R. Petitpas, J.T. Ibarra, M. Miranda, and C. Bonacic. 2016. Spatial patterns in a 24- year period show a case of increase forest cover and decrease fragmentation in Andean temperate landscapes, Chile. Cien. Inv. Agr. 43(3):384-395. Changes in landscape pattern were studied in a temperate landscape of the La Araucanía Region, Chile. Using aerial photographs from 1983 and 2007, we created land use/land cover maps. We then quantified the changes in composition and configuration by using landscape metrics and an adjacency matrix. By 2007, the dominant land cover had changed from agriculture to native vegetation. Residential areas showed the largest relative increase (670%) and had significant adjacency with native vegetation. The native vegetation increased by 375 ha, but the number of patches decreased by 45% and the mean patch area increased by 124%, which indicated that fragmentation decreased. The growth of tourism and the preference for “natural” spaces by new residents are suggested as the main drivers of this native vegetation recovery. Understanding the process of forest recovery may be helpful for reversing the general trend of forest loss in temperate forests of South America. This research is a first approach in exploring specific cases of native vegetation recovery and decreases in fragmentation in this Global Biodiversity Hotspot.
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Land use change models such as LCM provided an important spatio-temporal information of land use and land cover changes especially on urban areas. It also provide the possibility to understand the influence of urban dynamics supported by a set of drivers. Model results have shown remarkable changes in built up areas between the study periods. The accuracy of MLP to generate transition potential maps has got an acceptable value of 61%. Simulated results of 2010 quantified that much of agricultural land had been converted in to built up areas regardless of the driving factors which have negative impacts on the simulation process. Although the accuracy of MLP was good to model transition potential maps, simulated results using Markov chain showed visible differences with the actual land cover map of 2010. Validation results based on total disagreements (quantity and allocation disagreement) showed that the model, LCM, performed a total disagreement of 13% and considered as a lower performance. Validation using a three map comparison also confirmed that LCM showed a lower accuracy in predicting land use changes (hits) correctly in this study area (Bahir Dar area).
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This was obtained through the overlaying of two forest cover and land use maps with different dates and the resulting data analysis arranged in a matrix, which allowed efficient identification of changes between categories. The matrix is arranged in such a way, that the cover classes of the initial date (𝑇₁) are placed on the rows, and the categories of the final date (𝑇₂) are placed in the columns, whereby only the diagonal of the matrix shows the total amount of stable landscape between the two dates, while the rest corresponds to all possible combinations of exchange between categories (Table 1).
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were captured in February and March, during the dry season. Their spatial reference is path 022 row 048 in the Landsat World Reference System 2. On-screen visual interpretation was carried out by a method similar to that proposed for Tropical Ecosystem Environment Observations by Satellites Project (TREES) phase II (Achard et al., 2002). The three land- cover maps were digitized in El Colegio de la Frontera Sur geographical analysis laboratory. Different types of land cover were delineated by digitizing them with program ArcInfo 7.1. A color composition RGB 4, 7, 5 was used to display them on the screen. Bands 4 (0.750–0.900 lm), 7 (2.090–2.350 lm) and 5 (1.550–1.750 lm) were used to enhance differences among stages of succession of forested areas as well as features of agricultural and grazing areas. A scale of display of 1:80,000 was used, and a minimum map unit of 5 ha was applied. The interpretation was aided by three additional sources of information: INEGI vegetation and land-use maps with a scale of 1:250,000, edited from 1984 to 1988; the 2000 National For- est Inventory; (Palacio et al., 2000) and field verification.
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Ecuador has the highest deforestation rate in South America causing large scale soil erosion. Inter-Andean watersheds are especially affected by a rapid increase of the population leading to the conversion of large areas of montane forest into pasture and cropland. In this study we estimate soil erosion risk in a small mixed land use watershed in the southern Andes of Ecuador. Soil loss was estimated at a spatial resolution of 30 m, using the Revised Universal Soil Loss Equation (RUSLE) where the RUSLE factors were estimated based on limited publicly available data. Land cover maps for 1976, 2008 and 2040 were created assuming increasing deforestation rates over the ensuing decades. Greater erosion rates are estimated for succession areas with agricultural cropland and pasture with maximum values of 936 Mg ha -1 yr -1 , where slopes and precipitation amounts are greatest. Under natural forest vegetation, the estimated soil erosion rates are negligible (1.5 to 40 Mg ha -1 yr -1 ) even at steep slopes and higher elevations where rainfall amounts and intensities are generally higher. When the entire watershed has undergone substantial deforestation in 2040, erosion values may reach 2021 Mg ha -1 yr -1 . Vegetation cover is the most important factor for potential soil erosion. Secondary factors are related to rainfall (R-factor) and topography (LS-factors). The spatial predictions of potential soil erosion have only limited meaning for erosion risk, this method provides an important screening tool for land management and assessment of land cover change.
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can be used to link trends in vegetation cover changes with the underlying processes that are responsible for the changes in land cover. This connection helps researchers understand the mechanisms involved, generates predictions for future rates of change, identifies vulnerable sites, and contributes to the design of policies that can adequately respond to LULC changes (Braimoh, 2006; Henríquez et al., 2006; Pineda et al., 2009; Camacho et al., 2010). The regional analysis of land cover changes in heterogeneous landscapes can be masked by spatial variations caused by both bioclimatic and socioeconomic factors (Martínez-Fernández et al., 2015). According to Pontius et al. (2004), an appropriate methodology to analyze changes in land use is to obtain maps at two different times, examine the changes with a transition matrix to identify the most important transitions, and then investigate the processes that generate the transitions between land cover types. Often, researchers analyze this transition matrix at a very general level (e.g., CONAF- CONAMA-BIRF, 1999; Aguayo et al., 2009; CONAF-CONAMA, 2009; CONAF, 2011) and draw conclusions about the dynamics of LULC change based only on the net change in the class totals between the years (Mertens and Lambin, 2000; Braimoh, 2006). However, the net change can dramatically underestimate the total change in the landscape (Mertens and Lambin, 2000). It is possible that the change occurs in such a manner that a given category changes its location between sampling times, but its magnitude stays the same (Pontius et al., 2004).
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Only the FAO soils map was available for the whole country. as we11 as many loca1 soil stud ies covering srnall areas, when the MWP project was started. Very good p resent and pótential 1:50,000 scale land use maps are being deve1oped in Mexico by CETENAL (Com1sí6n de tstudios del Territorio Nacional). Unfortunate1y. the FAO soi1s map is too general, and not suíta ble for water resources planning. On the other hand, CETENAL maps are exce11�nt but cover less than a third of the country at present time.
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The guiding principle of this paper is that the organization of space is inseparable from the quest for sustainable development. Inequalities, a growing concern for most countries and the international community, are expressed in the physical segregation of different income, social and ethnic groups and in the sub-standard conditions of the places where the poor work live and work. The negative externalities caused by haphazard city growth and lack of proper planning such as sprawl, pollution, and traffic congestion are a tremendous burden on the cities’ vocation for attracting investment, employment and sustainable growth. The physical segregation of the city according to separate functional areas, such as business, industry and housing, creates dullness, alienation and insecurity. The unregulated functioning of land markets only reinforces the tendency to produce physical separations between urban elites and the rest of the urban population. Sprawl and low density development compete with the preservation of the vital roles of peri-urban and rural areas in feeding larger urban centres and offering sustainable livelihoods to rural residents. Finally, the same physical development model is a major cause of environmental degradation and a major contributor to CO2 emissions far in excess of what wiser spatial organization models would entail.
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76 Consequently, land use change has become an area of particular concern due to rapid land conversion practices in the highlands of the country. In recent decades, Remote Sensing (RS) with multi-temporal high-resolution satellite data has been widely used to obtain land cover information such as degradation level of forests and wetlands, rate of urbanization, intensity of agricultural activities, and other human-induced changes (Yuksel et al., 2008). However, these bio-physical approaches do not give information about why changes occur. Understanding land use/cover study requires an understanding of people and their societal situation, their priorities, livelihood strategies, views on the land, and the wider implications of social, political, cultural, biophysical and institutional factors, among others (Maro, 2011). Incorporations of local experiences of key informants in the community provide information on past, present and expected future land use changes (Sandewall et al., 2001). Therefore, it is necessary to go beyond disciplinary trend studies and examine methods for integrating LULC and social research to get knowledge and the experiences of different stakeholders. Therefore, integration of the remote sensing and household survey are important tools to study changes in land cover patterns and dynamics in order to obtain rapid, economical, reliable, and accurate results (Sertel et al., 2008). As explicitly stated by Maro (2011), one of the merits of using qualitative research in social science and survey research methods to understand local perceptions of land use change is its obvious contribution to answer the questions ‘why is change occurring?’ and ‘so what?. Klintenberg et al. (2007) used individual semi- structured interviews with local farmers to understand whether national and local perceptions of environmental change in central Northern Namibia were related. These and other similar studies show that a combination of local and scientific knowledge can lead to more useful assessment of land use change and its implications for local land- users and managers (Klintenberg et al., 2007). Hence, the integration of information from household surveys and data on land cover changes derived from remote sensing improves our understanding of the causes and processes of LULC changes (Benoît et al., 2000).
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Abstract. Digital classification of satellite images has become an important tool for land use cover monitoring. There have been many studies and researches aiming to develop and assess classification methods which are capable of pro- ducing results with higher accuracies in order to support decision making. Through a literature review, we found various classification methods that have been tested and are able to produce better results than the conventional one. This work compares different classification methods
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The use of sugarcane ethanol in Brazil is one of the greatest examples of partial or total substitution of oil in the world. Liquid biofuels are the fastest growing sector of bioenergy. Besides reducing oil consumption, the production and use of sugarcane ethanol has competitive advantages in economic and environmental terms, compared both to non- renewable fuels and renewable fuels from other crops. Their development has been the result of a interaction of policies, public and private institutions and partnerships that have created one of the most dynamic and competitive innovation systems in the world (Furtado et al., 2011). Brazilian ethanol production grew by 260 % from 2001 to 2009, achieving 27,512 Hl in 2009 (São Paulo state sugarcane industry association, Unica, 2013).The United States has set objectives of biofuel usage for 136,274 million litres in 2022 (Environmental Protection Agency, EPA, 2007). Both US and European targets are above the current production capacity of each block, so they rely on internationally traded ethanol to meet them (FAO, 2013). Since Brazil is the world’s second largest producer and first largest exporter, the country is in position to fill this gap and so global demand for Brazilian biofuel will continue to grow.
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Other tentative to build smart training sample is Active Learning (AL), an algorithm widely used in Machine Learning, which is being introduced in remote sensing classification lately. Rajan et al. (2008) propose an active learning approach for hyper-spectral images classification, and they get a better accuracy classification than just choosing traditional random samples. Under the philosophy of these methods, lie the acquisition of “smarter” samples which better defines the classes or the border between them (some AL algorithms are built upon the Support Vector Machines -SVM- where the samples are chosen in the margin between different classes). Regarding to Tuia et al. (2011) “Active Learning aims at building efficient training set by improving iteratively the performance of the model”. AL models return the pixel with more uncertainty to be classified, which are accurate labelled by the user and reincorporated to the model to reinforce and optimize it.
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(Ortiz, 2000; INEGI, 2005). Population density increased by 70 per cent from 1990 to 2000 (Ayuntamiento de Texcoco, 2003), and with the demand for goods and services as well as problems linked to urban land use change: lack of water, ecosystems deterioration, land function and ability alterations. The growth of urban territory in the municipio has been with no order at all, and it has expanded towards crop lands, forests and grasslands. The extraction of materials for the building sector (mining) covers croplands, forests and areas nearby urban sites (Muro, 1996; Ayuntamiento de Texcoco, 2003; Moreno, 2007). This is the results of poor planning politics relater to territorial development of the municipio and an inefficient decision taking about the management of natural resources. The municipio has had important social and economic changes, with a strong economy development in the tertiary sector that slowly extends to the East; thus, that part of Texcoco municipio shows a great potential for land use change (Moreno, 2007).
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increment in forest cover in both studied areas was observed. Massive national program to reclaim and restore Israel’s degraded Mediterranean landscape has a significant role in increasing forest cover in the studied areas over time. The result also showed dynamic temporal and spatial variation trends in land cover fragmentation. Patch number was relatively higher in the present period. A greater probability of dispersion in the forest land and woodland categories was observed. Woodland in PEF and Forest land in the present day had the highest IJI in Haifa area. On the other hand, Open space in PEF and agricultural land in the present day had the highest IJI in Jerusalem area. One important aspect which stands out from the study is that fragmentation seems to be driven by socioeconomic development need of the growing population in the studied areas. Generally, this study provides important knowledge on spatiotemporal land cover patterns in the studied areas and each of the results has a fundamental role to play on planning conservation works that aim to protect fragile land covers that are subjected to anthropogenic disturbances in the studied areas.
Diagonal values in the matrix at Table 5 keep tabs on area whose land cover did not change in the period studied. The persistence index is intended to assess land cover vulnerability to transitions that can either be area increase or reduction. The percentage of unchanged cover area was 98% for the entire basin, which means a great persistence of the initial landscape; the additional 2% is made up of all the places that had modifications in their land cover. This result is in agreement with similar research. Pontius et al. (2004), Burnicki et al. (2007) y Plata et al. (2009) stated that landscape persistence frequently exceeds 90%. Persistence in Mexico state was estimated in 93.3% (Pineda et al., 2009), and it went up to 97 % at the scale of the entire country (Velázquez et al., 2002b). Cortina et al. (1998) assessed 90% persistence in two south western states in Mexico.
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In recent decades, the analysis of floods in urban areas has improved considerably with the introduction of accurate numerical models [9–11] and advanced methodologies, which aim in particular at the combination and integration of land use change forecasting models with hydrological models, with the objective of being able to determine increasingly precise peak discharge rates as a function of the predicted urbanization [12,13]. In particular, Wang et al.  presented the application of a cellular automata based model (Celular Automata Dual-DraInagE Simulation, CADDIES) to a small study area to analyze flood inundation, and demonstrate the importance of identifying and using key urban features, obtained from terrain data, to better reproduce high resolution flood processes. The effect of urbanization and changes in land use on floods has been studied from different perspectives, focusing both on river feeding catchments and on potentially floodable areas [14–17], with the conclusion that an appropriate study of land use and consequent management are crucial in flood attenuation.
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Short, A.D., 1996. The role of wave height, period, slope, tide range and embaymentisation in beach classifications: a review. Revista Chilena de Historia Natural 69(4): 589-604. Silva, R., Baptista, P., Veloso-Gomes, F., Coelho, C. y Taveira Pinto, F., 2009. Sediment grain size variation on a coastal stretch facing the North Atlantic (NW Portugal). Journal of Coastal Research SI(56): 762-766.
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Heat Islands can raise energy demand for air conditioners during the summers, which emit more heat and by consequence degrades local air quality (Rosenfeld & Akbari, 1995). Most of this heat is produced in high density centers, where most of the office buildings, industries or overcrowded housing developments are located, and demand a greater amount of energy for cooling. It is said that, from 3 to 8% of the energy demanded for air conditioners in the United Sates, is used to mitigate the effect of Heat Islands. There has been a rise in global temperature between 1 to 2 °C since the 1950‟s (Hough, 1998, p. 247). It is estimated that, a sixth of the total energy consumed in the United States is used to cool down buildings, and equals to 40 billion dollars per year (Guhathakurta & Gober, 2007). In other resource, rise in temperature increase the demand on water supply. Research done in Phoenix showed that the rise in daily temperature is directly related with the increase of water use to 290 gallons per family, which also implies negative effects on economics and rational use (Guhathakurta & Gober, 2007).
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satisfy the requirements of the Kyoto protocol, the availability of updated cartography at different sca- les becomes more and more important. International organizations such as the European Space Agency (ESA) are involved in global projects that aim to provide these products to decision and policymakers all over the world. One of the most determining pro- jects is the currently running European GLOBCO- VER project (DUP-ESA, 2006). The objective is to develop a service which will produce a 300m global land cover map for the year 2005 using mainly full resolution data acquired by the MERIS (Medium Resolution Imaging Spectometer) sensor on-board ENVISAT (Joint Research Centre – Terrestrial Ecos- ystem Monitoring, 2006).
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The study area must be divided into zones, an important decision to be made at the beginning of a study. The number of zones has many effects on the modeling process. It is a common belief that lots of zones make a better model. This is not necessarily true, since a lot depends on the quality of the information. The error in smaller zones is larger than in big zones. Some variables are more reliable than others. For instance, population is reasonably accurate for small zones because it usually comes from census data. Employment by zone is far less reliable, and the number of trip origins and trip ends is even less.
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