Search Results (75)

Photovoltaic (PV) technology, as a crucial source of clean energy, can effectively mitigate the impact of climate change caused by fossil fuel-based power generation. However, improper use of PV installations may encroach upon agricultural land, grasslands, and other land uses, thereby affecting local ecosystems. Exploring the spatial characteristics of centralized or distributed PV installations is essential for quantifying the development of clean energy and protecting agricultural land. Due to the distinct characteristics of centralized and distributed PV installations, large-scale mapping methods based on satellite remote sensing are insufficient for creating detailed PV distribution maps. This study proposes a model called Joint Semi-Supervised Weighted Adaptive PV Panel Recognition Model (JSWPVI)to achieve reliable PV mapping using UAV datasets. The JSWPVI employs a semi-supervised approach to construct and optimize a comprehensive segmentation network, incorporating the Spatial and Channel Weight Adaptive Model (SCWA) module to integrate different feature layers by reconstructing the spatial and channel weights of feature maps. Finally, a guided filtering algorithm is used to minimize non-edge noise while preserving edge integrity. Our results demonstrate that JSWPVI can accurately extract PV panels in both centralized and distributed scenarios, with an average extraction accuracy of 91.1% and a mean Intersection over Union of 77.7%. The findings of this study will assist regional policymakers in better quantifying renewable energy potential and assessing environmental impacts. Full article

Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest fires in the Ghalahmah Mountains, Saudi Arabia, using remote sensing data and fire impact models to assess fire severity, environmental drivers, and post-fire vegetation recovery. The research integrates Landsat 8, Sentinel-2, and DEM data to analyze the spatial extent and severity of a 2020 fire event using the Relativized Burn Ratio (RBR). Results reveal that high-severity burns covered 49.9% of the affected area, with pre-fire vegetation density (NDVI) and moisture (NDWI) identified as key drivers of fire severity through correlation analysis and Random Forest regression. Post-fire vegetation recovery, assessed using NDVI trends from 2021 to 2024, demonstrated varying recovery rates across vegetation types. Medium NDVI areas (0.2–0.3) recovered fastest, with 134.46 hectares exceeding pre-fire conditions by 2024, while high NDVI areas (>0.3) exhibited slower recovery, with 26.55 hectares still recovering. These findings underscore the resilience of grasslands and shrubs compared to dense woody vegetation, which remains vulnerable to high-severity fires. The study advances fire ecology research by combining multi-source remote sensing data and machine learning techniques to provide a comprehensive understanding of fire impacts and recovery processes in semi-arid mountainous regions. The results suggest valuable insights for sustainable land management and conservation, emphasizing the need for targeted fuel management and protection of ecologically sensitive areas. This research contributes to the broader understanding of fire ecology and supports efforts to post-fire management. Full article

Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models’ (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests’ (RF) 0.5662 and GBM’s 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau. Full article

Forage legumes, besides their use as ruminant feed supplements, contribute to other agricultural, forestry and natural ecosystems’ sustainability around the world. Our objective in this summary is to emphasize that versatility in the face of biotic, abiotic and socio-economic variability is among the most important traits that forage legumes contribute to sustaining human populations in those diverse ecosystems. Forage legumes could contribute even more to agroecosystems if we 1. consider ecosystem services as well as food, feed and fuel production; 2. more fully exploit what we already know about forage legumes’ multiple uses; and 3. focus greater attention and energy exploring and expanding versatility in currently used and novel versatile species. To draw attention to the importance of this versatility to sustainable grasslands, here we review multiple legumes’ roles as forage, bioenergy, pulses (legume seeds for human consumption), pharmaceuticals and cover crops as well as environmental services, in particular soil health, C sequestration and non-industrial organic N. The major points we single out as distinguishing sustainable versatile forage legumes include (1) multiple uses; (2) adaptation to a wide range of edaphoclimatic conditions; (3) flexible economic contributions; and (4) how genomics can harness greater legume versatility. We predict that, because of this versatility, forage legumes will become ever more important as climates change and human pressures on sustainable agro-environments intensify. Full article

Amidst the backdrop of the fossil fuel energy crisis, the development of renewable energy sources is experiencing an unprecedented acceleration in Spain and focusing in metropolitan areas. This study investigates the potential for photovoltaic energy development in Spanish metropolitan areas, specifically Madrid and its surrounding region. Recognizing the inherent challenges of land use and material scarcity associated with this development, the research aims to quantify the achievable photovoltaic capacity with less environmental impact for the region, along with the corresponding land occupation and material consumption requirements. A Material Flow Analysis (MFA) methodology is employed to project these parameters to 2050. The analysis estimates a potential production capacity of 32,163 GWh/year, representing 79.46% of the projected electricity consumption in 2050 (and 41.32% of final energy consumption). This capacity would necessitate the utilization of 32,169 hectares of land (4.01% of the regional area), and 7139 hectares of rooftop space. Critically, 48% of the suitable land is agricultural land, 9% forest, 38% grassland and scrubland and 5% corresponds to other land uses. highlighting potential land-use competition. Furthermore, the study extrapolates the material requirements to a global scale, estimating the percentage of global mineral reserves required for a comparable energy transition. The analysis yields an estimate of 0.66% for aluminum, 14.49% for copper, and 33.13% for silver. These findings provide crucial insights into the material and geographical constraints impacting the feasibility of urban energy transitions. Full article

Vegetation changes on the Tibetan Plateau are indicative of the dual impacts of climate change and human activities, with satellite data offering a potent tool for monitoring these alterations. However, the impacts of future land cover change on vegetation changes on the Tibetan Plateau under different climate scenarios remain unclear. This study systematically investigates vegetation trends and their contributions driven by land cover changes under eight future climate scenarios from 2015 to 2100 using remotely sensed land cover and NDVI data. We estimated consistent NDVI data for land cover changes under the climate scenarios and quantified the vegetation trends and the relative contributions of each land cover type using a relative importance matrix. The study found that (1) Grasslands will remain the dominant land cover, increasing by 4.13% from 2015 to 2100, while Forests, particularly Woody Savannas and Mixed Forests, will significantly influence vegetation trends, with maximum contributions of 48–55% across seasons. (2) Vegetation trends under climate scenarios exhibit greening, browning followed by greening, fluctuation, and browning patterns, with greening being predominant. (3) Forests dominate vegetation trends in most scenarios, especially under pathways of sustainability (SSP1) and fossil-fueled development (SSP5). (4) The seasonal patterns of vegetation changes due to land cover changes are generally similar to the annual one; variations in land cover changes under different scenarios lead to differences in vegetation seasonal patterns. Our research promotes the understanding of the interaction between future land cover changes and vegetation changes on the Tibetan Plateau. Full article

Carbon fluxes are valuable indicators of soil and ecosystem health, particularly in the context of climate change, where reducing carbon emissions from anthropogenic activities, such as forest fires, is a global priority. This study aimed to evaluate the impact of prescribed burns on soil respiration in semi-arid grasslands. Two treatments were applied: a prescribed burn on a 12.29 ha paddock of an introduced grass (Eragostis curvula) with 11.6 t ha⁻¹ of available fuel, and a simulation of three fire intensities, over 28 circular plots (80 cm in diameter) of natural grasslands (Bouteloua gracilis). Fire intensities were simulated by burning with butane gas inside an iron barrel, which represented three amounts of fuel biomass and an unburned treatment. Soil respiration was measured with a soil respiration chamber over two months, with readings collected in the morning and afternoon. Moreover, CO₂ emissions by combustion and productivity after fire treatment were quantified. The prescribed burns significantly reduced soil respiration: all fire intensities resulted in a decrease in soil respiration when compared with the unburned area. Changes in albedo increased the soil temperature; however, there was no relationship between changes in temperature and soil respiration; in contrast, precipitation highly stimulated it. These findings suggest that fire, under certain conditions, may not lead to more CO₂ being emitted into the atmosphere by stimulating soil respiration, whereas aboveground biomass was reduced by 60%. However, considering the effects of fire in the long-term on changes in nutrient deposition, aboveground and belowground biomass, and soil properties is crucial to effectively quantify its impact on the global carbon cycle. Full article

Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km²) and grassland (645.741 km²). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration. Full article

Methane fermentation, which is one of the key processes in biogas production, plays an important role in the conversion of biomass to energy. During this process, changes occur in the chemical composition of organic feedstocks, including the chemical composition of grasses. The assessment of these changes is crucial for the efficiency and productivity of biogas production. The material for this study comprised fully mature grass blades with leaves and inflorescences and was collected from extensively used meadows and pastures, as well as cultivated and set-aside areas in the Wielkopolskie Voivodeship, the communes of Białośliwie and Trzcianka, Poland. The aim of this study was to compare methane fermentation efficiency in nine grass species and identify the biomass component involved in biogas production. The results indicate that the fermentation process, as expected, changed the cellulose content. The lignin content of the grasses before fermentation varied more than the cellulose content. The content of holocellulose (sum of carbohydrate components) in the grasses ranged from 59.77 to 72.93% before fermentation. Methane fermentation significantly reduced the carbohydrate content in the grasses, with a low degree of polymerization. Grassland biomass-based biogas production is a viable alternative to conventional fossil fuels. Full article

This review examines the essential components of a circular economy (CE) in relation to the agricultural sector. The bioeconomy and circular economy are crucial for sustainable global industrial growth, focusing on closed-loop systems. The sustainability debate centers on intergenerational equity and natural capital. The CE requires new environmental technologies and global coordination in order to combat climate change and biodiversity loss. In addition, efficient food production and waste reduction are essential due to population growth. However, biomass is vital for a bio-based economy, impacting food waste and climate change. Grasslands support sustainable dairy production and carbon sequestration. Thus, effective waste and wastewater management are critical, with biomass energy providing renewable alternatives. Nonetheless, biofuels remain key for sustainability, focusing on pollution control and Green Chemistry. It is well known that sustainable transportation relies on bioenergy, with ongoing research improving processes and discovering new fuels. One notable challenge is managing heavy metals in biofuel production, and this underscores the need for eco-friendly energy solutions. The main purpose for this review paper is to create a connection between circular economy aspects and the agricultural system, with focus on the following: bioeconomy research, biomass utilities, and biofuel production. Extensive research was performed on the specialized literature by putting in common the main problems. Key subjects in this paper include the use of biomass in agriculture, the problems of plastic recycling, and the function of the CE in mitigating climate change and biodiversity loss. Efficient food production and waste minimization are highlighted due to their relevance in a growing population. The study’s detailed research and discussion aim to give important insights into how these practices might promote economic development and sustainability. Furthermore, the study covers important waste management issues such as food waste, plant composting, and chemical waste neutralization. These topics are critical to understanding the circular economy’s broader implications for minimizing environmental damage and implementing sustainable waste management strategies. Full article

There is a widespread concern about the negative impact of intensive livestock farming on climate change and biodiversity loss. We analyzed the trade-off between meat production and environmental variables related to global warming—energy consumption, use efficiency of energy, greenhouse gas (GHG) emissions, carbon footprint, and GHG balance—of two alternative intensification strategies of livestock farming in the Flooding Pampa: conventional intensification (CI) based on external inputs, and ecological intensification (EI) based on maintaining native grassland in good condition through adaptive multi-paddock grazing (AMPG). We also explored the relationship between meat production and the economic variables gross margin and its year-to-year variation. Energy consumption was positively correlated with meat production (ρ = 0.95, p = 0.0117), and EI farms consumed less fuel energy and showed higher energy use efficiency than CI farms (294 ± 152 vs. 2740 ± 442 MJ ha⁻¹ y⁻¹, 38.4 ± 28.8 vs. 1.23 ± 0.13 MJ kg LW⁻¹ y⁻¹, p < 0.05, respectively). GHG emissions and carbon footprint did not show significant differences between EI and CI strategies. As soil carbon sequestration was significantly higher in EI farms than in CI farms (1676 ± 304 vs. −433 ± 343 kg CO_2eq ha⁻¹ y⁻¹, p < 0.05), GHG balance resulted almost neutral and higher under the EI strategy (−693 ± 732 vs. −3520 ± 774 kg CO_2eq ha⁻¹ y⁻¹, p < 0.05). CI strategy obtained higher meat production but a similar gross margin to the EI strategy and a more unstable economic return, as the coefficient of variation in the gross margin doubled that of the EI strategy (84 + 13.3 vs. 43 + 2.6, respectively, p < 0.05). Ecological intensification of cattle production in the Flooding Pampa demonstrates the potential for a positive relationship between individual cattle farmers’ profits and overall societal benefits, as reflected in improved environmental performance. Full article

Grassland covers approximately 17.4% of Europe’s land area, stores about 20% of the world’s soil carbon and has the potential to sequester carbon. With the help of sustainable management systems, grasslands could reduce greenhouse gases and act as a terrestrial sink for atmospheric CO₂. In this study, we will investigate the effect of grassland management (cutting, grazing, and a combination of the two) and soil depth (0–10, 10–20, 20–30 cm) on the physical (volumetric water content—VWC, bulk density—BD, porosity—POR, mass consisting of coarse fragments—FC) and chemical properties of soil (organic carbon—SOC, inorganic carbon—SIC, total carbon—STC, total nitrogen—STN, organic matter—SOM, C/N ratio, pH) in Central European lowlands. The management system affected BD, SOC and STN and tended to affect VWC and STC in the first soil depth only. Grazing and the combined system stored greater amounts of STN, SOC and STC and had higher BDs at the surface (0–10 cm) compared to the cutting system. Most soil properties were influenced by soil depth, with C/N ratio and BD increasing and SOC, STC, STN, SOM, VWC and POR decreasing with depth. Our study highlights an opportunity for grassland users to improve soil quality, reduce fossil fuel usage and improve animal welfare through their management systems and argues that systems such as grazing and the combined system should be promoted to mitigate climate change. Full article

Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. Full article

Fire is one of the principal factors influencing subalpine ecosystem succession. Species numbers and plant compositions are used to determine postfire disturbance, vegetation, structural change, and succession. Ecologists also integrate species diversity and mathematical models to enable researchers to obtain increasingly detailed insights into habitats during post-disturbance restoration processes. This study employed five species-abundance models, namely the niche preemption model, the broken-stick model, the log-normal model, the Zipf model, and the Zipf–Mandelbrot model, to perform fitting analysis on the abundance data of postfire species coverage in shrub grasslands near 369 Hut at Xue Mountain in Shei-Pa National Park, Taiwan. We performed the logarithmic transformation on plant-coverage areas for each period of postfire shrub-grassland succession, and then, based on histograms drawn for species–coverage distribution modes, the test results consistently showed normal distributions (p < 0.05). Species-coverage histograms measuring various periods showed that there were comparatively higher numbers of common species during postfire succession and that the numbers of rare species progressively increased. The fitting results of the five species-abundance models showed that although the most suitable abundance models for each period of postfire succession varied, the majority of these periods demonstrated decent fitting with respect to the Zipf–Mandelbrot model. These findings showed that fuel consumption provided nutrients in a manner that facilitated postfire regeneration. Moreover, dominant species, such as Yushania niitakayamensis, and Miscanthus transmorrisonensis, did not fully occupy growing spaces and resource availabilities; consequently, seeded species were able to grow. Full article

Grassland fires and dust weather in Mongolia can trigger major cascading disasters. Grassland fires from autumn to the following spring can indirectly affect dust weather occurrence in the spring by affecting land surface vegetation cover. In this paper, we selected the aimag (province) of Dornod, Mongolia, a typical temperate grassland area, as the study area. The study aims to (1) analyze the spatiotemporal patterns of grassland fire and dust weather in the past 22 years, as well as the effect of grassland fire on dust weather and to (2) explore in depth the mechanisms of the effects of grassland fire on dust weather. To achieve these goals, we utilize high-resolution satellite burned-area data and Synop dust data. In general, grassland fire and dust weather occurrence clearly varied spatiotemporally across the study area. Grassland fires are typically more frequent in spring and autumn, and dust weather is mainly concentrated in spring. Cumulative grassland fires (both days and burned area) from autumn to the following spring affected the spring cumulative dust weather days significantly, especially the spring cumulative dust storm days. Analysis of the mechanism of the effect of grassland fire on dust storms showed that abundant summer precipitation resulted in higher vegetation cover and more accumulated fuel from autumn to April of the following spring. Consequently, the cumulative grassland fire days were higher, and the cumulative burned area was larger during the period, leading to a significant increase in cumulative dust storm days in May of the spring. In Mongolia, grassland fires are often caused by human factors. The findings of the present study could facilitate the crafting of measures to prevent and reduce grassland fires and indirectly minimize dust weather frequency to protect the ecological environment and promote sustainable development. Full article