Ecological Indicators最新文献_第7页

Advantages and potentials of SuperDove imagery for fine monitoring of suspended particulate matter in estuaries and tidal channels

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-20 DOI: 10.1016/j.ecolind.2025.113258

Peng Li , Shenliang Chen , Congliang Xu , Wenjuan Wu , Jiarui Qi , Yinghai Ke , Hongyu Ji , Shihua Li , Xiaojing Zhong

Suspended particulate matter (SPM) concentration is an essential biogeochemical parameter for water quality evaluation and morphodynamic researches. As the newest satellite in Planet family, SuperDove (SD) with eight spectral bands achieves observation to Earth with unprecedented temporal and spatial resolution. In this study, we developed a SPM retrieval model for SD using in-situ datasets in Yellow River Estuary, and compared the spectral and SPM products of SD with Sentinel-2 MSI and Landsat-8 OLI, and finally investigated SPM variations within typical tidal channels in recent years using multiple SD images. The results revealed that SPM concentrations derived from SD achieved high accuracy (R² = 0.95, Relative Percentage Difference = 30.69 %) based on our algorithm. While SD, MSI and OLI agreed well in terms of top-of-atmosphere reflectance, remote sensing reflectance and retrieved SPM concentrations, SD was able to effectively monitor SPM dynamics in tidal channels due to its higher spatial resolution. Several human-derived floods in recent years caused damage to the south embankment of Yellow River, resulting in lateral transport of high-SPM river water, which dramatically increased SPM concentration in the tidal channels and influenced the neighboring tidal channel networks through the newly developed fine trenches. Moreover, for commonly used satellite data, the spatial resolution of 3–30 m is required to characterize the details of SPM distribution, and the observation frequency of at least 1/1d is necessary to capture monthly change pattern of SPM, which demonstrated that SD imagery has great potential for monitoring SPM or other parameters in high-turbidity, strong-dynamic and small-scale waters.

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引用次数: 0

How data collection may affect the carbon footprint – The case of carbon foodprint accounting for cities

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-20 DOI: 10.1016/j.ecolind.2025.113256

Małgorzata Świąder , Luke John Schafer , Marin Lysák , Christian Bugge Henriksen

Evidence-based policymaking can foster climate justice in the light of climate change risks. For this reason, adopting environmental metrics such as Ecological Footprint or carbon footprint becomes imperative.

As carbon footprint assessments evolve, there remains a critical challenge in the availability of data representing lifestyles of inhabitants. Therefore, this research refers to the question What is the effect of utilizing currently available data to describe the current impact of cities and their inhabitants on the environment? The research focuses on foodprint assessment across multiple resource scales: national, city-wide, regional, urban-size (more local-level structural data), and urban-regional. Therefore, five individual CF results for each of the 18 Polish cities were obtained, followed by a one-to-one comparison, offering ten comparisons of results, that is, each with each.

On average, the differences in total CF results for different levels of data detail are at most ±3.5 %. Of interest seems to be the impact of regional conditions on the values of the footprint, especially the quantified urban-regional results. Differences in this scale of data, relative to other results, are associated with an increase in the foodprint from 0.001 to 0.003 global hectares per capita. It could be especially important for cities having the largest population. Even though on average for 200–499 K urban-size cities the results were the highest compared to the national ones (higher by 0.004 global hectares), it was for cities with over 500 000 inhabitants that the largest differences were observed at the level of individual products. In general, variations of ±20 % in results were registered for products such as poultry, cheese, vegetable fats, sugar, and potatoes.

The findings reveal that for cities with populations under 200,000, the reliance on high-resolution local data provides limited additional accuracy, suggesting that national or regional datasets are often sufficient. This insight can optimize resource allocation for evidence-based policymaking, particularly in the context of urban adaptation plans and climate action strategies.

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引用次数: 0

Temporal analyses of global suitability distribution for fall armyworm based on Multiple factors

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113181

Minghao Wang , Wenjiang Huang , Yingying Dong , Yanru Huang , Bing Zhang , Gang Sun , Maged Elkahky , Changping Huang

The fall armyworm (FAW; Spodoptera frugiperda) has been a persistent threat to global food security due to its strong migratory ability and wide range of host plants. However, most current studies on the suitability distribution of FAW focus on extracting suitable areas in specific regions on an annual basis. Consequently, research on the suitability distribution of FAW at a larger scale and with higher temporal resolution is urgently needed to provide data support for early prevention and control. This study differentiated the historical occurrence records of FAW into annual distribution points and seasonal distribution points. By integrating multi-factor environmental data, including climate, soil, topography, and vegetation, we used MaxEnt to establish annual and monthly models. The annual model extracted the annual suitability distribution of FAW worldwide. Among the nine selected environmental factors, temperature seasonality had the greatest impact on the suitability distribution of FAW, with a single-factor contribution rate of 39.87%. The monthly models analyzed the inter-monthly variations in the global suitability distribution of FAW from January to December. The results indicated that FAW’s suitability was highest in July and lowest in March. Under the dominant influence of dynamic environmental factors such as temperature, precipitation, and vegetation index, the expansion and contraction of FAW’s suitability distribution corresponded with seasonal changes, exhibiting significant seasonal fluctuations. Our results can provide FAW control personnel with more practical references for formulating preventive strategies in advance, helping to prevent the potentially incalculable damage FAW could cause to crops in invaded areas.

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引用次数: 0

A land-cover-assisted super-resolution model for retrospective reconstruction of MODIS-like NDVI data across the continental United States by blending Landcover300m and GIMMS NDVI3g data

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113176

Zhicheng Zhang , Zhenhua Xiong , Xuewen Zhou , Kun Xiao , Wei Wu , Qinchuan Xin

Long-term archives of remote sensing data hold values for identifying temporal changes occurring on the land surface. Moderate-spatial-resolution data acquired by sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) have proven useful in large-scale studies. The absence of such data prior to the launch of MODIS in 2000 necessitates the retrospective reconstruction of MODIS-like datasets. While data fusion techniques are capable of generating spatiotemporally continuous data, challenges remain in capturing interannual variation of land surface dynamics at a spatial resolution where real observation data are lacking. This study introduces a novel deep learning-based model, termed the Land-Cover-assisted Super-Resolution SpatioTemporal Fusion model (LCSRSTF), designed to produce biweekly 500-meter MODIS-like data spanning from 1992 to 2010 across the Continental United States (CONUS). LCSRSTF integrates Landcover300m and the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI3g data. The model exacts moderate-resolution class features from annual Landcover300m data at the target year, incorporates GIMMS NDVI3g time series to capture seasonal fluctuations, and employs the Long Short-Term Memory (LSTM) method to mitigate sensor differences. Evaluation against observed MODIS images confirms the robustness of our model in generating MODIS-like data across CONUS. The root mean square error (RMSE) of the model results is 0.094 from 2001 to 2010, while that of GIMMS NDVI3g data is 0.154. The linear regression coefficient for the model simulation is 0.872, compared to 0.844 for GIMMS data. The model exhibits reasonable predictive capabilities in reconstructing retrospective data when assessed using Landsat data prior to 2000. The developed method as well as the MODIS-like dataset spanning from 1992 to 2010 across CONUS hold the promise in extending the temporal span of moderate-spatial-resolution data, thereby facilitating comprehensive long-term studies of land surface dynamics.

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引用次数: 0

How low-abundance amphibians shape functional diversity across tropical forest succession stages?

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113140

Fabio Zabala-Forero , Angela M. Cortés-Gómez , Nicolás Urbina-Cardona

The role of low-abundance species in ecosystems remains largely understudied, despite their unique trait values and significant influence on biotic interactions and ecosystem processes. Trait-based ecology provides key insights into the impact of these species on functional diversity metrics, particularly in landscapes undergoing ecological succession after land abandonment in tropical forests. This study evaluates the effects of sequentially losing low-abundance species on amphibian functional diversity in the Colombian Pacific region. We applied trait probability density (TPD) analysis at the assemblage level and functional rarity indices (Scarcity and Functional Distinctiveness) at the species level. By simulating scenarios of low-abundance species loss, we assessed changes in functional diversity metrics—such as richness, evenness, divergence, and redundancy—across three vegetation types: mature forest, secondary forest, and abandoned mixed crops. Our findings revealed significant variations in functional diversity metrics. Functional richness was sensitive to both vegetation types and species loss scenarios, while functional evenness and redundancy responded more specifically to the loss of low-abundance species. Correlation analyses showed significant relationships between species richness and functional diversity metrics. Interestingly, species with the highest Scarcity and Functional Distinctiveness values, which also had the lowest abundances, were the first to be lost in the scenarios. These results underscore the vital importance of low-abundance amphibian species in maintaining functional diversity and advocate for conservation strategies prioritizing these vulnerable species and their habitats. Our study revealed the intricate relationship between species richness and facets of functional diversity under scenarios of local extinction and anthropogenic land cover transitions in tropical ecosystems.

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引用次数: 0

Redefining seasons: Dynamic meteorological delineation unveils novel patterns in vegetation phenology responses to climate change

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113214

Xinyi Fan, Zhixin Hao, Yang Liu

Vegetation phenology is a sensitive indicator of climate change, and the impact of large-scale meteorological seasonal variations on phenological patterns remains understudied because traditional seasonal divisions fail to capture the dynamics of rapid phenological change. Utilizing CN05 gridded temperature data, we applied a dynamic meteorological model to analyze the spatial distribution and trends of spring and autumn, revealing their influence on regional vegetation phenology. We revealed that the start of spring and end of spring advanced at rates of −0.19 d·yr⁻¹ and −0.15 d·yr⁻¹, respectively, affecting 92.57 % and 82.85 % of the study area. Conversely, the start and end of autumn were delayed by + 0.06 d·yr⁻¹ and + 0.10 d·yr⁻¹, impacting 54.05 % and 89.96 % of the region. The length of spring and length of autumn increased at rates of + 0.05 d·yr⁻¹ and + 0.02 d·yr⁻¹, respectively, across 55.93 % and 53.97 % of the area. These changes exhibited a clear latitudinal gradient, with a decreasing duration from south to north. Significant correlations were observed between seasonal variations and vegetation phenology; earlier spring onset corresponded to an earlier start of the growing season in 71.58 % of the study area, while a later end of autumn correlated with a delayed end of the growing season in 59.72 % of the region. This study systematically demonstrates, for the first time, the extensive influence of climate-driven seasonal changes on vegetation phenology, offering valuable insights for weather forecasting, climate zoning, and phenology management.

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引用次数: 0

Digital mapping of soil organic carbon in a plain area based on time-series features

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113215

Kun Yan , Decai Wang , Yongkang Feng , Siyu Hou , Yamei Zhang , Huimin Yang

Improving the accuracy of digital soil organic carbon (SOC) mapping in plain areas is important for meeting the needs of agricultural development and environmental protection. Utilizing time-series environmental factors is thought to be helpful in digital soil mapping (DSM) of SOC, which is a current research hotspot. This study focused on the DSM of SOC in Fengqiu County, China, using terrain, climate, single-time ecological factors, and time-series features of time-series ecological factors as environmental covariates to investigate whether time-series environmental covariates could improve the accuracy in a plain area. SOC prediction models were established using random forests (RF), backpropagation neural networks (BP), and support vector machines (SVM). The results showed that ecological factors such as normalized difference vegetation index (NDVI) normalized difference built-up index (NDBSI), drought, and humidity indices, along with distance from rivers, played a dominant role in digital SOC mapping. The relative importance of the time-series features of the ecological factors was higher than that of the single-time-point vegetation indices. Introducing the time-series features of ecological factors resulted in a decrease in the mean error (ME) and root mean square error (RMSE), whereas the coefficient of determination (R²) and concordance correlation coefficient (CCC) showed increasing trends across the different models. Comparing the various environmental variable screening methods, the Boruta algorithm achieved the most significant improvement in model accuracy. The RFSTB (RF + Conventional variables + Time-series variables + Boruta algorithm) model was identified as the optimal model, with R² increasing by 65.45 % and RMSE decreasing by 47.12 %. This study introduces new environmental covariates for SOC mapping and provides new insights into digital mapping of SOC in plain areas.

{"title":"Digital mapping of soil organic carbon in a plain area based on time-series features","authors":"Kun Yan , Decai Wang , Yongkang Feng , Siyu Hou , Yamei Zhang , Huimin Yang","doi":"10.1016/j.ecolind.2025.113215","DOIUrl":"10.1016/j.ecolind.2025.113215","url":null,"abstract":"<div><div>Improving the accuracy of digital soil organic carbon (SOC) mapping in plain areas is important for meeting the needs of agricultural development and environmental protection. Utilizing time-series environmental factors is thought to be helpful in digital soil mapping (DSM) of SOC, which is a current research hotspot. This study focused on the DSM of SOC in Fengqiu County, China, using terrain, climate, single-time ecological factors, and time-series features of time-series ecological factors as environmental covariates to investigate whether time-series environmental covariates could improve the accuracy in a plain area. SOC prediction models were established using random forests (RF), backpropagation neural networks (BP), and support vector machines (SVM). The results showed that ecological factors such as normalized difference vegetation index (NDVI) normalized difference built-up index (NDBSI), drought, and humidity indices, along with distance from rivers, played a dominant role in digital SOC mapping. The relative importance of the time-series features of the ecological factors was higher than that of the single-time-point vegetation indices. Introducing the time-series features of ecological factors resulted in a decrease in the mean error (ME) and root mean square error (RMSE), whereas the coefficient of determination (R<sup>2</sup>) and concordance correlation coefficient (CCC) showed increasing trends across the different models. Comparing the various environmental variable screening methods, the Boruta algorithm achieved the most significant improvement in model accuracy. The RFSTB (RF + Conventional variables + Time-series variables + Boruta algorithm) model was identified as the optimal model, with R<sup>2</sup> increasing by 65.45 % and RMSE decreasing by 47.12 %. This study introduces new environmental covariates for SOC mapping and provides new insights into digital mapping of SOC in plain areas.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113215"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative contribution of climate change and vegetation restoration to ecosystem services in the Inner Mongolia under ecological restoration projects

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113240

Yang Zhang , Xiaoya Chen , Yun Zhang , Bo Wang

In the context of ongoing ecological restoration efforts, understanding the impacts of vegetation restoration on ecosystem services (ESs) is critical for sustainable ecosystem management. However, the quantitative contributions of vegetation restoration and climate change to ESs, as well as the relationship between vegetation restoration and ESs, remain insufficiently explored. This study focuses on the Inner Mongolia Section of the Beijing-Tianjin Sandstorm Source Control Project Area (IM-BTSSCPA) to address these gaps. The temporal and spatial dynamics of total ecosystem services (TES) in the IM-BTSSCPA from 2001 to 2020 were evaluated. The contributions of climate and Human Appropriation of NDVI (HANDVI) to TES were quantified, and the nonlinear constraint thresholds of NDVI on TES were identified. Key findings include: (1) TES improved significantly from 2001 to 2020, with a spatial trend of increasing from the northwest to the southeast; (2) HANDVI was identified as the primary driver of TES improvement, contributing 63.23%; and (3) NDVI exhibited nonlinear constraint effects on sand fixation, water yield, and TES, with respective thresholds of 0.32, 0.49, and 0.79. These findings suggest the need for multi-scale eco-spatial management and planning strategies, and offering valuable guidance for the implementation of sustainable ecological restoration projects.

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引用次数: 0

Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113167

Jean Stephan, Melissa Korban

Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.

This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.

MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.

The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. All models showed satisfactory results, with the third model with a non-cropped background and an exhaustive list of predictors showing the highest performance and providing accurate maps, especially when compared to the first run with a cropped background around the riparian zone and the omission of distance from the riverbank and the sea from the predictors used. Predictors such as the river flow regime, the distance from the riverbank, the Emberger Quotient, and the mean of the minimal temperature of the coldest month were essential for the predicted distribution of the selected species.

The order of contribution of each predictor in the model enabled us to validate the grouping of species into obligate and non-obligate riparian and conclude which predictors to select for ENM based on the species’ nature. The results could be suggested for red listing assessment of riparian tree species and appropriate species selection for ecosystem restoration.

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引用次数: 0

Attraction points: A new sampling design method to quantify common finches’ population

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-02-01 DOI: 10.1016/j.ecolind.2025.113155

Lorenzo Marazuela Pinela , Ángel Julián Martín Fernández , Pablo-Luis López-Espí

Existing methodologies for monitoring bird populations primarily focus on their presence, habitat, physical and health characteristics, and, more comprehensively, their abundance and trends in breeding season. Due to the fact that these methodologies do not use unbiased statistical estimators, the accurate estimation of bird densities or obtaining measures of abundance is often based on indirect counts, such as, detection probability versus distance, or assumptions, such as the homogeneous distribution of all individuals, which are then correlated with bird density. Most of these counts provide a multispecies approximation of long-term trends but may be inaccurate for specific species, regions or outside of the breeding season. In the case of finches (Fringillidae), determining a direct and more accurate estimate of density of their populations in a specific area requires a specialised methodology based on unbiased statistical estimators, one that accounts on the unique characteristics of these birds and minimises the issues associated with commonly applied multispecies methodologies.

Currently, no quantitative population management method exists that enables wildlife managers to ensure the sustainable management of finch populations in a specific territory throughout their annual life cycle. As part of the development of a quantitative method for this purpose, this study introduces a population inventory model based on fixed points utilising live decoy birds of the fringillid species being inventoried. The model leverages the ethological characteristics of this bird family, which are non-territorial and seeks the company of conspecifics, which they attract through song. Consequently, its applicability is limited to other bird taxa with similar behavioural traits.

The “Attraction Points Method” (APM), which uses the songs of live congeners, as described here, has been employed and evaluated in several projects for tracking finch populations in Spain, particularly in the Community of Madrid, where a continuous study of their populations has been conducted since 2018. The study was planned in three periods: the beginning and end of the breeding season (pre-nuptial and post-nuptial), and wintering, allowing for the calculation of survival, mortality and reproductive success ratios.

This article presents the model, the results obtained from its application, and its advantages and disadvantages for the control and sustainable management of fringillid populations compared to other frequently used sampling models.

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引用次数: 0