Vegetation Types Research Articles

Use of light response curve parameters to estimate gross primary production capacity from chlorophyll indices of global observation satellite and flux data

The photosynthetic rate has a nonlinear relationship with PAR during the day. We previously developed an algorithm for estimating GPP capacity, which is defined GPP under low-stress condition, using light response curves (LRCs). In this study, we studied the characteristics of LRC parameters of the initial slope and the maximum gross photosynthesis rate (Pmax), and formulas to calculate Pmax from the relationship between the chlorophyll index of the green and near-infrared (NIR) bands (CIgreen) and the GPP capacity at PAR = 2000 μmol m−2 s−1 (GP2000) for nine vegetation types spanning tropical to subarctic climates on the Eurasian and North American continents using eddy covariance flux measurements and Moderate Resolution Imaging Spectrometer (MODIS) data. The slope of the relationship between CIgreen and GP2000 was highest for sites dominated by herbaceous plants such as open shrubland, savanna, and cropland (rice paddy); it was lower at sites dominated by woody plants. The yearly GPP/GPP capacity ratio was close to one in flux data. When the method was applied to satellite data, the daily GPP capacity exhibited a similar seasonal pattern to that of the Flux GPP and MODIS GPP products. Under high dryness conditions, Flux GPP showed the drop from the GPP capacity estimated from CIgreen and diurnal PAR data around noon, and they were nearly identical during the early morning and late afternoon. The instantaneous GPP capacity could be considered the baseline of the instantaneous GPP with stress-free conditions and important for quantifying midday depression at the sub-day scale.

Untangling the environmental drivers of gross primary productivity in African rangelands

Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa.

Monitoring native, non-native, and restored tropical dry forest with Landsat: A case study from the Hawaiian Islands

Tropical dry forests are highly threatened at a global scale. Long-term monitoring of remaining stands is needed to assess forest health, efficacy of management practices, and potential impacts of climate change. Using a multi-seasonal Landsat time series, we examined Normalized Difference Vegetation Index (NDVI) patterns in native dry forest, non-native vegetation types, and dry forest restoration sites from 1999 to 2022 in the Hawaiian Islands. We calculated trends in median NDVI and robust coefficient of variation of NDVI for dry and wet seasons, and used Breaks for Additive Seasonal and Trend analysis to detect trend departures. To assess the impact of regional drying trends, NDVI trends were compared to the seasonal long-term precipitation anomaly and cumulative precipitation anomaly. We found that native dry forest was less green than non-native forest, particularly during the dry season, and that median NDVI increased in both native and non-native dry forests over the study period despite negative precipitation anomaly trends. This result differs from coarser-scale studies in Hawaii, but is supported by trends in other dry forest regions. Greening was also observed in restoration study sites, especially larger sites where native species establishment and recruitment has been reported. Non-native grassland NDVI exhibited a strong positive link to precipitation anomalies, suggesting that drier climate scenarios may exacerbate the invasive grass-wildfire cycle that threatens native dry forest. These results demonstrate that Landsat time series may be used to detect seasonal variation in dry forest plots and to support restoration site monitoring in a highly fragmented ecosystem.

The threat from ozone to vegetation in Ireland

Ozone is the most damaging air pollutant to vegetation globally. Metrics of accumulated ozone above a concentration threshold (e.g. AOT40, ppb·h) have been widely used to assess ozone risk. However, there is growing consensus that accumulated Phytotoxic Ozone Dose (POD) above a receptor-specific critical stomatal flux threshold (y; nmol O3 m−2 s−1), expressed per unit of projected leaf area, provides a more reliable risk assessment, as it considers ozone entering the leaf (PODy, mmol m−2 leaf area). Few studies have assessed both concentration- and flux-based metrics using site-specific observations of ozone and meteorology. In this study we assessed the risk that ozone poses to five vegetation types across eight sites in Ireland during 2005–2021, using AOT40 and PODy risk metrics, and we predicted impacts using dose–response relationships. Long-term trends in both metrics were also assessed. The PODy critical level for vegetation protection was exceeded for all vegetation types, with exceedances most common at Atlantic coastal sites, and for tree species (beech POD1 15.7–25.7 mmol/m2 PLA). When PODy and AOT40 results were normalised based on their respective critical levels, predicted impacts were higher for PODy. There were significant increases in PODy for three vegetation types at rural sites during the study period, which also experienced increases in temperature and global solar radiation. The long-term trends were consistent with other European studies that show decreases in AOT40 and increases in PODy. While ozone concentrations in Ireland are relatively low (39–75 μg/m3 five-year average range), the humid climate and longer growing season may lead to elevated stomatal ozone uptake and thereby a risk to vegetation.

Estimating fuel load for wildfire risk assessment at regional scales using earth observation data: A case study in Southwestern Australia

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

The application of unsupervised machine learning for the elucidation of the burial effect of lignin in peatland: Case of TMAH thermochemolysis

Lignin is one of the major components of organic macromolecules in peatland. Analysis of lignin in sediments is considered as a valuable tool for understanding peat vegetation as well as carbon cycle. It provides valuable perspectives on different sides such as occurrence of various vegetation types and the extent of decay in terrestrial organic material. Thermochemolysis technique using tetramethyl ammonium hydroxide (TMAH) was used for lignin analysis, in an ombrotrophic peatland. It specifically breaks of O-aryl bonds followed by methylation of the lignin components yielding phenolic subunits. Several discrepancies exist between TMAH thermochemolysis findings and those obtained from previous investigations with CuO oxidation. This could be explained by the specific pool of aryl ether, specifically targeted by the thermochemolysis, with discarding of C-C bonds. Hence, this would show the capacity of TMAH thermochemolysis to cleave oxidised lignin leaving lignin of fresh and preserved organic matter intact. The TMAH thermochemolysis method's efficiency in molecular characterization and lignin degradation was evaluated using principal component analysis (PCA). PCA reduced dimensionality and redundancy of component contributions, with the first two principal components accounting for 70.33 % of the total variance. PCA has reinforced the selectively of the thermochemolytic approach in targeting O-aryl bonds while maintaining C-C bonding of polyphenolic sub-units. Hence, two distinct oxidation phases: recent (acrotelm and mesotelm) and older (catotelm) have been revealed. PCA was applied to diagenetic and source vegetation proxies, revealing strong agreement among variables which is supported by the use of molecular ratios as indicators of organic matter source and dynamics in a peat core.

Soil labile organic carbon and nitrate nitrogen are the main factors driving carbon-fixing pathways during vegetation restoration in the Loess Plateau, China

Although the microbial fixation of CO2 is a key process in regulating soil carbon cycling, the effects of vegetation type on microbial carbon-fixing pathways and their driving factors in soils have yet to be sufficiently established. In this study, based on macro-genome sequencing and other analytical methods, we sought to determine the soil physicochemical properties, soil organic carbon contents, carbon-fixing microorganisms, and carbon-fixing genes in areas of farmland (FL), grassland (GL). Robinia pseudoacacia (RP), Caragana korshinskii (CAK), and Prunus sibirica (PS) in the Wuliwan watershed of the Loess Plateau region of China. Our findings revealed that the organic carbon contents of the assessed soils increased in the following order: FL < GL∼PS < CAK < RP (P < 0.05). Re-vegetation-based restoration was found to enhance soil organic carbon pool stability. Compared with farmland soil, the proportions of recalcitrant organic carbon had increased by 6 % and 9 % in the soil at sites that had undergone restoration with C. korshinskii and R. pseudoacacia respectively. Among the identified carbon fixation pathways, the DC/4-HB cycle had the highest relative abundance of 25.10–25.52 %. The dominant groups of carbon-fixing microorganisms were identified as Actinobacteria and Proteobacteria, accounting for over 60 % of the total abundance. Furthermore, analysis based on a partial least squares path model revealed labile organic carbon and soil nitrate nitrogen as the primary drivers of carbon fixation pathways. Collectively, our findings in this study provide evidence to indicate that restoration of vegetation on the Loess Plateau can contribute to increases in soil organic carbon content and stability and the abundance of carbon-fixing microorganisms, with R. pseudoacacia and C. korshinskii having the most significant effects in this regard. These findings have important implications for restorative vegetation carbon pool management and provide additional perspectives for understanding global carbon cycling.

Temporal and spatial dynamics of carbon and water use efficiency on the Qinghai-Tibet plateau and their ecosystem responses

Research on the carbon and water use efficiency (CUE, WUE) in the Qinghai-Tibet Plateau (QTP) holds significant importance due to its implications in understanding and addressing global environmental challenges, particularly climate change. This study comprehensively explored the spatiotemporal distribution patterns and trends of WUE and CUE on the plateau from 2001 to 2018 using linear regression analysis and geostatistical methods. The results revealed a gradual decrease in WUE from southeast to northwest, showing a non-linear variation trend during the study period. CUE exhibited relatively minor spatial variations but demonstrated a significant decrease trend in the eastern regions. The results from the Random Forest algorithm and path analysis indicated that ecosystem respiration (Re) was the predominant controlling factor for WUE and CUE in the cold alpine (PA) region. In the temperate, warm temperate (Te&WT) region, temperature emerged as the primary determinant for WUE, while Leaf Area Index (LAI) served as the absolute dominant factor for CUE. Additionally, LAI also played a crucial role in determining WUE and CUE in the tropical and subtropical (Tr&S) region. Partial correlation analysis highlighted the heterogeneity in the responses of WUE and CUE to environmental changes across different regional climates and vegetation types. The findings emphasized the significant impact of biological factors on terrestrial carbon and water cycling processes (WUE and CUE) within the context of global climate change. Furthermore, the study identified a potential linkage between WUE and CUE, with their relationship to vegetation dynamics showing significant variations across various ecosystems. WUE and CUE showed a positive correlation in the western regions, particularly in high-altitude areas, while a negative correlation was observed in eastern regions with high SOC. In the Te&WT and the Tr&S region, the increase of NDVI (Normalized difference vegetation index) suppressed the rise of both WUE and CUE, establishing a negative correlation between them. Conversely, in the PA region, a significant positive correlation trend between WUE and CUE was observed, indicating a positive response to the decrease in NDVI. These findings are of significant importance for advancing the understanding of and effectively addressing climate change, maintaining ecosystem stability, and promoting sustainable socio-economic development.

Enhancing fruit and vegetable detection in unconstrained environment with a novel dataset

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to sustainable and technologically advanced farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, a dataset named FRUVEG67 was curated that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. A semi-supervised data annotation algorithm (SSDA) was developed that generates bounding boxes for objects to label the remaining nonannotated images. For detection, Fruit and Vegetable Detection Network (FVDNet) was proposed, an ensemble version of YOLOv8n featuring three distinct grid configurations. In addition, an averaging approach for the prediction of the bounding box and a voting mechanism for the prediction of the classes was implemented. Finally Jensen–Shannon Divergence (JSD) in conjunction with focal loss was integrated as the overall loss function for better detection of smaller objects. Experimental results highlight the superiority of FVDNet compared to recent versions of YOLO, showcasing remarkable improvements in detection and localization performance. An impressive mean average precision (mAP) score of 0.82 across all classes was achieved. Furthermore, the efficacy of FVDNet on open-category refrigerator images were evaluated, where it demonstrates promising results.

Contribution of Climatic Change and Human Activities to Vegetation Dynamics over Southwest China during 2000–2020

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems.

Remote sensing insights into land cover dynamics and socio-economic Drivers: The case of Mtendeli refugee camp, Tanzania (2016–2022)

The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis of high-resolution Earth observation data (Sentinel-1&2) with ground-based observations and input from local experts. Time series classifications of annual land use/land cover in the surroundings of the camp is developed from remote data. Subsequently main transitions and trends are quantitatively achieved. This is a first study which, not only treats the land transition process in a comprehensive manner, but also tracks the changes and their main drivers on an annual scale over the lifetime of the camp (2016–2021) and the post-closure situation in 2022. Most importantly, thanks to the involvement of social studies, it unfolds the socio-economical drivers of those changes. Drawing upon a random forest algorithm and available databases, we achieve overall classification accuracies of 83.5% (2020) and 82.0% (2022). Our findings indicate an ongoing expansion of cropland between 2016 and 2021, to the detriment of natural vegetation classes. The impact of environmental restoration programs implemented in the former camp area is visible by 2022. The proposed method can be used to identify areas of environmental risk and thus support decisions linked with sustainable development and land management.

Variation in soil water content and groundwater levels across three land cover types in a floodplain of the Kromme catchment, South Africa

AbstractInvasions of floodplains and riparian areas by alien woody species replacing predominantly herbaceous indigenous vegetation have altered the hydrological and ecosystem functioning in catchments. Although existing studies have examined changes in river flows following the establishment or clearing of alien woody vegetation, our understanding of impacts on soil water content and groundwater remains poor. Limited process knowledge restricts our capacity to reliably model and predict the impacts of land cover changes. As such, this work compared temporal variations in soil water content (SWC) and groundwater levels at three locations with different vegetation types: black wattle (Acacia mearnsii) trees, palmiet (Prionium serratum), and grass (dominated by Pennisetum clandestinum spp), within a floodplain site in the Kromme Catchment in the Eastern Cape Province of South Africa. Soil water content and shallow groundwater levels (&lt; 4 m below ground) were monitored from August 2017 to December 2019 using soil moisture probes and piezometers. Rainfall, vegetation type and antecedent conditions were identified as the major factors controlling observed responses. On average, soil water content and water retention were significantly higher (p &lt; 0.05) at the palmiet site, whilst the wattle site had the lowest SWC among the three sites. Shallow groundwater levels were also higher at the palmiet and grass sites and lowest at the wattle site. Results showed the negative impacts of black wattle trees on SWC and groundwater levels. These results are crucial for improved quantitative predictive capacity which would allow for better catchment management, for example, informing water supply planning and guiding restoration programs focusing on alien plant clearing.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

Landscape patterns of carbon fluxes in natural and disturbed ice-wedge-polygon tundra

ABSTRACT The degradation of ice-rich permafrost ecosystems due to climate change and infrastructure development strongly impacts carbon exchange dynamics in tundra landscapes. This study investigates the effects of surficial geology and infrastructure disturbances from road dust and flooding on vegetation and trace gas fluxes in polygonal ice-wedge tundra in arctic Alaska. We compared CO2 and CH4 fluxes from closed-chamber measurements at common landform elements (polygon centers, troughs, and rims) at a natural site and a disturbed site within the Prudhoe Bay Oil Field. Relationships among environmental parameters, plant species composition, and trace gas fluxes were assessed through nonmetric multidimensional scaling. Map extrapolations showed spatial variations in midsummer landscape-level ecosystem productivity and CH4 efflux at the various geologic landforms. Highest carbon uptake occurred in ice-rich drained thaw lake basins with aquatic, graminoid-dominated polygon troughs. In contrast, wet, featureless areas associated with more recently drained, ice-poor thaw lake basins showed a net carbon loss even during summer. The damming effect of road infrastructure led to deeply flooded, minimally vegetated troughs with low ecosystem respiration and high CH4 fluxes close to the road. This work highlights the importance of the complex interactions among surficial geology, landform elements, vegetation type, and disturbance factors in understanding carbon exchange dynamics in ice-rich permafrost environments.

Evidence for a floristically diverse rainforest on the Falkland archipelago in the remote South Atlantic during the mid- to late Cenozoic

Abstract We report the discovery of an ancient forest bed near Stanley, on the Falkland Islands, the second such ancient deposit identified on the South Atlantic island archipelago that is today marked by the absence of native tree species. Fossil pollen, spores and wood fragments preserved in this buried deposit at Tussac House show that the source vegetation was characterized by a floristically diverse rainforest dominated by Nothofagus-Podocarpaceae communities, similar to cool temperate Nothofagus forests/woodlands and Magellanic evergreen Nothofagus rainforests. The age limit of the deposit is inferred from the stratigraphic distribution of fossil pollen species transported by wind, birds or ocean currents from southern Patagonia, as well as similar vegetation types observed across the broader region. The deposit is suggested to be between Late Oligocene and Early Miocene, making it slightly older than the previously analysed Neogene West Point Island forest bed (200 km west of Tussac House). The combined evidence adds to our current knowledge of the role of climate change and transoceanic dispersal of plant propagules in shaping high-latitude ecosystems in the Southern Hemisphere during the late Palaeogene and Neogene.

Physicochemical, Nutritional, and Antioxidant Properties of Traditionally Fermented Thai Vegetables: A Promising Functional Plant-Based Food.

Fermented plant-based products were gathered from various regions in Thailand and categorized into 10 types of traditional commercial vegetables. Different vegetable materials and natural fermentation methods influence the diverse physical, chemical, nutritional, and functional attributes of the products. All the traditionally fermented Thai vegetable samples collected showed physicochemical properties associated with the fermentation process, contributing to the nutritional and functional quality of the final products. Achieving consistent research results is challenging due to the intricate nature of food matrices and biochemical processes during fermentation. The roles of microorganisms, especially probiotics, are crucial in delivering health benefits through fermented foods. Traditionally fermented Thai vegetable foods contain high levels of total soluble solids, titratable acidity, and salinity in pickled shallot and ginger as a result of the natural fermentation process and the ingredients used. The research findings were confirmed using a hierarchical cluster analysis (HCA)-derived dendrogram pattern. The nutritional compositions, total phenolic contents, and antioxidant activities varied among the different types of vegetables. The correlations among lipid, protein, fiber, total soluble solid (TSSs), total titratable acidity (TTA), and salinity as potential biomarkers in fermented vegetable products were examined. The results suggest that traditionally fermented Thai vegetable products significantly impacted food research by enhancing the quality and preserving the authenticity of traditionally fermented Thai vegetables.

NDVI Changes and Influencing Factors of Different Soil and Water Conservation Zones in Shandong Province

Exploring the characteristics of vegetation change and its influencing factors is essential to construct an ecological environment. Based on the NDVI dataset from 2000 to 2020, this study analyzed the spatial temporal attributes of NDVI changes in Shandong Province using the Sen trend analysis and the gravity center migration model. Furthermore, the spatial heterogeneity of NDVI and its influencing factors within the whole study area and different soil and water conservation zones were investigated using a Geo-detector model, considering population, hydrological, topographic, soil types, and vegetation types. The results were as follows： ① The NDVI in Shandong Province from 2000 to 2020 showed a fluctuating upward trend with significant seasonal characteristics that varied from different zones. The annual NDVI change showed a trend of single-peak in the Ⅲ-4-2t, Ⅲ-4-1xt, and Ⅲ-5-2w but showed a trend of double-peak in the Ⅲ-5-3fn. ② Regarding the spatial distribution, the NDVI was higher in the west-north and west-south areas and lower in the north and coastal areas. During the 21 years, the primary type of NDVI change was "medium-high coverage → high coverage," especially in the northeastern part of the soil conservation area of the Ⅲ-4-2t, the western part of the Ⅲ-4-1xt, and the ecological maintenance area of the Ⅲ-5-2w. Overall, 61.47% of the area had a positive trend of NDVI change with the gravity center of high coverage mitigating to the northeast, and the ecological environment was improved. ③ Soil types and population density were the dominant factors affecting NDVI in Shandong Province, with q values of 0.174 and 0.130, respectively. The chief factor in the Ⅲ-5-3fn, Ⅲ-4-2t, and Ⅲ-4-1xt was population density, with q values higher than 0.22, and the dominant factors in the Ⅲ-5-2w were soil types and vegetation types, with q values of 0.326 and 0.227, respectively. The interaction of the two factors enhanced the influence of the single factor, and the relationship between the influencing factors showed two-factor enhancement and nonlinear enhancement. The q-value of population density ∩ relative humidity was the highest, with a value of 0.257 in the Ⅲ-5-3fn. The q-value of population density ∩ soil types was the highest in the Ⅲ-4-2t and Ⅲ-4-1xt, reaching 0.297 and 0.378, respectively. The q-value of soil types ∩ vegetation types was the highest, with a value of 0.444 in the Ⅲ-5-2w. The results are expected to provide valuable references for improving the ecological environment of Shandong Province and lay a scientific foundation to make different conservation strategies for the individual soil and water conservation zones.

Spatial Distribution Characteristics of Plant Community and the Identification of Driving Factors in Riparian Zone of the Three-River Headwaters Region, China

The unique geographical and climatic conditions in the Three-River Headwaters Region gave birth to distinctive plant species and vegetation types. To reveal the spatial distribution of plant communities and soil habitats along the riparian zone of the Sanjiangyuan Region and their influencing mechanisms, 14 survey plots were set up （ten from the Yangtze River source, two from the Lancang River source, and two from the Yellow River source）, and the effects of soil nutrient characteristics （especially soil phosphorus morphology）, climate factors, and river topography on plant community characteristics were quantitatively analyzed. The results showed that the plant community composition in the riparian zone of the source of the three rivers was dominated by perennial herbs （72.2%）, followed by annual herbs （20.4%） and shrubs （7.4%）. The dominant plants were Stipa purpurea, Polygonum orbiculatum, Carex parvula, Potentilla anserina, and Gentiana straminea. The average plant coverage, Shannon-Wiener index, and Pielou index were （64.4% ±23.6%）, （1.31 ±0.42）, and （0.84 ±0.08）, respectively. The plant community diversity index was the highest in the Yangtze River source, followed by that in the Lancang River source, and the lowest in the Yellow River source. The soil pH of the riparian zone of the Yangtze River source was significantly higher than that of the Lancang River source, whereas the mean contents of organic matter, total nitrogen, and Fe-Al combined phosphorus were significantly lower than those of the Lancang River source. The calcium and magnesium-combined phosphorus was the main form of phosphorus in riparian soil （63.89%）. Temperature, soil organic phosphorus content, and pH had significant effects on plant composition in the riparian zone of the Three-River Headwaters Region, whereas soil calcium and magnesium-combined phosphorus content had significant effects on plant community diversities. These results may deepen the scientific understanding of the evolution trend and genetic mechanism of plant communities in the riparian zone of the Three-River Headwaters Region.

Multi-factor coupled forest fire model based on cellular automata

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Pelatihan Pembuatan Ice Cream Wortel untuk Komunitas PKK di Desa Wonolelo Magelang

Carrots are one of the abundant agricultural products in Wonolelo Village, Magelang. This type of root vegetable contains various nutrients such as Vitamin A, β-carotene, and fiber, which are beneficial for eye health and boosting the immune system against infections. The consumption of fresh carrots is less favored by the community, especially children, so carrot-based processed products in the form of healthy beverages like carrot ice cream can be an attractive and healthy alternative for consumption. The purpose of this activity is to explore the processing of carrots to maximize harvests and optimize the selling value and functionality of carrots as a healthy and appealing product for children. This training uses practical methods, allowing participants to practice and directly process carrots into ice cream. The conclusion of this activity is that it provides insight into carrot processing innovations for the PKK mothers in Wonolelo Village, specifically on how to turn carrots into ice cream, thereby increasing the selling value of carrots, which impacts the welfare of the community, especially carrot farmers.