Hasil untuk "Land use"

R. Houghton, J. House, J. Pongratz et al.

Abstract. The net flux of carbon from land use and land-cover change (LULCC) accounted for 12.5% of anthropogenic carbon emissions from 1990 to 2010. This net flux is the most uncertain term in the global carbon budget, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and not all analyses consider the same types of management activities. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. In addition to deforestation, all analyses considered changes in the area of agricultural lands (croplands and pastures). Some considered, also, forest management (wood harvest, shifting cultivation). None included emissions from the degradation of tropical peatlands. Means and standard deviations across the thirteen model estimates of annual emissions for the 1980s and 1990s, respectively, are 1.14 ± 0.23 and 1.12 ± 0.25 Pg C yr−1 (1 Pg = 1015 g carbon). Four studies also considered the period 2000–2009, and the mean and standard deviations across these four for the three decades are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr−1. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr−1. The standard deviations across model means shown here are smaller than previous estimates of uncertainty as they do not account for the errors that result from data uncertainty and from an incomplete understanding of all the processes affecting the net flux of carbon from LULCC. Although these errors have not been systematically evaluated, based on partial analyses available in the literature and expert opinion, they are estimated to be on the order of ± 0.5 Pg C yr−1.

G. Hoek, R. Beelen, K. Hoogh et al.

R. Fell, J. Corominas, C. Bonnard et al.

C. Kremen, N. Williams, M. Aizen et al.

Sean L. Tuck, C. Winqvist, F. Mota et al.

Summary The benefits of organic farming to biodiversity in agricultural landscapes continue to be hotly debated, emphasizing the importance of precisely quantifying the effect of organic vs. conventional farming. We conducted an updated hierarchical meta‐analysis of studies that compared biodiversity under organic and conventional farming methods, measured as species richness. We calculated effect sizes for 184 observations garnered from 94 studies, and for each study, we obtained three standardized measures reflecting land‐use intensity. We investigated the stability of effect sizes through time, publication bias due to the ‘file drawer’ problem, and consider whether the current literature is representative of global organic farming patterns. On average, organic farming increased species richness by about 30%. This result has been robust over the last 30 years of published studies and shows no sign of diminishing. Organic farming had a greater effect on biodiversity as the percentage of the landscape consisting of arable fields increased, that is, it is higher in intensively farmed regions. The average effect size and the response to agricultural intensification depend on taxonomic group, functional group and crop type. There is some evidence for publication bias in the literature; however, our results are robust to its impact. Current studies are heavily biased towards northern and western Europe and North America, while other regions with large areas of organic farming remain poorly investigated. Synthesis and applications. Our analysis affirms that organic farming has large positive effects on biodiversity compared with conventional farming, but that the effect size varies with the organism group and crop studied, and is greater in landscapes with higher land‐use intensity. Decisions about where to site organic farms to maximize biodiversity will, however, depend on the costs as well as the potential benefits. Current studies have been heavily biased towards agricultural systems in the developed world. We recommend that future studies pay greater attention to other regions, in particular, areas with tropical, subtropical and Mediterranean climates, in which very few studies have been conducted.

M. Rodell, P. Houser, U. Jambor et al.

R. Blair

P. Waddell

J. Olesen, M. Bindi

L. Frank, J. Sallis, T. Conway et al.

T. Rudel, O. Coomes, E. Moran et al.

J. Vliet, H. Groot, P. Rietveld et al.

I. Hegazy, Mosbeh R. Kaloop

Abstract Urban growth is a worldwide phenomenon but the rate of urbanization is very fast in developing country like Egypt. It is mainly driven by unorganized expansion, increased immigration, rapidly increasing population. In this context, land use and land cover change are considered one of the central components in current strategies for managing natural resources and monitoring environmental changes. In Egypt, urban growth has brought serious losses of agricultural land and water bodies. Urban growth is responsible for a variety of urban environmental issues like decreased air quality, increased runoff and subsequent flooding, increased local temperature, deterioration of water quality, etc. Egypt possessed a number of fast growing cities. Mansoura and Talkha cities in Daqahlia governorate are expanding rapidly with varying growth rates and patterns. In this context, geospatial technologies and remote sensing methodology provide essential tools which can be applied in the analysis of land use change detection. This paper is an attempt to assess the land use change detection by using GIS in Mansoura and Talkha from 1985 to 2010. Change detection analysis shows that built-up area has been increased from 28 to 255 km 2 by more than 30% and agricultural land reduced by 33%. Future prediction is done by using the Markov chain analysis. Information on urban growth, land use and land cover change study is very useful to local government and urban planners for the betterment of future plans of sustainable development of the city.

Amna Butt, R. Shabbir, S. Ahmad et al.

Abstract Evaluation of watersheds and development of a management strategy require accurate measurement of the past and present land cover/land use parameters as changes observed in these parameters determine the hydrological and ecological processes taking place in a watershed. This study applied supervised classification-maximum likelihood algorithm in ERDAS imagine to detect land cover/land use changes observed in Simly watershed, Pakistan using multispectral satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 respectively. The watershed was classified into five major land cover/use classes viz. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water. Resultant land cover/land use and overlay maps generated in ArcGIS 10 indicated a significant shift from Vegetation and Water cover to Agriculture, Bare soil/rock and Settlements cover, which shrank by 38.2% and 74.3% respectively. These land cover/use transformations posed a serious threat to watershed resources. Hence, proper management of the watershed is required or else these resources will soon be lost and no longer be able to play their role in socio-economic development of the area.

F. Tubiello, M. Salvatore, A. Ferrara et al.

David Kipping

Approximately two-thirds of the Earth, the only known inhabited planet, is covered in ocean. Why not 0.01% or 99.99%? It has been previously suggested that this may represent a certain degree of fine-tuning, and thus perhaps observers are a-priori more likely to develop on those rare worlds with nearly equal land-ocean ratios, such as our own. In this work, we take the single datum of the Earth and then use Bayesian inference to compare four models for the probability distribution of a planet becoming inhabited by observers as a function of land-fraction, $f$, which we classify as i) land-centric ii) ocean-centric iii) equi-centric and iv) indifference. We find that no model is strongly favoured over the others, but that 1) the land-centric model is disfavoured over all others, and, 2) the equi-centric model is favoured over all competitors. Further, we show that more extreme models with heavy tail-weighting are strongly disfavoured even when conditioned upon the Earth alone. For example, a land-centric model where the median planet has $f=0.82$ (or greater) is in strong tension with our existence. Finally, we consider the potential addition of more data via Mars or exoplanets. Should paleo-Mars have once harboured life and had $f<0.20$, then this would strongly favour the ocean-centric model for life, over a land-centric hypothesis. We show that strong evidence for/against the equi-centric model versus its competitors would likely require at least a dozen inhabited exoplanets, offering a well-motivated sample size for future experiments.

C. Wolff, F. Bade, C. M. Kraan

Abstract Managed retreat, the purposeful relocation of households and assets to reduce flood risk, is gaining recognition as an essential adaptation strategy under intensifying climate change. Although often contested and perceived as socially or politically unacceptable, managed retreat holds potential to enhance the long‐term resilience of at‐risk communities. In Europe, however, it remains comparatively underexplored, with only a handful of European managed retreat cases that have been reported on in the academic literature. Here, we present a data set of European managed retreat cases, based on a multilingual review of academic and gray literature, as well as media articles. We found 44 implemented or planned cases of managed retreat across the continent, spanning 11 countries, ranging from the relocation of individual assets to more than 1,500 households. Through a cross‐case analysis, we identify five key factors that influence the process and outcomes of managed retreat projects: the compensation offered, the timing of the project, the engagement of the affected community, the leadership taken by the government, and the post‐relocation land use. Our analysis demonstrates that, although managed retreat remains less common than engineered protection measures, it is more prevalent in Europe than previously assumed and is already being practiced in varied forms. By uncovering common challenges and enabling conditions, this study offers transferable insights for advancing more anticipatory and strategically designed managed retreat initiatives, both within Europe and beyond.

Laura D. Pachón-Acuña, Jorge López-Rebollo, Junior A. Calvo-Montañez et al.

Accurate runoff estimation in ungauged basins is critical for water resource management but often relies on static parameters like the runoff threshold (<i>P</i>₀), derived from the Soil Conservation Service Curve Number method, which fail to capture spatiotemporal soil moisture variability. This study proposes an automated methodology utilising Google Earth Engine to dynamically adjust <i>P</i>₀ by integrating daily soil moisture data from SMAP L4, land cover from MODIS, and precipitation from GSMaP. Unlike traditional approaches that use antecedent precipitation as a proxy, this method classifies moisture conditions using historical percentiles to update the threshold daily. The methodology was validated in two sub-basins within the Guadiana River basin (Spain). The results highlight a stark contrast between methods: while static regulatory values remained invariant (36 and 48 mm), the proposed dynamic model revealed significant fluctuations, with <i>P</i>₀ values ranging from over 50 mm in dry periods down to less than 14 mm during saturation. Conversely, the proposed dynamic method effectively captures real-time soil saturation, exhibiting adaptability with reductions in <i>P</i>₀ of up to 72% immediately following rainfall events. This satellite-based approach provides a scalable, physically consistent alternative for assessing runoff potential in data-scarce regions, significantly enhancing the reliability of hydrological modelling compared to conventional regulatory standards.

P. Alexander, M. Rounsevell, C. Dislich et al.

Runsen Zhang, L. Pu, Jianguo Li et al.