Hasil untuk "Land use"

P. Borrelli, D. Robinson, P. Panagos et al.

Significance We use the latest projections of climate and land use change to assess potential global soil erosion rates by water to address policy questions; working toward the goals of the United Nations working groups under the Inter-Governmental Technical Panel on Soils of the Global Soil Partnership. This effort will enable policy makers to explore erosion extent, identify possible hotspots, and work with stakeholders to mitigate impacts. In addition, we provide insight into the potential mitigating effects attributable to conservation agriculture and the need for more effective policy instruments for soil protection. Scientifically, the modeling framework presented adopts a series of methodological advances and standardized data to communicate with adjacent disciplines and move toward robust, reproducible, and open data science. Soil erosion is a major global soil degradation threat to land, freshwater, and oceans. Wind and water are the major drivers, with water erosion over land being the focus of this work; excluding gullying and river bank erosion. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity, is important both for policy makers engaged in land use decision-making and for earth-system modelers seeking to reduce uncertainty on global predictions. Here we predict future rates of erosion by modeling change in potential global soil erosion by water using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. Global predictions rely on a high spatial resolution Revised Universal Soil Loss Equation (RUSLE)-based semiempirical modeling approach (GloSEM). The baseline model (2015) predicts global potential soil erosion rates of 43−7+9.2 Pg yr−1, with current conservation agriculture (CA) practices estimated to reduce this by ∼5%. Our future scenarios suggest that socioeconomic developments impacting land use will either decrease (SSP1-RCP2.6–10%) or increase (SSP2-RCP4.5 +2%, SSP5-RCP8.5 +10%) water erosion by 2070. Climate projections, for all global dynamics scenarios, indicate a trend, moving toward a more vigorous hydrological cycle, which could increase global water erosion (+30 to +66%). Accepting some degrees of uncertainty, our findings provide insights into how possible future socioeconomic development will affect soil erosion by water using a globally consistent approach. This preliminary evidence seeks to inform efforts such as those of the United Nations to assess global soil erosion and inform decision makers developing national strategies for soil conservation.

Swapan Talukdar, P. Singha, Susanta Mahato et al.

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Xiaoping Liu, Xun Liang, Xia Li et al.

E. Lambin, P. Meyfroidt

S. Hasan, L. Zhen, M. G. Miah et al.

Abstract Changes in land use and ecosystem services influence each other and such changes have consequences for human wellbeing. In this paper, we review the research literature on how different types of ecosystem services are affected by LUC, and the consequences for human well-being. We begin with a review of the different types of ecosystem services. We examine the influence of LUC on provisioning ecosystem services due to mismatches between agricultural production and hydrological systems. We continue with a review of the impacts of LUC on supporting ecosystem services through the conversion of an ecosystem to cultivated land, and the resulting changes in soil properties and the hydrological balance. Next, We also discuss the regulating ecosystem services which are affected by LUC and alters water purification processes, as well as the effects on cultural ecosystem services. We conclude with a review of the valuation and quantification of the effects of LUC on the management of ecosystem services, and propose future research directions. Most of the research reveals a negative impact of LUC on ecosystem services, despite research gaps related to methods for valuing ecosystem services more accurately and for collecting social responses to the impacts of LUC on different ecosystem services.

G. Hurtt, L. Chini, R. Sahajpal et al.

Abstract. Human land-use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth surface, with consequences for climate and other ecosystem services. In the future, land-use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community is developing the next generation of advanced Earth System Models (ESMs) to estimate the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, are required as input for these models. Here we present results from the Land-use Harmonization 2 (LUH2) project, with the goal to smoothly connect updated historical reconstructions of land-use with new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land-use patterns, underlying land-use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds off a similar effort from CMIP5, and is now provided at higher resolution (0.25 × 0.25 degree), over a longer time domain (850–2100, with extensions to 2300), with more detail (including multiple crop and pasture types and associated management practices), using more input datasets (including Landsat remote sensing data), updated algorithms (wood harvest and shifting cultivation), and is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5, and are designed to enable new and improved estimates of the combined effects of land-use on the global carbon-climate system.

Amy Molotoks, Pete Smith, T. Dawson

In recent years, global hunger has begun to rise, returning to levels from a decade ago. Climate change is a key driver behind these recent rises and is one of the lead-ing causes of severe food crises. When coupled with population growth and land use change, future climate variability is predicted to have profound impacts on global food security. We examine future global impacts of climate variability, population, and land use change on food security to 2050, using the modeling framework FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation). The model uses national food balance sheets (FBS) to determine mean per capita calories, hence incorporating an assumption that minimum dietary energy requirements (MDER) remain constant. To account for climate variability, we use two Representative Concentration Pathway (RCP) scenarios from the Intergovernmental Panel on Climate Change (IPCC), alongside three Shared Socio-economic Pathway (SSP) scenarios incorporating land use and population change within the

Adator Stephanie Worlanyo, Jiangfeng Li

Mining has been a long-standing key player in economic development, employment, infrastructure, and supply of essential raw materials for society. It has served as a viable route to economic transformation in resource-rich countries like Australia, Canada, the United States, and parts of Africa. In this review, the impact of mining has been conceptualized into economic, environmental, and social impacts. While it is clear that mining has transformed many economies, it has also impacted negatively on the environment and, to some extent, society. Some of the negative impacts of mining are loss of vegetation cover, mass destruction of water bodies, loss of biodiversity, land-use changes and food insecurity, increased social vices and conflicts, high cost of living, and air pollution. However, reclamation has been a viable way of reducing the negative impacts of abandoned mine lands and ensure productive and efficient utilization of mine wastelands. Compaction, low or high pH, low water holding capacity, gullies, bulk density, deficiency of micro, and macronutrients are the major factors limiting the productivity of mine wastelands. A combination of physical, chemical, and biological restoration practices is ideal for restoring the mine soil productivity. While the physical method deals with earth-battering, thus putting the land back to shape, the chemical and biological methods include various amendments such as biochar, compost, synthetic fertilizers, synthetic chelates, shrubs, and grasses, and nanoparticles. A combination of these three restoration methods restores soil fertility, stimulates microbial growth, and facilitates early ecological succession. However, before embarking on reclamation, the particular post-mined land use should be clearly stated, such as conservations, forestry, agriculture, construction, intensive recreation, non-intensive recreation, and lake or pool through land suitability and selection analyses. This review has guiding significance and recommendations for mining and post-mined rehabilitation.

A. Vali, S. Comai, M. Matteucci

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

Ali Hassan Chughtai, Habibullah Abbasi, I. R. Karas

Abstract The assessment of land use land cover change is extremely important for understanding the relationship between humans and nature. The enormous changes at a regional scale and advancements in technology have encouraged researchers to gather more information. The remote sensing technology and GIS tools cooperatively have made it easier to monitor the changes in land use land cover (LULC) from past to present. This technology has unraveled the changes at the regional and global level and has also contributed tremendous benefits to the scientific community. A variety of change detection algorithms have been used in the history of remote sensing to detect changes at earth's surface and newer techniques are still in process. The data from remote sensing satellites are the primary sources that provide an opportunity to acquire information about LULC change in recent decades, which extensively use different algorithms according to the research needs. The selection of appropriate change detection method is highly recommended in every remote sensing project. This review paper begins with the traditional pre and post-classification change detection techniques related to LULC information at the regional level. Therefore, this paper evaluated the mostly used change detection method among all others to find remarkable results. Thus the review concludes the post-classification change detection method using maximum likelihood classifier (MLC) supervised classification is applicable in all cases. The comparative analysis was also performed in a selected region having multiple land features during review in which MLC results best in comparison to others. MLC is the most commonly used technique from the past till present that has achieved high accuracy in all regions comparatively to other techniques.

R. Fei, Ziyi Lin, J. Chunga

Abstract In recent years, China's agricultural land has decreased year by year. In order to alleviate the shortage of land and improve the efficiency of land use, the state has successively issued a number of relevant policies to encourage the transfer of agricultural land management rights. In this context, based on the data of 30 provinces from 2000 to 2017, this paper uses the global data envelopment method to establish indicators to evaluate the agricultural land efficiency, and uses the PSM method to construct a counterfactual framework to analyze the impact of the land transfer on agricultural land efficiency. This paper draws the following conclusions: 1) the national average value of land use efficiency is low, only 0.288, showing a decreasing trend from the east to the central and west. 2) The provinces that transfer land in are more efficient in land use than the ones with land transfer out, which further illustrates the seriousness of agricultural land tension in China. Also, this result testifies that increasing agricultural arable land can bring about a scale effect, which increases the output of unit land, while the outflow of land reduces the income of agricultural workers. This means that the land transfer in recent years is actually at the expense of agricultural operator’s interests. At last, this paper put some policy implications from the perspective of the land-use system reform, urban-rural transformation, and rural revitalization in China.

Joseph W. Millard, Charlotte L. Outhwaite, R. Kinnersley et al.

Pollinating species are in decline globally, with land use an important driver. However, most of the evidence on which these claims are made is patchy, based on studies with low taxonomic and geographic representativeness. Here, we model the effect of land-use type and intensity on global pollinator biodiversity, using a local-scale database covering 303 studies, 12,170 sites, and 4502 pollinating species. Relative to a primary vegetation baseline, we show that low levels of intensity can have beneficial effects on pollinator biodiversity. Within most anthropogenic land-use types however, increasing intensity is associated with significant reductions, particularly in urban (43% richness and 62% abundance reduction compared to the least intensive urban sites), and pasture (75% abundance reduction) areas. We further show that on cropland, the strongly negative response to intensity is restricted to tropical areas, and that the direction and magnitude of response differs among taxonomic groups. Our findings confirm widespread effects of land-use intensity on pollinators, most significantly in the tropics, where land use is predicted to change rapidly. Anthropogenic losses of animal pollinators threaten ecosystem functioning. Here the authors report a global analysis showing geographically varied yet widespread declines of pollinator diversity and abundance with land use intensification, particularly in tropical biomes.

Xue Xie, Bin Fang, Hanzeyu Xu et al.

Abstract China needs not only high land use efficiency, but also a high-quality ecological environment. Thus, clarifying interaction between land use efficiency and the ecosystem is crucial in accelerating China’s new urbanization. In this study, therefore, we measured China’s urban land use efficiency and ecosystem health to obtain the coordination effect between the two. Results showed that (1)from 2005 to 2018, China’s urban land use efficiency slightly increased, and presented a decreased spatial trend from “East-West-Center”. (2)The spatial pattern of ecosystem health was “high in the South and low in the North.” Areas with an excellent ecosystem are mainly distributed through Southwest China.(3)The study found that most areas of China were in the process of slightly unbalanced to barely balanced development and presented decreases from the East Coast and Northwest to the Inland. Finally, this paper puts forward policy recommendations according to each region’s coupling type.

Manikandan Kamaraj, S. Rangarajan

Human population growth, movement, and demand have a substantial impact on land use and land cover dynamics. Thematic maps of land use and land cover (LULC) serve as a reference for scrutinizing, source administration, and forecasting, making it easier to establish plans that balance preservation, competing uses, and growth compressions. This study aims to identify the changeover of land-use changes in the Bhavani basin for the two periods 2005 and 2015 and to forecast and establish potential land-use changes in the years 2025 and 2030 by using QGIS 2.18.24 version MOLUSCE plugin (MLP-ANN) model. The five criteria, such as DEM, slope, aspect, distance from the road, and distance from builtup, are used as spatial variable maps in the processes of learning in MLP-ANN to predict their influences on LULC between 2005 and 2010. It was found that DEM, distance from the road, and distance from the builtup have significant effects. The projected and accurate LULC maps for 2015 indicate a good level of accuracy, with an overall Kappa value of 0.69 and a percentage of the correctness of 76.28%. MLP-ANN is then used to forecast changes in LULC for the years 2025 and 2030, which shows a significant rise in cropland and builtup areas, by 20 km2 and 10 km2, respectively. The findings assist farmers and policymakers in developing optimal land use plans and better management techniques for the long-term development of natural resources.

E. Ellis

Human use of land has been transforming Earth's ecology for millennia. From hunting and foraging to burning the land to farming to industrial agriculture, increasingly intensive human use of land has reshaped global patterns of biodiversity, ecosystems, landscapes, and climate. This review examines recent evidence from archaeology, paleoecology, environmental history, and model-based reconstructions that reveal a planet largely transformed by land use over more than 10,000 years. Although land use has always sustained human societies, its ecological consequences are diverse and sometimes opposing, both degrading and enriching soils, shrinking wild habitats and shaping novel ones, causing extinctions of some species while propagating and domesticating others, and both emitting and absorbing the greenhouse gases that cause global climate change. By transforming Earth's ecology, land use has literally paved the way for the Anthropocene. Now, a better future depends on land use strategies that can effectively sustain people together with the rest of terrestrial nature on Earth's limited land.

Gebeyehu Abebe, Dodge Getachew, Alelgn Ewunetu

Mapping and quantifying the status of Land use/Land cover (LULC) changes and drivers of change are important for identifying vulnerable areas for change and designing sustainable ecosystem services. This study analyzed the status of LULC changes and key drivers of change for the last 30 years through a combination of remote sensing and GIS with the surveying of the local community understanding of LULC patterns and drivers in the Gubalafto district, Northeastern Ethiopia. Five major LULC types (cultivated and settlement, forest cover, grazing land, bush land and bare land) from Landsat images of 1986, 2000, and 2016 were mapped. The results demonstrated that cultivated and settlement constituted the most extensive type of LULC in the study area and increased by 9% extent. It also revealed that a substantial expansion of bush land and bare land areas during the past 30 years. On the other hand, LULC classes that has high environmental importance such as grazing land and forest cover have reduced drastically through time with expanding cultivated and settlement during the same period. The grazing land in 1986 was about 11.1% of the total study area, and it had decreased to 5.7% in 2016. In contrast, cultivated and settlement increased from 45.6% in 1986 to 49.5% in 2016. Bush land increased from 14.8 to 21% in the same period, while forest cover declined from 8.9 to 2% in the same period. The root causes for LULC changes in this particular area include population growth, land tenure insecurity, and common property rights, persistent poverty, climate change, and lack of public awareness. Therefore, the causes for LULC changes have to be controlled, and sustainable resources use is essential; else, these scarce natural resource bases will soon be lost and will no longer be able to play their contribution in sustainable ecosystem services. Forest cover and grazing lands declined rapidly. Fluctuating trends in cultivated and settlement, bush land and bare land. Population pressure and associated demand are the main causes behind LULC changes in the study area. Forest cover and grazing lands declined rapidly. Fluctuating trends in cultivated and settlement, bush land and bare land. Population pressure and associated demand are the main causes behind LULC changes in the study area.

SHIVANI, SANJEEV KUMAR, KUMARI SHUBHA et al.

A field experiment was conducted for three consecutive years during 2019–20, 2020–21 and 2021–22 at ICAR-Research Complex for Eastern Region, Patna, Bihar to assess the best and profitable rice (Oryza sativa L.) based cropping system through crop diversification for sustainable agriculture. Diversification of wheat (Triticum aestivum L.) with rabi vegetables and inclusion of green gram (Vigna radiata L.) during summer season in rice-wheat cropping system was studied with rice cultivars of different duration. The rice-cauliflower (Brassica oleracea var. botrytis)-spinach (Spinacia oleracea L.)-green gram system recorded the highest system productivity with a rice equivalent yield of 34.26 t/ha, followed by the rice-broccoli (Brassica oleracea var. italica)-spring onion (Allium fistulosum)-green gram system (32.47 t/ha) which were more than double as compared to rice-wheat-green gram system (12.29 t/ha). Land use efficiency was recorded maximum in rice-tomato (Solanum lycopersicum)-green gram system (95.34%) and minimum in rice-garden pea (Pisum sativum L.)-green gram system (82.19%). Growing shorter duration rice cultivar (Swarna Shreya) in the cropping systems significantly enhanced the system productivity, system production efficiency and income as compared to longer duration rice variety in different cropping systems. Diversification of wheat with rabi vegetables enhanced the gross return, net return and benefit cost ratio irrespective of rice duration. The cropping intensity was also increased by diversifying wheat with cauliflower and broccoli grown after short duration rice (400%), as it provided an opportunity to grow a short span crop in the rabi season itself before sowing of green gram during summer season.

Maxim Khatser

Стаття присвячена формуванню нових наукових рішень щодо дослідження особливостей управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і, особливо, національні, галузеві і регіональні). Розкрита наукова та практична необхідність активізації процесів підвищення адаптивності, гнучкості та ефективності бізнесу в Україні за рахунок управління активами (майном) трансформаційним процесам, які відбуваються на ринках товарів та ресурсів. Метою статті є пошук шляхів підвищення ефективності управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і, особливо, національні, галузеві і регіональні). Методологія: використано комплекс загальних та спеціальних методів на емпіричному і теоретичному рівнях, таких як: метод літературного аналізу для дослідження наукової проблематики у сфері управління активами суб’єктами підприємництва; метод аналізу для визначення тенденцій і особливостей управління активами (майном) загалом, необоротними та оборотними активами на великих, середніх, малих та мікропідприємствах України; метод синтезу для визначення проблематики управління активами (майном) загалом, необоротними та оборотними активами на великих, середніх, малих та мікропідприємствах України; методи індукції та дедукції для формування сукупність рекомендацій щодо підвищення адаптивності, гнучкості та ефективності бізнесу в Україні у контексті управління активами (майном) трансформаційним процесам, які відбуваються на ринках товарів та ресурсів; методи систематизації, групування і логічного узагальнення для систематизації інформації, формування висновків і наукових пропозицій статті. Результати: визначено шляхи підвищення ефективності управління активами на підприємствах України в умовах трансформації ринків товарів та ресурсів (світові, міжнародні, і особливо національні, галузеві і регіональні).

John A. Mathews

Linjiao Wang, Xiang Gao, Maoyin Sheng

Abstract Background Phytolith-occluded organic carbon (PhytOC) is an important mechanism of long-term stable carbon sinks in terrestrial ecosystems. Farmland abandonment is a widespread land use change in the process of urbanization and industrialization and is still ongoing. Farmland abandonment can significantly affect soil carbon cycling. To elucidate the effects of farmland abandonment on soil PhytOC accumulation, in the present study, corn fields abandoned for 0 to 30 years ago in the mountainous areas of southern China were selected as the research objects. The change trends, influencing factors, and driving mechanisms of soil PhytOC accumulation during the abandonment process were studied. Results The following results were obtained: (1) The range of PhytOC content and storage of the 0–15 cm soil profile for both active and abandoned corn fields was 0.39–1.49 g·kg− 1 and 0.27–0.83 t·hm− 2, respectively. (2) There was a notable enhancement in soil PhytOC accumulation as the duration of abandonment lengthened. In particular, after 30 years of abandonment, soil PhytOC accumulation rose significantly. (3) Abandonment noticeably altered the contents and ratios of soil nutrients of C, N, P and Si, along with key soil enzyme activities such as urease, sucrase, alkaline phosphatase, and catalase. (4) In the context of corn field abandonment, increase in soil PhytOC was primarily attributed to modifications in PhytOC inputs due to variations in surface vegetation cover. The impact of soil environment alterations resulting from abandonment on PhytOC decomposition was less pronounced. Conclusions These findings are instrumental for accurately assessing the carbon sequestration potential of farmland abandonment and for developing regional carbon management strategies based on such practices.