Despite the knowledge hitherto gained, a comprehensive approach to study landcoverchange in the SE-Ecuadorian Andes over an extended period considering fragmentation, fire–pasture–bracken dynamics, and the study of proximate and local and non-local underlying drivers is still lacking.
Landsat data, with its spatial resolution of 30–60 m, longer term (since 1972), and free availability, is especially suitable for landcoverchange detection, as already demonstrated elsewhere in a variety of studies [49–53]. Due to the complex terrain and the difficult weather conditions in the study area, an accurate change detection analysis is only possible if an adequate atmospheric and topographic correction is applied to the satellite images. Additionally, other pre-processing steps like (i) image co-registration; (ii) cloud/cloud-shadow detection; and (iii) intercalibration are also necessary to produce accurate results. Hence, a framework to conduct the required processing steps with a minimum of manual work is needed to fully exploit this valuable source of information efficiently.
A steadily growing population, changing consumption habits and biogeophysical drivers, e.g. soil nutrient depletion, are the main pressure forces of continuous forest conversion into agricultural land in the South American tropics (Riebsame et al., 1994; Barbier, 2004). Consequently, the decrease in areas with natural forest is a major problem in South America with Ecuador having the highest deforestation rate at 1.8% per year (Mosandl et al., 2008; FAO, 2011). At the same time, agriculture was intensified in many land-use systems along with substitution of manual labor by mechanized power as well as organic manure and natural pest management by agrochemical use (Giller et al., 1997). Nonetheless, agricultural land expansion in South America was even correlated with a decrease in gross domestic product per capita which was attributed to the failure of governmental policies to effectively target a sustainable management of natural resources (Barbier, 2004). Therefore, the large expansion of the total agricultural area can be closely associated with stagnating productivity (Southgate and Whitaker, 1992). As a consequence, long-term productivity of tropical agroecosystems is at stake and has raised awareness for the need of management strategies which maintain and protect soil resources (Chander et al., 1997).
In Gamo Gofa, the 2005 data of land use patterns (LUPs) were partitioned as cultivated (28%), grazing (14%), forest and bush (16%), fertile/cultivable (16%), not cultivable (12%), and others (14%) land use systems (Table 1, Figure 2). Similarly, the 2012 data of LUPs were partitioned as cultivated (35%), grazing (14%), forest and bush cover (16%), fertile/cultivable (10%), not cultivable (11%), and others (14%) (Table 1, Figure 2). Table 3 shows the correlation between land use variables. Since 2005 and 2012 data were used, the forest and bush land area positively correlated, 0.83045 (p-value <0.003) with fertile land area. After seven years later we observed same correlation in 2012. On other side the 2005 and 2012 data of cropland conversion was positively (r= 0.2694, 0.75734*) correlated with fertile land, respectively. The impressing result of the correlation between cropland and fertile land in 2012 has been improved from 0.2694 to 0.75734 (p-value <0.012) in seven year time period. This at least in part, and explains the significant reduction in cultivable land and expansion of cropland in the study area. These changes were being driven by population pressure (Table 3 and 4) and development programs, which is in line with 10 but, in comparison to other drivers, it is unlikely that climate change will affect land use change significantly. Thus, this study indicated that most of cultivable land was converted into cropland in all woredas (Table 1, Figure 2), and the increase in relative human and cattle population (Table 3,
Anthropogenic denudation has increased exponentially over the last decades and now exceeds natural denudation by several orders of magnitude (Cendrero et al., 2006). Ac- cordingly, human activity has also become a major factor in increasing landslide susceptibility. It affects the inten- sity and frequency of landsliding globally through global warming and enhanced precipitation (Crozier, 2010), and lo- cally especially through landcover changes (Glade, 2003) and constructions (Dikau et al., 1996). Deforestation and roads in particular seem to enhance the likelihood of mass movement occurrence (Glade, 2003; Goetz et al., 2011). In- creased landslide activity in the vicinity of populated areas and transportation infrastructure may lead to human catas- trophes (Fassin and Vasquez, 2005) and vast economic dam- age (Vranken et al., 2013). Tropical mountain ecosystems are especially sensitive and respond quickly to any change (Vanacker et al., 2007). In the Andes of southernEcuador, ac- celerated human pressure has increased sediment yield man- ifold (Molina et al., 2008), and areas adjacent to highways have proved to be most susceptible to landsliding (Muen- chow et al., 2012).
Local-scale carbon stock assessments have been conducted for forests in cities (Jim and Chen, 2009) but are not comparable with this research for several reasons. Tree cover, density and forest maturity affect carbon sequestration and storage rates. Sequestration rates decrease as forests mature due to a higher proportion of dead trees and large diameter trees. Natural forest stands typically have higher tree cover than urban forests and thus store and sequester more carbon annually, but the reverse is true on a per unit tree basis due to higher growth rates as a result of lower tree density (Nowak and Crane, 2002).
When we controlled for area, there were no noticeable change in diversity at the lower eleva- tion bands (i.e., 0 m— 1957m), but there was increasing loss of diversity between 1958 m— 2347 m and considerable loss between 2352 m —2744 m. We envisage two explanations for this observation. First, species range contractions and extinctions are more likely to occur at higher elevations, since there is a physical constraint on elevation at mountaintops and plants can no longer track shifting climatic conditions [ 27 ]. In the tropics, species are more likely to respond to climate –driven temperature change by shifting their range by elevation through upslope mi- gration, rather than latitude [ 29 ]. This is particularly true for high elevation endemics, such as plants found in ericoid thickets that occupy a narrow climate space [ 16 , 64 ]. Alternatively, a de- crease in richness at high elevation may be linked to the inability of plants to migrate to suitable climatic conditions since current and future suitable habitats do not overlap (i.e., range-shift gaps sensu Colwell et al. (2008) [ 27 ], which is an acute problem for low–and–high–elevation plant species [ 17 ]. Our study does not address these questions, but they are areas of continuing inquiry. What seems most clear from our study is that high elevation plants will contract their geographic range in response to the combined effects of climate and landcoverchange. The likely consequence of either alternative explanation may be that Madagascar’s high elevation species, particularly endemics, will become increasingly threatened into the future [ 65 ].
Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-use land-coverchange (LUCC) studies. This is due to the increased awareness by governments, NGOs and Indus- try that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This pa- per examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected stud- ies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important eve- ryday applications.
The soil of plantation, grassland and cultivated land are found to be more acidic by 2.2, 2, and 1 unit respectively than the soil of natural forest (Table 1). The relatively lower pH value of plantation forest soil is probably due to the uptake of basic cations in tree biomass and the acidic nature of the litter of the tree species after decomposition. This suggests the need to take precaution in tree species selection for plantation forest because trees differ in litter quality and so in restoring soil fertility. As Table 1 shows, the conversion of natural forest into other land-use/cover has resulted in a decreased soil pH value. This decreasing trend of soil pH suggests a systematic removal of bases by annual crops (Gebrelibanos and Assen, 2013). The relatively high content of available phosphorus in the forest soil next to cultivated land (application of phosphorus fertilizer) could be due to high content of soil organic matter resulting in the release of organic phosphorus. Probably for this reason, available P is strongly associated with soil organic matter. The relatively low exchangeable Ca 2+ and Mg 2+ in cultivated and grassland soil is attributed to their continuous removal with crop harvest and cattle grazing. As the level of soil organic matter is low to withhold release of nutrients, soil erosion is also responsible for the low content of exchangeable Ca 2+ and Mg 2+ in cultivated soil. Thus, the degradation of organic matter had left the soil of cultivated land with low CEC. Soil CEC is important for maintaining soil fertility as it influences the total quantity of nutrients available to plants at the exchange site (Gebrelibanos and Assen, 2013).
precipitation, wet season onset, and number of wet days for each hydrological year. To investigate the intraregional variability of precipitation (e.g. Eck et al. 2018), the study region was classified into five distinct groups through a K-means test, which determined the optimal number of groups and maximized within group precipitation characteristic similarities and outside group differences in precipitation characteristics. All groups were elongated with their axis aligned parallel to the Andes with Groups 1 and 2 in the north and east and Groups 3, 4, and 5 in the south and west closest to the Pacific Ocean. Groups The Kruskal-Wallis H test, suitable for three or more groups of non-parametric data, and a post-hoc test for the Kruskal-Wallis H (Kruskal and Wallis 1952) tested for significant differences among ENSO phases in each of the groups.
The analysis steps to model and identify changes between 2013 and 2017 from bi-temporal imagery are shown in Figure 2. The process was as follows. First, a series of unsupervised classifications were undertaken using a minimum distance approach with percentage change threshold of 2% and a maximum of 10 iterations. The aim of these was to identify an appropriate classification scheme for this study area. This determined a set of seven classes that could be reliably identified in each image which were then manually labeled. Then, class-to-class training data were selected and labeled automatically with the change classes from the 2013 and 2017 unsupervised classifications. For each class-to-class pair, including no change class pairs, 100 sample locations were selected from within homogenous areas and subjected to a 70/30 training and validation split. From these, the start and end coordinates in six-dimensional image space from the 2013 and 2017 coupled images were determined for each sample and three change vector variables were calculated: magnitude, spectral direction and spectral angle. Thus, the reference samples were labeled with the from-to change classes and contained 15 predictor variables of the six spectral bands for each year and the three derived vector measures. These were used input parameters to a Random Forest classifier to create a predictive model of landcoverchange. The model was then applied to the combined bi-temporal Landsat images to predict change areas. Finally, regional comparisons were undertaken to compare the magnitude and direction of change in different landscape contexts.
In South and Central America, beef cattle ranching has been the dominant driver of forest clearance (e.g. Grainger 1993; Fearnside 2005; Gibbs et al. 2010). During the 1960s, 70s and 80s, road building and financial incentives from the Brazilian government encouraged deforestation of the Amazon to create pasture and cattle ranches (Carvalho et al. 2002; Fearnside 2005). This process often involves clearance of the forest followed by burning to remove any residual trees. Until the 1990s, Brazilian beef was usually sold domestically but in the early 2000s, international demand for Brazilian beef partly drove a spike in deforestation rates between 2002 and 2004 (Nepstad et al. 2006). During the 1970s, an oil embargo prompted rapid expansion of biofuel crop growth in South America, specifically the growth of sugar cane to produce ethanol. In 1977, the Brazilian government mandated that all gasoline must be blended with ethanol. This mandate is still in place and the current minimum blend level is set at 27% ethanol. As well as directly driving forest clearance, the growth of bioenergy crops can indirectly lead to deforestation if it displaces food production which then moves onto forested land.
This study shows an integrated GIS and remote sensing approach to detect and analyze changes in LULC over Sidon district. The Landsat satellite data for the study period of 1985 and 2015 were used to generate landcover maps through a maximum likelihood supervised image classification algorithm. The remote sensing image classification result was able to shows the various per class changes of LULC. There was a rapid changing in built up areas and agricultural areas as well which saw the size of both LULC classes increased significantly. In Lebanon there has been lacking in the land use and landcoverchange study or research. The previous study was dated which cover the period between 1963 and 1998. In this study, the research covers the period from 1985 up to 2015 and represent the more recent development in LULC changes in modern Lebanon. The result from both research indicates that the conflict period brings decline in agricultural areas, impacting the economic aspect of agro trading and destruction of property and development. Yet, after the conflicts ended, both period saw significant increase in urbanisation and development activities at national or local scale. Land use change result provided an important spatio-temporal information of land use and landcover changes. It also provide the possibility to understand the influence and dynamics of LULC change supported by a set of drivers. The result of this research can provide important information for Sidon’s decision makers for better land use monitoring.
Abstract: This research was carried out to digitize and interpret the land use and landcoverpatterns of the southern Haripur tehsil with remote sensing and Geographical Information System techniques. Interpretation was done with the topographical map of Haripur district and online images of Landsat 7, 2012, Google earth 2016 and zoom level image of Landsat 8, 2015 (Urban unit, 2015). Global Positioning System (GPS) coordinates were collected via field work for ground verification of the features. For statistical analysis, Arc GIS 9.3 and Excel sheet 2010 was used. From the analysis it was found that the maximum agricultural area was 265.47 sq.km. The other two classes i.e. railway station and river bars covered minimum area of 0.01 sq.km. In this paper it was found the maximum area was covered by the agricultural land that was 265.47sq.km. The two other classes i.e. railway stations and river bars which covered a minimum area (0.01 sq.km). An area of 46.02 sq.km was without cover.
Mosaic plots were proposed by Hartigan and Kleiner  and extended by Friendly . In these, the significance of the interactions between column and row factors are indicated by the shading, in which the standardised residuals of a log-linear model are indicated by the colour and outline of the mosaic tiles. The mosaic plot in Figure 3 has axes for the different regions being compared and the landcoverchange types. The size of the plot tiles is proportionate to the landcover areas (counts in the contingency tables). Their shading indicates whether the combinations of groups, regions, classes etc. are less or greater than expected under a model of proportionality. In the examples below, tiles shaded deep blue show interactions that are significantly higher than would be expected (i.e., corresponding to combinations of change and region whose standardized residuals are greater than +4), when compared to a model of proportionally equal levels of change. Tiles shaded deep red correspond to residuals less than ´4 indicating significantly lower frequencies than would be expected when compared to the model. The standardized Pearson residuals measure the deviation of each tile from independence. From Figure 3, statements can be extracted under the assumption of proportionally equal levels of change (loss and gain) for each landcover class and region. In this case, the mosaic plot indicates that the gains to Urban in Region 3 are much greater than expected.
CESM output, however, with large biases over the Tibetan Plateau; this high-altitude region is therefore excluded from subsequent analyses. Urban expansion shows strong impacts on the surface energy balance due to low albedo and the thermal properties of urban surface. Over urban areas, the decrease in latent heat flux and increase in sensible heat flux lead to higher surface air temperature and less humidity in the atmosphere. Larger urban warming effects are found during night and in summer, which can be attributed to the high heat capacity of urban areas. Urbanization does not exert a significant impact on the overall future temperature trend in China. However, at regional and local/urban scales, urbanization shows strong warming effects of up to 1.9°C, which is comparable to the greenhouse effects under RCP 4.5 scenarios. The impacts of urbanization on precipitation show a combined effect from changes in both local moisture and the large-scale atmospheric circulation. There is a decrease in atmospheric moisture over urban areas. The East Asian summer monsoon is strengthened in southern China, but the East winter monsoon is slightly weakened in the Yangtze River Delta.
Abstract. Land use and coverchange is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important ap- proach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of land use using re- mote sensing technology and analyzes historical space–time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source informa- tion and creates a convenient and efficient high-precision land use computer automatic classification method which meets the application requirements and is suitable for com- plex landform of the studied area. This work analyzes the histological space–time characteristics of land use and coverchange in the Qiantang River basin in 2001, 2007 and 2014, in order to (i) verify the feasibility of studying land use change with remote sensing technology, (ii) accurately un- derstand the change of land use and cover as well as histori- cal space–time evolution trend, (iii) provide a realistic basis for the sustainable development of the Qiantang River basin and (iv) provide a strong information support and new re- search method for optimizing the Qiantang River land use structure and achieving optimal allocation of land resources and scientific management.
cases where the class variations per unit area are high , greater number of categories may reduce the interpretation clarity . Also define the number of iterations and the convergence threhol limits . The process output is generated when either of these criteria is met . The specified maximum number of iterations was 30 and the convergence threshold was specified at 0.98.The generated outpur resembles the reference satellite image subset since the colour scheme option chosedn was approximate tru colour. In case the colour scheme option chosen was preudo colour , the generated outpur layer would appear in shades of grey . Image interpreation is easier with the approximate true colour option. Geo linking the satellite image subset with the generated output layer helps identify classes more accurately. For the software generated classes , type and colour definition is done using the keyboard after clicking on well defined land use class samples on the generated output layer.
Concerns about the availability of freshwater inflows have been increasing as many of the World’s estuaries are experiencing a decrease in freshwater input. The rate of freshwater inflows is highly influenced by land use and landcover (LULC) change, upstream water diversion for human uses, and the effects of climate change (Russell et al., 2006). These changes affect estuarine chemical and physical properties (Xu and Wu, 2006). Salinity is used as an indicator of estuary condition because it strongly affects estuaries’ productivity. Upstream water diversions and extractions may impact the natural salinity of estuaries both on an annual and seasonal basis. For example, increasing salinity and decreasing Mulinia coloradoensis population in the Colorado River Delta of Baja California, Mexico, were the result of extensive upstream diversion on the Colorado River (Rodriguez et al., 2001). In addition, reducing freshwater influx to the Colorado River Estuary has accelerated the growth of two main bivalve mollusk species in the northern Gulf of California by 6 to 28% (Schone et al., 2003).
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appropriate for the Amazon which represents a frontier setting, where the institutions necessary for profit maximisation may not be present or fully functional (Walker et al. 2004).
Research at both local and regional scales have found complex relationships, feedbacks and interactions between human (social, political, economic) and environmental systems (Deadman et al. 2004). One such relationship is that between road construction and deforestation, with this causal interaction driven by economic and cultural factors (Geist & Lambin 2002). Another common relationship is found between property rights and deforestation: Araujo et al. (2009) found that insecurity in property rights and social conflicts increased deforestation, because landowners needed to assert use of the land to avoid expropriation and squatters deforested in the hope that property rights will be awarded in the future. Differences in how models assume people will behave can exert large effects on model predictions, as shown by scenarios modelled by Dale et al. (1994) that compared alternative behaviours of farmers and their farming practices. In one scenario, it was assumed that farmers will make innovative use of their land and implement positive agro-forestry practices, leading to predictions that 40% of forested land would be cleared by farmers after 40 years. By contrast, when the model assumes that farmers will not use innovative practices and do not implement agro-forestry, the model predicted that 100% of the land would be deforested within just 10 years.