’ species. With this thesis, progresses have been made in order to properly understand the impact of climate on these vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understand the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of MaxEnt, important changes in the species suitable areas were obtained. Laurel forest species, highly dependent on precipitation and relative humidity, may face major losses on their future suitable areas, leading to the possible extinction of several endangered species, such as Leiostyla heterodon. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of IUCN assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

Soil physical and Chemical properties of cultivated pasture on forest land, Roraima, Brazil .

Soil conditions under pasture were examined in a range of sites representing the sequence of conversion of forest to pasture at two locations in the vicinity of Ilha de Maracã, Roraima. Comparisons were made with adjacent savana. Soil bulk densities shown to increase after forest clearance and soil chemical data indicate that the initial beneficial effects on nutrient supply of burning forest debris are rather short-lived. Very low levels of available phosphorus prevail in areas of savanna and cultivated pasture of all ages. Variations in the status of older cultivated pastures are mainly attributable to different grazing levelt.

Geomorphology and land development in the Maraca area of northern Roraima, Brazil.

The geomorphological materials and forms of the Maraca area of Roraima, Brazil are described, an their sgnificance for land development examined. Significant contrasts are noted in areas presently under rainforest and savanna vegetation. Lateritic gravels and extensive shetwash accumulations in savanna areas constrast with incipient or absent plinthite development, few gravels and limited evidence of colluvium under rainforest. Terrain is in general relatively highly-dissected. Slope profiles are characterised, particularly within the savanna zone, by a relatively steep lower concavity. These contrasts are sharply-demarcated by the present savanna/rainforest bondary, unexpectedly in view of the generally accepted hypothesis of repeated contraction an expansion of Amazonian rainforest throughout the Pleistocene. It is concluded that geomorphological conditions in the Maraca area are not favorable for land develoment.

Land of the rising soy an internationalization plan for nutre to the Japanese Soy milk market

The present work project studies the next step in the internationalization process of Shoyce, the soy milk products brand of Nutre. In order to select the best target market in the Asia-Pacific for Nutre to export, a sequential screening process was developed using two complementary approaches: preliminary country screening and country ranking, followed by an in-depth analysis of the country ranking first. The analysis revealed Japan as the most attractive country for Shoyce’s international expansion. Potential entry modes in the Japanese soy milk market were then evaluated, whereby direct exporting via a local distributor was found to be the most appropriate.

Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.

Leaf micronutrient concentrations and potential photosynthesis in Ochroma pyramidale established in a degraded land

In the Brazilian Amazon, large areas of abandoned lands may revert to secondary forest. In the process, pioneer tree species have an important role to restore productivity in old fields and improve environmental conditions. To determine potential photosynthesis (Apot), stomatal conductance (g), transpiration (E), and leaf micronutrient concentrations in Ochroma pyramidale (Cav. ex Lam.) Urban a study was carried out in the Brazilian Amazon (01o 51' S; 60o 04' W). Photosynthetic parameters were measured at increasing [CO2], saturating light intensity (1 mmol (photons) m-2 s-1), and ambient temperature. The rate of electron-transport (J), Apot,and water-use efficiency (WUE) increased consistently at increasing internal CO2 concentration (Ci). Conversely, increasing [CO2] decreased gs, E, and photorespiration (Pr). At the CO2-saturated region of the CO2 response curve (1.1 mmol (CO2) mol-1(air), J was 120 μmol (e-) m-2s-1 and Apot reached up to 24 μmol (CO2) m-2s-1. Likewise, at saturating C1 g and E were 30 and 1.4 mmol (H2O) m-2s-1, respectively, and P 2 r about 1.5 μmol (CO2) m-2s-1. Foliar nutrients were 185, 134, 50, and 10 μmol (element) m-2 (leaf area) for Fe, Mn, Zn, and Cu, respectively. It was concluded that [CO ] probably limits light saturated photosynthesis in this site. Furthermore, from a nutritional point of view, the low Fe to Cu ratio (15:1) may reflect nutritional imbalance in O. pyramidale at this site.

Changes in population and land use over time in the Ecuadorian Amazon

This paper draws upon a detailed longitudinal survey of households living on agricultural plots in the northern three provinces of the Ecuadorian Amazon, the principal region of colonization by migrants in Ecuador since the 1970s. Following the discovery of petroleum in 1967 near what has subsequently come to be the provincial capital and largest Amazonian city of Lago Agrio, oil companies built roads to lay pipelines to extract and pump oil across the Andes for export. As a result, for the past 30 years over half of both Ecuador's export earnings and government revenues have come from petroleum extracted from this region. But the roads also facilitated massive spontaneous in-migration of families from origin areas in the Ecuadorian Sierra, characterized by minifundia and rural poverty. This paper is about those migrants and their effects on the Amazonian landscape. We discuss the data collection methodology and summarize key results on settler characteristics and changes in population, land use, land ownership, technology, labor allocation, and living conditions, as well as the relationships between changes in population and changes in land use over time. The population in the study region has been growing rapidly due to both natural population growth (high fertility) and in-migration. This has led to a dramatic process of subdivision and fragmentation of plots in the 1990's, which contrasts with the consolidation of plots that has occurred in most of the mature frontier areas of the Brazilian Amazon. This fragmentation has led to important changes in land tenure and land use, deforestation, cattle raising, labor allocation, and settler welfare.

Diversity of the land resources in the Amazonian State of Rondônia, Brazil

Conflicting opinions are recorded in the literature concerning the suitability of Amazon lands for sustainable agriculture following deforestation. This article has been written to shed light on this question by summarizing climate, landform, soil and vegetation features from the findings of a land resource study of the Brazilian state of Rondônia in south-west Amazonia. The work, which followed the World Soils and Terrain Digital Database (SOTER) methodology, was financed by the World Bank. During the course of the survey special emphasis was given to studying soils; 2914 profiles were analyzed and recorded. The study identified a complex pattern of land units with clear differences in climate, landform, soils and native vegetation. Forested areas mosaic with lesser areas of natural savannas. The latter occur on both poorly-drained and well-drained, albeit nutrient deficient sandy soils. The tallest and most vigorous forests or their remnants were seen growing on well-drained soils formed from nutrient-rich parent materials. Many of these soils could, or are being used for productive agriculture. Soils developed on nutrient-poor parent materials support forests that are significantly lower in height, and would require large lime and fertilizer inputs for agriculture. Low forests with high palm populations and minor areas of wet land savannas cover the poorly drained soils. It is evident that forest clearing in the past was indiscriminant; this cannot be condoned. The diversity of land conditions found throughout Rondônia would suggest that many past studies in the Amazon have simply been too broad to identify significant soil differences.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Spatial and temporal evolution of family-farming land use in the Tapajós region of the Brazilian Amazon

Pressures on the Brazilian Amazon forest have been accentuated by agricultural activities practiced by families encouraged to settle in this region in the 1970s by the colonization program of the government. The aims of this study were to analyze the temporal and spatial evolution of land cover and land use (LCLU) in the lower Tapajós region, in the state of Pará. We contrast 11 watersheds that are generally representative of the colonization dynamics in the region. For this purpose, Landsat satellite images from three different years, 1986, 2001, and 2009, were analyzed with Geographic Information Systems. Individual images were subject to an unsupervised classification using the Maximum Likelihood Classification algorithm available on GRASS. The classes retained for the representation of LCLU in this study were: (1) slightly altered old-growth forest, (2) succession forest, (3) crop land and pasture, and (4) bare soil. The analysis and observation of general trends in eleven watersheds shows that LCLU is changing very rapidly. The average deforestation of old-growth forest in all the watersheds was estimated at more than 30% for the period of 1986 to 2009. The local-scale analysis of watersheds reveals the complexity of LCLU, notably in relation to large changes in the temporal and spatial evolution of watersheds. Proximity to the sprawling city of Itaituba is related to the highest rate of deforestation in two watersheds. The opening of roads such as the Transamazonian highway is associated to the second highest rate of deforestation in three watersheds.