migration of illegal wood extraction to the northeast of Machadinho d’Oeste, into Colniza municipality in Mato Grosso State.
2.4.2 Database of potential location factors
Potential landuse/cover proximate drivers and location factors were selected based on the review of previous landuse studies, fieldwork information from 2001 and 2006 and data availability. The selected variables include biophysical, accessibility and socioeconomic aspects, as well as public policies. The first exploratory models included 55 variables, but only 38 had significant contributions in the final models (see Table 2.2). Classes of the categorical variables geomorphology, lithology and soil types are counted each as a unique variable. The grid database was built at a spatial resolution of 250 x 250 m, the highest resolution possible with the available data. This resolution is an exact multiplier of the average size of lots in the agrarian projects (2000 x 500 m). The original scale and resolution of the variables selected were quite different; especially biophysical variables have a different spatial variability than socioeconomic data and the accessibility measures. This suggests that some loss of information took place during the data aggregation process. In a preliminary test all data was aggregated to 500 m resolution. Initial analysis demonstrated similar patterns and correlation between deforested areas, secondary forest and location factors indicating limited loss of information. These results are consistent to other studies at multiple scales (Veldkamp and Fresco, 1997; Walsh et al., 1999). Therefore, it was decided not to change the data resolution and use only the 250 m resolution data.
In order to understand deforestation rates, it is crucial to take subsequent land uses and their dynamics into account. This paper focuses on developing methods to detect patterns of land-coverdynamics using data from remote sensing and identifying large-scale differences between subregions of the BrazilianAmazon as a sample region. To do so, we draw on the theory of Markov chains that has been used in the context of land-system science to describe and analyze land-coverdynamics (Bell and Hinojosa, 1977; Baker, 1989). Markov chains are stochastic systems that are described by transition probabilities between discrete states, here referring to a spe- cific land-use or land-cover type. An ensemble of such chains describes a collection of land patches that undergo stochas- tic transitions between land-cover classes. Because simple Markov models do not take spatial correlations into account, they often form only one part of hybrid land-cover models that introduce stochasticity into the model (see, e.g., Brown et al., 2000; Subedi et al., 2013). For example, Fearnside (1996) applied a Markov analysis to estimate greenhouse gas emissions from land-use change in the BrazilianAmazon and found that carbon storage in the land system decreases as it approaches an equilibrium.
Despite Marsh’s prose decrying humans’ impact upon the natural world, geographers paid scant attention to the consequences of human-environment interactions for LULC change in the early part of the twentieth century, due primarily to the preoccupation with environmental determinism (Glacken 1956) and the backlash against it (Wilson 2005). This changed in 1955, with a symposium entitled "Man's Role in Changing the Face of the Earth", sponsored by the Wenner-Gren Foundation, resulted in the production of a second major global assessment of human-environment interactions. The symposium brought together an interdisciplinary group that included many geographers. Man’s Role in Changing the Face of the Earth (Thomas 1956) emphasized human utilization of the environment and its impacts and highlighted the importance of understanding past and current history of global change processes. Kates et al. (1990) notes that this volume has had a lasting influence on scientists in the humanities and natural and social sciences, as it is characterized as a seminal work that influenced global-scale integrative thinking about the environment (Williams 1987, Hornsby 1998).
Land-change studies aim to observe and monitor landcover and landuse changes (LCLUC), explain its causes and consequences, and model its processes to predict future changes [Robinson et al., 2013]. LCLUC can alter regional as well as global climate through changing characteristics of the Earth’s surface and atmosphere [Jain et al., 2013]. It can affect the behavior of the essential components of the climate system such as biophysical (e.g., surface temperature, albedo, evaporation), biogeochemical (e.g., carbon cycle) and biogeographical (e.g., species location and migration) components [Robinson et al., 2013]. LCLUC is an important indicator to understand the interactions between anthropogenic activities and the environment [Dewan et al., 2012]. Understanding the dynamics and drivers of LCLUC at local, regional and global scales will help policy-makers in effectively targeting areas of concern and implementing proper landuse policies. Human activities have profound effect on landcover, especially observed in developing countries in the recent years where LCLUC are driven by socioeconomic development [Dewan et al., 2012] . To assess the landcover changes, there is an increasing demand of detailed spatial coverages with high temporal frequency to assess landcover changes [Thackway et al., 2013]. A large number of studies have been devoted to the LCLUC across the globe over different temporal and spatial time scales (E.g.: [Meiyappan et al., 2016], [Roy et al., 2015], [Reddy et al., 2016], [Huq et al., 2015], [Islam and Hassan, 2011], [Zaman et al., 2010], [Chowdhury and Koike, 2010]). However, further study is required to find the relationship between the dynamics and drivers of LCLUC at local, regional and global scales.
Extensive deforestation across the BLA started in the 1970s and was concentrated along the southern and eastern rims of the region . The main deforestation drivers were conversion of forest to cropland and/or cattle ranching, which were carried out both by small farmers and large landholders . According to the data provided by INPE, 2013 , the states where the three sites are located displayed different deforestation rates (Figure 10). Deforestation rates were always higher in Para´, followed by Rondoˆnia and Amazonas. However, since 2003–2005, deforesta- tion rates have been decreasing, with Rondoˆnia experiencing a rapid decrease leading to values closer to those observed in the Amazonas state. Several factors contributed to this decline, namely the improvement of market-driven environmental governance . Para´ experienced the highest number of settled families by agrarian reform in the nine states composing the BLA (,31,000 settled families per year in the period 2003–2006), and the number has been increasing since the 1960s . Although the vast majority of deforestation in the BLA can be tracked to large landholders occupying the land for cattle ranching, the imple- mentation of planned settlements is not negligible at all, especially in Rondoˆnia, which is known by its small farmers’ radial, fishbone and watershed deforestation patterns (e.g., ). For example, up to the mid-1990s, the number of settled families in Rondoˆnia was only second to Para´, with 1,423 settled families per year against 1,462 settled families per year in Para´ .
Depending on the type of information you want to extract from the origin data, classes may be associated with known features on the ground or may simply represent areas that look different to the computer. An example of a classified image is a landcover map, showing vegetation, bare land, pasture, urban and so forth. In this study, we are using supervised image classification maps, one common application of remotely-sensed images to rangeland management is the creation of maps, vegetation type, or other discrete classes by remote sensing software. In supervised classification, the image processing software is guided by the user to specify the landcover classes of interest.
Land-cover changes are not only cumulative in nature but also the result of a number of interacting variables and processes. The distribution of the various land-cover and land-use types are primarily controlled by factors such as slope gradient, soil depth, terrain configuration, and the demand for fuel wood. Most of the cropland is found in the plains constituting the middle and lower reaches of the catchment, although most of the slopes on the mountainous terrain to the north are also under intensive cultivation. A considerable proportion of the cropland expansion that took place between 1957 and 1986 appears to have taken place along the valley rims in the middle and lower reaches of the watershed. The steep ridges along the eastern edges of the catchment are left out of crop production primarily because of the rugged terrain, very steep slopes, and shallow soils. The presence of the patches of natural vegetation along the western and northern slopes is also attributed to the same factors (Tegene, 2002). The expansion of agricultural land at the expense of other lands indicated increased pressure on agricultural land latter reduces the productivity due to its resources exploitation, unsustainable cultivation and soil fertility decline. These days cultivation through conversion of grazing land or bush lands to cultivated lands is due to high population pressure (Abate and Lemenih, 2014).
Does Land Tenure Insecurity Drive Deforestation in the BrazilianAmazon?
The purpose of this paper is to highlight the detrimental impact of land tenure insecurity on deforestation in the BrazilianAmazon. It is related to recent controversies about the detrimental impact of land laws on deforestation, which seem to legitimize land encroachments. The latter is mainly the result of land tenure insecurity which is a key characteristic of this region and results from a long history of interactions between rural social unrest and land reforms or land laws. A simple model is developed where strategic interactions between farmers lead to excessive deforestation. One of the empirical implications of the model is a positive relationship between land tenure insecurity and the extent of deforestation. The latter is tested on data from a panel of BrazilianAmazon municipalities. The negative effect of land tenure insecurity proxied by the number of squatters on deforestation is not rejected when estimations are controlled for the possible endogeneity of squatters. One of the main policy implications is that ex post legalizations of settlements must be accompanied by the enforcement of environmental obligations.
the agrarian structure of BrazilianAmazon and its impact on deforestation since 1970. The figures show that most of the agricultural settlement of the region took place in the last 30 years. In 1970, the area under private farms represented only 12% and deforestation 7% of the geographic of the region. In the same years, almost 50% of the farm holders were squatters (posseiros) which had no legal land tenure whatsoever, while farm holders with property titles represented less than 27% of establishments. As the settlement of the region progressed the share of squatters naturally declined but, in 1996, they still represented 26% while proprietors represented 64% of farmers (Map 1). Regionally, squatters were quite important in the states of Maranhão, Amazonas and Acre where they represented, respectively, 43%, 33% and 30%. Figures in Table 1 also show that throughout the whole period landownership concentration was extreme with values of Gini coefficient close to 0.9.
This paper assessed the dynamics in the landuse/landcover (LULC) within patterns of the landuse/landcover (LULC) in Calabar metropolis. The ther- mal imageries for 2002, 2006, 2008, 2010, 2012, 2014 and 2016 were obtained and processed using remote sensing and Arc GIS software package in order to determine the changes that have occurred in the LULC in study area. The re- sult of the LULC thematic maps overall accuracies was computed above 80 percent, which indicates an almost perfect agreement. The findings of this study reveal that, LULC classes by the year 2016 have assumed different di- mensions of change from the sizes of their previous sizes in comparison to their current sizes. Land-use pattern changes in the study area were characte- rized by an increase in the built up class, waterbody (though with a slightly negative change from 2010 to 2012) and a predominant negative trend in dense vegetation and bare land classes; thus, indicating that the future changing trends will pose a depleting threat to the overall LULC. This study has shown that, the changing landuse pattern of the area is capable affecting certain cha- racteristics of the environment such as surface temperature. The study re- commends that effort should be made by the government to increase urban vegetation around city centers and outliers by embarking on reforestation.
Assuming that the average rate of population increase in the whole watershed remained constant, the population of the studied area would have been around half of its current size about 30 years ago i.e. 1973. At this point it might be worthwhile to mention the controversial issues related to the impact of population growth on resource degradation as stated by Hurni (1996). “In the absence of sound implementation of environmental policy, unchecked population growth is usually considered the primary culprit in resource degradation”. However, others (Tiffen et al., 1994, quoted in Hurni, 1996) argue that the reverse is true, i.e. more people, less degradation. Because of the reasons mentioned above and others, population in one way or another has been contributing to the dramatic landuse/landcover changes seen in the area. Beyond the reduced possibility for cultivated land-expansion and severe shortage of land, one of the immediate impacts of the thinning and destruction of the shrub and bush land is shortage of fuel wood and construction materials for the local community. This condition forces farmers not only to travel very long distances to collect wood, but also to increasingly burn crop residues and organic manure for cooking and heating.
The conference will be focused on landuse, landscape and countryside in Europe. However, any papers not concerning Europe are appreciated too. A paper has to fit one of the topics above. Participants can either hold an oral presentation of their papers, or display their posters. Posters will be displayed during the first two days of the conference; however, no special poster-session is intended. Of course, it is possible to participate in the conference without presenting a paper or displaying a poster.
41 overlapped patches introduces too much redundant computations, thus, severely restricting the actual utility of the method for large-scale landcover classification (Fu et al. 2017, Maggiori et al. 2017). Recent research has shifted the focus on patch-based CNN for landcover classification towards designing pixel-level architectures for pixel labelling using VFSR remotely sensed imagery (Volpi and Tuia 2017). Particularly, the fully convolutional networks (FCN) and their extensions (Paisitkriangkrai et al. 2016, Wang et al. 2017, Zhao et al. 2017) were proposed for the task of semantic segmentation to classify a set of low-level landcover semantics, such as building, grassland and cars (Liu et al. 2017). These FCN-based methods involve convolution and down-sampling together with subsequent up-sampling to maintain the resolution of output map to be the same as the original input image, where the class likelihoods for an entire image were produced for pixel-wise semantic segmentation (Chen et al. 2016). However, the convolution utilises the neighbourhood information as context, and there is a trade-off between strong down-sampling, which allows the network to see a large context, but loses fine spatial details for precise boundary delineation (Marmanis et al. 2018). Besides, the up-sampling layers are performed in a sense of interpolation at the pixel level that tends to over-smooth the object with insufficient spatial information during the inference stage (Liu et al. 2017). As a consequence, the FCN models still face challenges in pixel-wise dense labelling.
Scholars from different disciplines have studied LULC change in different regions around the world. These studies mostly have been conducted at a sub-national scale, from villages to provinces and to local regions. Nowadays, there are some studies that link social surveys with other information (e.g., hydrology, soil, climate, vegetation) and new techniques such as GIS, remote sensing, global position system (GPS) to assess pattern – process relations for assessing the causes and consequences of LULC change. These studies mostly occur in the developing world such as in the Amazon Basin (e.g., Moran et al., 1994; Skole et al., 1994; Walsh et al., 2001), India (e.g., Rosenzweig, 2001), Nepal (e.g., Shivakoti and Axinn, 1999), China (e.g., Liu et al., 2001), and Thailand (e.g., Entwisle et al., 1998; Rindfuss and Stern, 1998; Walsh et al., 1999). These studies started in different areas and in different environments, but they have some common objectives (e.g., relationships between population and environment) and methodologies (e.g., social survey, GIS and remote sensing). Findings from different locations and characteristics of areas show that LULC change is influenced by different variables that generally represent the social, biophysical, and geographical domains. In northeastern of Thailand, soil, hydrology and climate are different from that of the Amazon Basin. Policies and plans from inside and outside the region influence how farmers use their land. In China, for instance, the One Child Policy does not affect native people in Wolong Nature Reserve, so there is increased population pressure in this area that causes more land fragmentation (Liu et al., 2001).
study area were mapped mainly based on 2004 digital imagery and were field checked. It provides a good ground truth not only for the 2004 imagery, but must also be part of the reference for interpreting 1988, 1972 and 1954 orthoimages. The interpretation was carried out with respect to the forest canopy patterns that appeared on the imagery, relationships with other land covers, and DEM derived attributes such as aspects and slope declivity. For example, cool temperate rainforest appears as closed canopy in or adjacent wet forest and distributes along valleys with above 300 m elevation, especially where aspect provides the shadiest local climate. The most daunting photo interpretation challenge refers to the black and white orthoimages. Stereo models were built using stereo pairs of aerial photograph to support the interpretation. This 3D view of terrain
Abstract: The present study focuses the rate and pattern of landuse/landcover change during 1988 to 2008 of Hisar district using remote sensing and GIS techniques. Landuse / landcover mapping of Hisar district were carried out using Landsat TM (1988 & 1998) and IRS-P6 (Resourcesat-I) LISS-III (2008) satellite data. On screen visual interpretation technique of satellite imagery for identification and delineation of landuse/landcover classes was employed using on screen digitization technique. The built-up area in the year 1988 was commuted to be 6776.0 ha (1.68%). It was increased 2.55% in 1998 and 4.15% in 2008 of the total geographical area. It was noticed that the agriculture area were also increased. It was 79.47% in 1988, 80.4% in 1998 and 83.7% in 2008 of the total geographical area due to decrease the sandy waste. It is also evident that vegetation has decreased 6.67% in 1988 to 4.30% in 2008. Water bodies and wasteland were also observed negative changes from 1988 to 2008. The study gives a fairly good understanding of landuse/landcover changes for a period of two decades, which in turn will be very helpful for local administrative bodies, decision makers and regional planners.
cially historical maps, may contain errors, inconsistencies and inaccuracies. Some of them might be completely wrong for political and military intelligence reasons of the given time (Draganits, 2008). For example, military maps are limited by the fact that they were created for specific military purposes, and by the methods used in elaborating them at the time when they were made. Therefore, in close cooperation with historians and cartographers, re- searchers should carefully check the maps for their contents, age, details of their production, cartographic parameters, and historical circumstances, etc., to exclude possible pitfalls as much as possible. In addition, there are other four constrains using historical maps to recon- struct landcover as follows: 1) The mapping criteria of the past is not totally the same with today’s and it is often difficult to find the historical mapping instructions. 2) An obstacle in the previous studies using historical maps is that they have geometrical irregularities com- pared to modern maps due to surveyor’s purpose and the technical factors. Unless this is taken into consideration, and without correctly transforming the maps, it is hard to make accurate spatial analysis of land-cover change. 3) Each map shows its own landcover classes mainly based on its purpose and criteria. Thus, researchers must first produce a map series with unified contents. 4) Some classes of land covers have their own certain bounda- ries (e.g., lakes, rivers, hamlets, and wetlands), and the boundaries of forest are difficult to be distinguished from those of arable land, for example. So it is very difficult to extract and digitize the spatial explicit forest and arable data.
Agricultural activities in Centre Region is the primary driver of deforestation LULC changes. On the field, a wide range of agricultural activities was observed and included crop cultivation, grazing, agroforestry, forestry, and fish farming. There are almost twenty different varieties of crops (sweet potato, cassava, cocoyam, yam, banana plantain, maize, pineapple, peanut, pepper), grown through slash and burn for subsistence purposes. Commercial or rental crops grown included cocoa, coffee, sugar cane, oil palm and income generating crops were pineapples, bananas, oil palms, and tomatoes. Subsistence shifting cultivation took the form extensive farming and was characterized by fallow periods of more than five years. Since 1990, fallow periods have reduced to between 1-3 years due to demographic pressure and urbanization. Commercial or rental agriculture is intensive but occupied large expanses of land in the region, even in the case of family farms. The cultivation of market oriented food crops was favoured by the economic crisis of the early 1980s and are cultivated extensively and intensively. Small farmers seldom cultivate more than 1ha while elites acquire farmlands of more than 10 or 200ha. There are also common initiative groups of farmers who acquire large parcels of land for the production of mainly market oriented crops. As concerns industrial farming, a few agro industries such as SOCAPALM (oil palm) and SOSUCAM (Sugar cane) exist and possess largest land holdings. In 2008, an agro pole for Banana was created in Mpagne found in the northern part of Mbam & Inoubou Division (figure 7). In addition to the development of agriculture, one can notice the clearing of the land for wood extraction for commercial, fuelwood purposes and charcoal production (mainly domestic uses) to be sold in the town (Yaoundé above all) since 1990. The region is suitable for traditional logging with carpenters (residing in the city of Yaoundé) who uses saw for cutting trees and wood for domestic and commercial uses. Industrial logging is also observed mainly in forest management units and some community forest. Wood industry and first transformations are found around the city of Yaoundé coupled with hundreds of carpenters working to produce household furniture for the city dwellers. These are factors considered as direct or proximate causes of deforestation and LULC changes in this region.
Water is essential to people and the largest available source of fresh water lies underground. Increased demands for water have stimulated exploration of underground water resources. Water resources get polluted due to rapid industrialization, advancement in agricultural techniques, increasing population and other adverse impacts of environments. All these factors may result in changing the hydrological cycle. The urban environmental quality always depends on the usage of land. The quality of the environment is determined by studying the landuse features and their impacts are analyzed. In present study an attempt is made to evaluate the impact of landuse / landcover on ground water quality of Zone VII under Greater Hyderabad Municipal Corporation (GHMC) area. Various thematic maps are prepared from the toposheet on 1:50000 scale using ArcGIS Software. The landuse / landcover map of the study area is prepared from the linearly enhanced fused data of IRS-1D PAN and LISS-III satellite imagery by using Visual Interpretation Techniques.