Hasil untuk "Land use"

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's 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 has developed 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, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight 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 on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) 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) and updated algorithms (wood harvest and shifting cultivation); it 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.

W. G. Hansen

E. Lambin, B. Turner, H. Geist et al.

W. Alonso

C. Hong, J. Burney, J. Pongratz et al.

Yuan Zhou, Mingxing Chen, Zhipeng Tang et al.

Abstract Globally, urbanization has dramatically changed land cover, causing a rapid growth in carbon emissions and related risk of climate change. This study estimated city-level land use carbon emissions (LUCEs) using a novel method based on the correction coefficient calculated by the carbon emissions from energy consumption and basic land use emissions. The method was applied to 13 cities in the Beijing-Tianjin-Hebei (BTH) urban agglomeration in China using 30 m resolution land use data and energy balance tables (EBTs), and the Environmental Kuznets Curve (EKC) was used to discuss the relationship between urbanization and LUCEs in three typical models. The results revealed the expansion of built-up land in the BTH region, and LUCEs at the city-level increase continually, except in Beijing, which showed the most significant expansion of built-up land but a declining trend in LUCEs in recent years. The relationship between urbanization and LUCEs can be summarized into three modes: ‘high urbanization - low emissions’, ‘middle urbanization - high emissions’, and ‘low urbanization - low emissions’. The results have great significance for the formulation of policies to reduce city-level carbon emission at different urbanization levels, and the implementation of high-quality people-oriented new-type urbanization can allow the realization of the carbon emission reduction targets of China.

Saeid Amini, M. Saber, Hamidreza Rabiei‐Dastjerdi et al.

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping.

D. van de Ven, Íñigo Capellán-Pérez, I. Arto et al.

Although the transition to renewable energies will intensify the global competition for land, the potential impacts driven by solar energy remain unexplored. In this work, the potential solar land requirements and related land use change emissions are computed for the EU, India, Japan and South Korea. A novel method is developed within an integrated assessment model which links socioeconomic, energy, land and climate systems. At 25–80% penetration in the electricity mix of those regions by 2050, we find that solar energy may occupy 0.5–5% of total land. The resulting land cover changes, including indirect effects, will likely cause a net release of carbon ranging from 0 to 50 gCO2/kWh, depending on the region, scale of expansion, solar technology efficiency and land management practices in solar parks. Hence, a coordinated planning and regulation of new solar energy infrastructures should be enforced to avoid a significant increase in their life cycle emissions through terrestrial carbon losses.

M. Leta, T. A. Demissie, J. Tränckner

Change of land use land cover (LULC) has been known globally as an essential driver of environmental change. Assessment of LULC change is the most precise method to comprehend the past land use, types of changes to be estimated, the forces and developments behind the changes. The aim of the study was to assess the temporal and spatial LULC dynamics of the past and to predict the future using Landsat images and LCM (Land Change Modeler) by considering the drivers of LULC dynamics. The research was conducted in Nashe watershed (Ethiopia) which is the main tributary of the Upper Blue Nile basin. The total watershed area is 94,578 ha. The Landsat imagery from 2019, 2005, and 1990 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated depending on the historical trends of LULC changes for the years 2035 and 2050. LCM integrated in TerrSet Geospatial Monitoring and Modeling System assimilated with MLP and CA-Markov chain have been used for monitoring, assessment of change, and future projections. Markov chain was used to generate transition probability matrices between LULC classes and cellular automata were used to predict the LULC map. Validation of the predicted LULC map of 2019 was conducted successfully with the actual LULC map. The validation accuracy was determined using the Kappa statistics and agreement/disagreement marks. The results of the historical LULC depicted that forest land, grass land, and range land are the most affected types of land use. The agricultural land in 1990 was 41,587.21 ha which increased to 57,868.95 ha in 2019 with an average growth rate of 39.15%. The forest land, range land, and grass land declined annually with rates of 48.38%, 19.58%, and 26.23%, respectively. The predicted LULC map shows that the forest cover will further degrade from 16.94% in 2019 to 8.07% in 2050, while agricultural land would be expanded to 69,021.20 ha and 69,264.44 ha in 2035 and 2050 from 57,868.95 ha in 2019. The findings of this investigation indicate an expected rapid change in LULC for the coming years. Converting the forest area, range land, and grass land into other land uses, especially to agricultural land, is the main LULC change in the future. Measures should be implemented to achieve rational use of agricultural land and the forest conversion needs to be well managed.

H. Long, Yingnan Zhang, Li Ma et al.

The study of land use transition has generally become an important breakthrough point to deeply understand the human-land interaction and reveal major socio-economic development issues and related environmental effects. Attempting to provide scientific support for sustainable land use and environmental management, this review systematically analyzes the overall picture, development trends, key fields and hot topics of land use transition research in the past two decades from a comprehensive perspective, which incorporates two complementary parts including the systematic quantitative literature review (based on CiteSpace) and the traditional literature review. The results reveal that: a. current research presents three characteristics, i.e., focusing on complex social issues, driven by realistic demand, and research branches becoming clearer and more systematic; b. there are four key fields and hot topics in land use transition research, i.e., i. theories and hypothesis of land use transition; ii. measuring land use transition; iii. the impacts of land use transition on “social-economic-ecological” system; iv. drivers and regulation of land use transition. However, challenges remain, current land use transition research is still to some extent fragmented, and it should be enriched by integrating with land system science. The dominant morphology biased should be redressed by underlining the recessive morphology transition process. Meanwhile, new techniques and methods are necessary to observe, track, monitor and model the recessive attributes. Finally, distant drivers of land use transition should not be ignored in this rapidly globalizing world.

C. Hong, Hongyan Zhao, Yue Qin et al.

International trade separates consumption of goods from related environmental impacts, including greenhouse gas emissions from agriculture and land-use change (together referred to as “land-use emissions”). Through use of new emissions estimates and a multiregional input-output model, we evaluated land-use emissions embodied in global trade from 2004 to 2017. Annually, 27% of land-use emissions and 22% of agricultural land are related to agricultural products ultimately consumed in a different region from where they were produced. Roughly three-quarters of embodied emissions are from land-use change, with the largest transfers from lower-income countries such as Brazil, Indonesia, and Argentina to more industrialized regions such as Europe, the United States, and China. Mitigation of global land-use emissions and sustainable development may thus depend on improving the transparency of supply chains. Description Trade and land-use emissions International trade allows goods and services produced in one country to be consumed elsewhere, separating consumption from its environmental impacts. Greenhouse gas emissions from land use is one key impact. Hong et al. present estimates of land-use emissions embodied in global trade from 2004 to 2017. They found that 27% of all land-use emissions were related to agricultural products consumed in a different country from where they were produced. They also identified the trade relationships representing the largest transfers of land-use emissions, which could help target efforts to improve the sustainability of land use and agricultural production. The authors suggest that improving transparency by regular accounting of land-use emissions embodied in trade could support strategic trade adjustments that would mitigate land-use emissions. —AMS Consumption in industrialized regions contributes to land-use change and greenhouse gas emissions in low-income regions.

Xiaoyang Liu, Xupeng Huang, Rongjin Zhu et al.

To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.

Ryan Nedd, K. Light, M. Owens et al.

Land is a natural resource that humans have utilized for life and various activities. Land use/land cover change (LULCC) has been of great concern to many countries over the years. Some of the main reasons behind LULCC are rapid population growth, migration, and the conversion of rural to urban areas. LULC has a considerable impact on the land-atmosphere/climate interactions. Over the past two decades, numerous studies conducted in LULC have investigated various areas of the field of LULC. However, the assemblage of information is missing for some aspects. Therefore, to provide coherent guidance, a literature review to scrutinize and evaluate many studies in particular topical areas is employed. This research study collected approximately four hundred research articles and investigated five (5) areas of interest, including (1) LULC definitions; (2) classification systems used to classify LULC globally; (3) direct and indirect changes of meta-studies associated with LULC; (4) challenges associated with LULC; and (5) LULC knowledge gaps. The synthesis revealed that LULC definitions carried vital terms, and classification systems for LULC are at the national, regional, and global scales. Most meta-studies for LULC were in the categories of direct and indirect land changes. Additionally, the analysis showed significant areas of LULC challenges were data consistency and quality. The knowledge gaps highlighted a fall in the categories of ecosystem services, forestry, and data/image modeling in LULC. Core findings exhibit common patterns, discrepancies, and relationships from the multiple studies. While literature review as a tool showed similarities among various research studies, our results recommend researchers endeavor to perform further synthesis in the field of LULC to promote our overall understanding, since research investigations will continue in LULC.

Sugianto Sugianto, A. Deli, E. Miswar et al.

The change in land use and land cover in upstream watersheds will change the features of drainage systems such that they will impact surface overflow and affect the infiltration capacity of a land surface, which is one of the factors that contributes to flooding. The key objective of this study is to identify vulnerable areas of flooding and to assess the causes of flooding using ground-based measurement, remote sensing data, and GIS-based flood risk mapping approaches for the flood hazard mapping of the Teunom watershed. The purposes of this investigation were to: (1) examine the level and characteristics of land use and land cover changes that occurred in the area between 2009 and 2019; (2) determine the impact of land use and land cover changes on the water overflow and infiltration capacity; and (3) produce flood risk maps for the Teunom sub-district. Landsat imagery of 2009, 2013, and 2019; slope maps; and field measurement soil characteristics data were utilized for this study. The results show a significant increase in the use of residential land, open land, rice fields, and wetlands (water bodies) and different infiltration rates that contribute to the variation of flood zone hazards. The Teunom watershed has a high and very high risk of ~11.98% of the total area, a moderate risk of 56.24%, and a low and very low risk of ~31.79%. The Teunom watershed generally has a high flood risk, with a total of ~68% of the area (moderate to very high risk). There was a substantial reduction in forest land, agricultural land, and shrubs from 2009 to 2019. Therefore, the segmentation of flood-risk zones is essential for preparation in the region. The study offers basic information about flood hazard areas for central governments, local governments, NGOs, and communities to intervene in preparedness, responses, and flood mitigation and recovery processes, respectively.

Long Li, Xianjin Huang, Hong Yang

Handrian Ginting Jonson, Afrida Afrida, Zulkifli Addina et al.

This study examines the 2024 flash flood in Pandai Sikek, West Sumatra, through the lens of disaster anthropology and social memory. Based on preliminary research and one week of ethnographic fieldwork, the research reveals that while extreme rainfall triggered the event, socio-ecological drivers such as post-COVID return migration, deforestation, and land-use change significantly amplified its impacts. The community’s vulnerability was heightened by the absence of social memory: no oral traditions, rituals, or institutional practices existed to anticipate or respond to such a disaster. The flood therefore collapsed long-standing narratives of safety associated with Mount Singgalang and forced the community to confront a new reality of risk. Findings show that the disaster produced both trauma and solidarity, as gotong royong, remittances from migrants, and local organizing supported immediate recovery. At the same time, new and contested memories of vulnerability began to emerge. Early mitigation efforts, including reforestation, canal reinforcement, and disaster awareness initiatives, indicate steps toward resilience, though challenges remain in institutionalizing these lessons. The study concludes that building resilience in Pandai Sikek requires not only ecological restoration but also the transformation of traumatic absence into enduring social memory.

Nicodemo Abate, Diego Ronchi, Sara Elettra Zaia et al.

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes.

Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp et al.

Bioenergy with carbon capture and storage (BECCS) is a crucial element in most modelling studies on emission pathways of the Intergovernmental Panel on Climate Change to limit global warming. BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. To assess the former pros and cons of BECCS deployment, interdisciplinary model approaches require detailed estimates of technological information related to BECCS production technologies. Current estimates of the cost structure and capture potential of BECCS vary widely due to the absence of large-scale production. To obtain more precise estimates, a global online expert survey ( N = 32) was conducted including questions on the regional development potential and biomass use of BECCS, as well as the future operating costs, capture potential, and scalability in different application sectors. In general, the experts consider the implementation of BECCS in Europe and North America to be very promising and regard BECCS application in the liquid biofuel industry and thermal power generation as very likely. The results show significant differences depending on whether the experts work in the Global North or the Global South. Thus, the findings underline the importance of including experts from the Global South in discussions on carbon dioxide removal methods. Regarding technical estimates, the operating costs of BECCS in thermal power generation were estimated in the range of 100–200 USD/tCO _2 , while the CO _2 capture potential was estimated to be 50–200 MtCO _2 yr ^−1 by 2030, with cost-efficiency gains of 20% by 2050 due to technological progress. Whereas the individuals’ experts provided more precise estimates, the overall distribution of estimates reflected the wide range of estimates found in the literature. For the cost shares within BECCS, it was difficult to obtain consistent estimates. However, due to very few current alternative estimates, the results are an important step for modelling the production sector of BECCS in interdisciplinary models that analyse cross-dimensional trade-offs and long-term sustainability.

Sharon Paoli Bias Ramos, Gabriel Rodrigues Martins de Freitas, Silvana Teresa Lacerda Jales

Objetivo: analisar o perfil pós-consumo dos medicamentos descartados pela comunidade universitária no coletor disponibilizado no Centro de Ciências da Saúde da Universidade Federal da Paraíba. Métodos: O projeto de extensão Descarta CIM instalou um coletor no Centro de Informações sobre Medicamentos - CIM do Departamento de Ciências Farmacêuticas e promoveu campanhas educativas em outros centros de ensino do Campus I. A partir disso, foi feita a pesagem e catalogação dos medicamentos descartados durante um período de 6 meses, seguida de análise detalhada do tipo de medicamento, categoria regulatória, classificação ATC (Anatomical Therapeutic Chemical), forma farmacêutica, tipo de embalagem e prazo de validade. Resultados: Os dados encontrados indicam que os medicamentos genéricos representam 42,5% do volume descartado, seguidos por medicamentos de referência (35,7%) e similares (21,7%). A análise da classificação ATC revela uma prevalência de medicamentos relacionados ao sistema digestivo e metabolismo, seguidos por sistema cardiovascular, sistema nervoso e músculo-esquelético. Observa-se uma alta porcentagem de medicamentos fora do prazo de validade (72,8%), levantando questões sobre a prática de automedicação e a necessidade de conscientização sobre o uso racional de medicamentos. Conclusão: O estudo demonstra algumas limitações, sobretudo quando não se pode determinar com exatidão se todos os medicamentos descartados foram efetivamente utilizados pela comunidade universitária. Contudo, a análise demonstrou que a promoção de campanhas educativas e a presença de coletores incentivam o descarte adequado de medicamentos e estimulam uma mudança de comportamento e a prática de ações de proteção ambiental.  

Paolo Magaudda

This paper examines the emergence of the ‘gravel bike’, a new and successful category of sports bicycles that gained prominence in the global cycling industry in the late 2010s, to advance the understanding of the role of users in the processes of sociotechnical innovation. The study traces the development of gravel cycling and the gravel bike within the framework of science and technology studies (STS), introducing the concept of ‘user betrayal’ to highlight how innovations initially driven by users can later diverge from their original values and needs. The development of the gravel bike represents a case where users’ input played a crucial role in creating an alternative cycling culture that directly supported the introduction of a new, successful bicycle model. However, the commercialization and institutionalization of gravel cycling, driven by industries, institutions and sporting bodies, has led to a significant shift away from the values that motivated early enthusiasts. This case reveals the tensions between user-driven innovation and the forces of commodification, emphasizing how marketing and institutional pressures can undermine the original needs and ideals of user collectives.