Ecological Informatics最新文献

{"title":"LEAVES: An open-source web-based tool for the scalable annotation and visualisation of large-scale ecoacoustic datasets using cluster analysis","authors":"Thomas Napier ,&nbsp;Euijoon Ahn ,&nbsp;Slade Allen-Ankins ,&nbsp;Lin Schwarzkopf ,&nbsp;Ickjai Lee","doi":"10.1016/j.ecoinf.2025.103026","DOIUrl":"10.1016/j.ecoinf.2025.103026","url":null,"abstract":"<div><div>Ecoacoustics has emerged as a pivotal discipline in the conservation and monitoring of ecosystems, offering insights into species’ behaviour and ecosystem health through soundscape analysis. Central to this is the need for accurate annotations of environmental audio recordings, which underpin the computational models used in ecological monitoring. However, due to the increasingly large scale of datasets, annotation using existing tools and techniques cannot be performed at feasible speeds or with the necessary accuracy required for real-world application. The LEAVES (Large-scale Ecoacoustics Annotation and Visualisation with Efficient Segmentation) platform addresses this gap by leveraging unsupervised clustering techniques optimised for the high-throughput annotation of large-scale ecoacoustics datasets. Our evaluation across six real-world datasets shows that LEAVES improves annotation efficiency by up to 7.12 times compared to manual annotation while maintaining 79%–90% label similarity to validated data. We expect that our proposed tool will greatly accelerate the annotation process when generating high-quality labelled datasets, supporting larger-scale studies with broader community engagement in ecoacoustics research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103026"},"PeriodicalIF":5.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480761","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}

{"title":"Evaluating effects of data quality and variable weighting on habitat suitability modelling","authors":"Stephanie Arsenault ,&nbsp;Robyn Linner ,&nbsp;Yong Chen","doi":"10.1016/j.ecoinf.2025.103086","DOIUrl":"10.1016/j.ecoinf.2025.103086","url":null,"abstract":"<div><div>Habitat modelling is important in the conservation and management of fishes and can be sensitive to data inputs and model configuration. Survey data used in Habitat Suitability Index (HSI) models may undergo changing sampling protocols over time, and these inconsistencies may impact results. Additionally, the various habitat variables included in HSI models are typically given equal weights, even though some variables may have greater influence over distribution than others. The Long River Survey, part of the Hudson River Biological Monitoring Program, in the Hudson River Estuary (HRE), has undergone considerable protocol changes, and was calibrated to address these issues in 2023. This survey and region are an excellent case study to compare two approaches in constructing HSI models: using calibrated versus uncalibrated abundance data and weighting all environmental variables equally or using a model-based weighting method. The results of this study suggest that using calibrated abundance data with unweighted habitat variables provide the most robust estimates for bay anchovy suitable spawning habitat in the HRE, which indicates that in cases when sampling has not been consistent over time, using calibrated abundance data in habitat suitability modelling may lead to improved models. Some model configurations were unable to identify a significant trend in suitable habitat over time and overestimated habitat quality illustrating the importance of carefully considering data inputs and model configuration when building habitat models to properly quantify suitable habitat and contribute to ecosystem-based fisheries management in the wake of climate change.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103086"},"PeriodicalIF":5.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465028","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}

{"title":"Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting","authors":"Xu Zhai ,&nbsp;Yuzhong Liu ,&nbsp;Yuanyuan Hong ,&nbsp;Yunjie Yang ,&nbsp;Pengju Wang ,&nbsp;Zhicheng Ye ,&nbsp;Xiaoyan Liu ,&nbsp;Tianlong She ,&nbsp;Lihui Wang ,&nbsp;Chen Xu ,&nbsp;Lili Zhang ,&nbsp;Qiang Wang","doi":"10.1016/j.ecoinf.2025.103083","DOIUrl":"10.1016/j.ecoinf.2025.103083","url":null,"abstract":"<div><div>Existing soil texture mapping methods cannot accurately predict soil texture in complex geographical environments. To address this challenge, we propose a method that combines a kernel temperature–vegetation dryness index (kTVDI) with a gradient boosting algorithm to accurately predict the spatial distribution of soil texture. In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. We validated model performance by mapping the spatial distributions of sand, silt, and clay particle fractions in the city (30-m resolution), using the boosting algorithms adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). Among the environmental covariates, the kTVDI, digital elevation index, and salinity index have the highest importance values for soil texture prediction. The kTVDI is better for sand and silt prediction (especially sand). When combined with AdaBoost, the kTVDI can effectively improve the accuracy and consistency of the prediction model. Uncertainty analyses showed that the kTVDI was more effective at modeling soil texture in the plains. In summary, we present a new approach for accurately predicting the spatial distribution of soil texture and empirically validate its effectiveness and advantages for practical applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103083"},"PeriodicalIF":5.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453727","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}

{"title":"A theoretical model of plant species competition: The case of invasive Carpobrotus sp. pl. and native Mediterranean coastal species","authors":"Simonetta Bagella,&nbsp;Iulia Martina Bulai ,&nbsp;Marco Malavasi,&nbsp;Giulia Orrù","doi":"10.1016/j.ecoinf.2025.103070","DOIUrl":"10.1016/j.ecoinf.2025.103070","url":null,"abstract":"<div><div>In this paper we introduce and study a new mathematical model that describes the interaction between invasive and native plants in competition for resources and space. The first are represented by <em>Carpobrotus</em> sp. pl. while the second by the native coastal area species. Following the terminology used in modeling, we have used the term “population” to refer to the set of plants, i.e. native and alien invasive. Differently than in a classical Lotka–Volterra competition model, here we assume that the invasive species grow following a generalized logistic growth law while the native population a logistic one. Moreover, in the competition term we take into account the fact that the two populations of plant interact in a 3D space and the interaction occurs on the borders of the vegetation patch and/or volume, meaning that both densities will be characterized also by two exponents. A complete mathematical analysis of the proposed model was done by studying also some particular cases of it. The model is characterized by the presence of different equilibrium points such as the trivial equilibrium point, where both populations get extinct, the Carpobrotus-free equilibrium, the native-plant-free equilibrium and the coexistence, where both plant populations coexists. Computational simulations show that different bistability scenarios exist but also the tristability of the last three equilibrium introduced above was studied. The obtained results suggest that external factors, due to human intervention, that leads to a decrease in the initial population of <em>Carpobrotus</em> sp. pl. and/or increase of the native population might help in having the stability of the Carpobrotus-free equilibrium or the coexistence equilibrium.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103070"},"PeriodicalIF":5.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471478","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}

{"title":"Suitability of the Amazonas region for beekeeping and its future distribution under climate change scenarios","authors":"Darwin Gómez-Fernández ,&nbsp;Ligia García ,&nbsp;Jhonsy O. Silva-López ,&nbsp;Jaris Veneros Guevara ,&nbsp;Erick Arellanos Carrión ,&nbsp;Rolando Salas-Lopez ,&nbsp;Malluri Goñas ,&nbsp;Nilton Atalaya-Marin ,&nbsp;Manuel Oliva-Cruz ,&nbsp;Nilton B. Rojas-Briceño","doi":"10.1016/j.ecoinf.2025.103082","DOIUrl":"10.1016/j.ecoinf.2025.103082","url":null,"abstract":"<div><div>Beekeeping plays an important role in global food production and the conservation of wild species. However, determining territorial suitability and future distribution under climate change scenarios is a relatively understudied area in Peru. This study assessed the beekeeping suitability of the Amazonas region and its variation under climate change scenarios in two projected periods (2041–2060 and 2081–2100), according to Shared Socioeconomic Pathways (SSP). The methodological framework integrated the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), and the Hadley Centre Global Earth Model - Global Coupled configuration 3.1 (HadGEM3-GC31-LL) was used for future climate analysis. The beekeeping suitability of the region was determined based on eleven criteria: four climatic, three topographic, and four environmental. The results indicate that beekeeping suitability is distributed as follows: 3.4 % (1417.90 km²) with ‘High’ suitability, 79.2 % (33,318.61 km²) with ‘Moderate’ suitability, 17.2 % (7230.26 km²) with ‘Marginal’ suitability, and 0.2 % (83.64 km²) as ‘Not suitable’. Moreover, the average temperature across the region is projected to increase by approximately 3 °C under the SSP2–4.5 scenario and between 6 °C and 8 °C under the SSP5–8.5 scenario during the projected periods. Precipitation will decrease in the northern part of the region, while the southwestern part will experience an increase. In the highly suitable beekeeping area, a temperature increases up to 10.8 °C is expected, with frequent variations around 3 °C to 8 °C, affecting more than 500 km². Additionally, a reduction in precipitation up to 311 mm/year is projected, with predominant variations ranging from −49.5 to −32.8 mm/year over approximately 600 km².Therefore, it is suggested to implement strategies to mitigate these upcoming challenges, particularly if the modeled economic development under the SSPs continues. This study modeled and mapped areas with present conditions suitable for beekeeping and future climate behavior. The modeling aims to guide beekeepers and local authorities in developing sustainable practices and implementing preventive measures to address future climatic challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103082"},"PeriodicalIF":5.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453656","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}

{"title":"Grassland management and phenology affect trait retrieval accuracy from remote sensing observations","authors":"Maksim Iakunin ,&nbsp;Franziska Taubert ,&nbsp;Reimund Goss ,&nbsp;Severin Sasso ,&nbsp;Hannes Feilhauer ,&nbsp;Daniel Doktor","doi":"10.1016/j.ecoinf.2025.103068","DOIUrl":"10.1016/j.ecoinf.2025.103068","url":null,"abstract":"<div><div>Grasslands, the most widespread terrestrial biome, are subject to various management practices that influence their biodiversity and ecological functions. Remote sensing offers a promising tool for monitoring these impacts, but challenges persist in heterogeneous grassland systems. This study combines radiative transfer model (RTM) and machine learning algorithms to assess the efficacy of the model inversion in predicting plant functional traits under different grassland management regimes. The model was applied to intensively and extensively managed grasslands using field-collected hyperspectral data. Results show that while RTM inversion effectively predicts traits such as leaf area index (LAI) and pigment concentrations in homogeneous, intensively managed systems, its accuracy diminishes in diverse, extensively managed grasslands, particularly for traits like leaf mass per area (LMA) and pigment content. These limitations stem from the model’s assumption of homogeneous canopy scattering, which fails to account for the heterogeneity in mixed green and brown vegetation, especially at senescence. Despite these challenges, the study highlights the potential of hyperspectral remote sensing to capture grassland management based on a solely empirical approach. Future research should focus on refining models to better account for canopy heterogeneity and integrating in-situ data to improve trait prediction in complex ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103068"},"PeriodicalIF":5.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465027","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}

{"title":"Understanding the ecological impacts of vertical urban growth in mountainous regions","authors":"Lujin Zhang ,&nbsp;Yong Wang ,&nbsp;Hongyan Bian ,&nbsp;Jie Gao ,&nbsp;Zhenzhen Yuan ,&nbsp;Zixuan Wang ,&nbsp;Yixuan Dai ,&nbsp;Haimeng Liu","doi":"10.1016/j.ecoinf.2025.103079","DOIUrl":"10.1016/j.ecoinf.2025.103079","url":null,"abstract":"<div><div>Approximately 35 % of Chinese cities are situated in mountainous areas, where rapid urban expansion on slopes has led to the degradation of urban ecosystem health, marked by the loss of natural landscapes, diminished resilience, and reduced ecosystem services. However, the impact of vertical urban growth on ecosystem health, especially in terms of temporal dynamics, distribution, and underlying mechanisms, remains poorly understood. Chongqing, a typical mountainous metropolis, was selected to investigate these impacts over the past two decades. By integrating the slope spectrum (SS), climbing index (CI) of built-up land, and ecosystem health index (EHI), we explored how urban expansion has affected the EHI. Additionally, we developed a method for identifying the expansion advantage slope range (EASR), which can track dynamic slope-climbing urban expansion. Our findings revealed that the built-up land expansion in Chongqing was primarily concentrated in the central areas of the western region. From 2000 to 2010, this expansion occurred primarily in urban built-up areas and shifted to other built-up areas between 2010 and 2020. Although the Climbing Index was −0.11, the EASR results indicated an increasingly significant slope-climbing trend, with slopes of built-up land increasing from [1.17, 8.25] to [1.42, 8.48]. Notably, when expansion occurs on slopes exceeding 12°, the decline in the EHI becomes significantly more pronounced, with the impact coefficient rising from 0.201 to 0.447. This study reveals the fundamental relationship between slope-climbing urban expansion and ecosystems, providing valuable insights for urban planning and sustainable development in mountainous regions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103079"},"PeriodicalIF":5.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453717","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}

{"title":"Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors","authors":"Marcin Kluczek,&nbsp;Bogdan Zagajewski","doi":"10.1016/j.ecoinf.2025.103074","DOIUrl":"10.1016/j.ecoinf.2025.103074","url":null,"abstract":"<div><div>Climate change is increasing the frequency of extreme events, including those in forests. One of the major drivers of forest change in Europe is the bark beetle, which causes large-scale annual changes in spruce forest areas. Mountain forests are particularly vulnerable as changes occur rapidly and require long-term monitoring of ongoing ecological changes. For this purpose, a 10-year time series of Sentinel-2 optical satellite data fused with Sentinel-1 radar and topographic derivatives was applied to the natural forests of the Tatra Mountains in Central Europe. Based on machine learning algorithms and iterative methods, overall classification accuracies of 0.94–0.96 and snags with an F1-score of 0.81–0.98 were achieved. The highest spruce mortality rate was observed in 2018, with extensive snag areas persisting until 2024. This study revealed that smaller infestation patches (&lt; 0.1 ha) consistently dominated the landscape, peaking in 2018, whereas larger patches (&gt; 0.5 ha) showed a declining trend, particularly after 2020. The variable importance analysis revealed that topographic factors are critical for predicting forest disturbance patterns. Elevation emerged as the most significant predictor with a Mean Decrease in Accuracy ranging from 95 to 150, followed by slope and aspect. Snag occurrence was strongly influenced by elevation, ranging from 700 to 1700 m a.s.l., with the median elevation increasing from 1150 m in 2015 to 1400 m in 2024. The slope also played an important role, with the median slopes for snag occurrences ranging from 15° to 25°, indicating a tendency for mortality on moderate inclines, although mortality on steeper slopes (up to 50°) was occasionally observed, particularly in 2017 and 2023. Regarding the slope orientation, the southeastern and eastern aspects consistently experienced higher proportions of spruce mortality (particularly between 2017 and 2021). A strong correlation between spruce mortality and temperature-related variables was identified, particularly degree days above 5 °C and 8.3 °C during key months (April, June, and July). Median yearly air temperature showed a correlation, whereas precipitation-related variables, including the Standardised Precipitation Evapotranspiration Index (SPEI), exhibited negative correlations, particularly the SPEI 01 median. These findings improve the understanding of long-term forest changes caused by disturbances and provide key insights for the data-driven management of protected forests in a changing climate.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103074"},"PeriodicalIF":5.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428748","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}

{"title":"Soil moisture dominates gross primary productivity variation during severe droughts in Central Asia","authors":"Tao Yu ,&nbsp;Guli Jiapaer ,&nbsp;Anming Bao ,&nbsp;Ye Yuan ,&nbsp;Jiayu Bao ,&nbsp;Tim Van de Voorde","doi":"10.1016/j.ecoinf.2025.103076","DOIUrl":"10.1016/j.ecoinf.2025.103076","url":null,"abstract":"<div><div>The onset of severe drought is usually accompanied by anomalies in soil moisture (SM) and climatic factors such as temperature (Tem), solar radiation (Srad), and vapor pressure defict (VPD). These factors are critical for gross primary productivity (GPP) as they directly influence photosynthesis. Central Asia (CA), characterized by a dry climate and water scarcity, frequently experiences severe droughts, leading to a decrease in GPP. However, how anomalies in SM and climatic factors affect GPP in CA, and the dominant factors causing the decline in GPP during severe droughts, remain unclear. Here we first identified severe droughts in CA from 2000 to 2021 using the self-calibrated Palmer Drought Severity Index (scPDSI) and then investigated the effects of anomalies in SM, Tem, Srad, and VPD on solar-induced chlorophyll fluorescence (SIF) and GPP under severe drought conditions by light-use efficiency P-model and the Random Forest (RF) algorithm. The results show that the severe droughts in CA from 2000 to 2021 occurred in 2008 and 2021, with scPDSI values of −3.26 and − 3.94, respectively. In 2008 and 2021, SIF and GPP showed significant decreases: the area of negative anomalies in SIF and GPP was more than 70 % of CA. The spatial pattern of SM anomalies is consistent with that of SIF and GPP. P-model simulations indicate that SM deficits dominated the decline in GPP in 2008 and 2021, affecting regions covering 31 % and 17 % of CA, respectively, with GPP reductions exceeding 5 %. RF predictions also indicated that during severe drought, SM had a significant effect on GPP, while Srad, Tem, and VPD had a slight effect on GPP.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103076"},"PeriodicalIF":5.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428686","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}

{"title":"Rice cropping sequence mapping in the tropical monsoon zone via agronomic knowledge graphs integrating phenology and remote sensing","authors":"Hongzhang Nie ,&nbsp;Yingchen Lin ,&nbsp;Wenfei Luo ,&nbsp;Guilin Liu","doi":"10.1016/j.ecoinf.2025.103075","DOIUrl":"10.1016/j.ecoinf.2025.103075","url":null,"abstract":"<div><div>Rice cropping sequence mapping via multitemporal remote sensing and agronomic techniques provides critical geoinformatics for agroecosystem modeling. The East Asian tropical monsoon region is an important rice-growing area, and the regional food security depends on efficient rice mapping. Frequent cloud cover during the rainy season leads to insufficient available optical remote sensing images that cover the growth stages of rice, which renders remote sensing-derived rice identification difficult. Thus, we proposed a simple and efficient strategy from an agronomic knowledge graph perspective in which specific phenological events of rice and Landsat/Sentinel-2 time series were employed to extract different rice cropping sequences on the Leizhou Peninsula (China). Then, a control group and five experimental groups were established by integrating spectral features via pixel- and object-based random forest (RF) algorithms. The results revealed that five key phenological events could be obtained for rice cropping sequence identification in the study area. The overall accuracy of the pixel-based classification results ranged from 83.48 % to 92.49 %, whereas that of the object-based classification results ranged from 84.98 % to 92.80 %. These findings indicate that efficient rice cultivation mapping via optical remote sensing data requires the selection of specific time windows corresponding to phenological events to benefit rice cultivation monitoring and regional agroecosystem sustainability.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103075"},"PeriodicalIF":5.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520517","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}