Regional Vegetation Research Articles

An Evaluation of the Performance of Remote Sensing Indices as an Indication of Spatial Variability and Vegetation Diversity in Alpine Grassland

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing grassland vegetation diversity. In this study, the relationship between remote sensing indices and species diversity was investigated at varying spatial and temporal scales in Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as the coefficient of variation in vegetation indices, was used as a proxy for species diversity, which was quantified using species diversity indices. The “spectral diversity-species diversity” relationship was validated across diverse spatial scales and between different years using Sentinel-2 images and ground investigation data. This study found that Kendall’s τ coefficients showed the best performance in evaluating the relationship between the coefficient of variation in VIs (CVVIs) and species diversity index. The highest τ value was observed for CVNDVI in 2017 (τ = 0.660, p < 0.01), followed by the Shannon index in 2018 (τ = 0.451, p < 0.01). In addition, CVEVI demonstrated a significant positive correlation with the Shannon-Wiener Index at the 50 m scale (τ = 0.542), and the highest relationship τ between CVNDVI and the Shannon-Wiener Index was observed at the 100 m scale (τ = 0.660). The Shannon-Wiener Index in relation to CVVIs performs better in representing changes in grassland vegetation. Spatial scales and vegetation indices influence the assessment of grassland vegetation diversity. These findings underscore the critical role of remote sensing technology in assessing grassland vegetation diversity across various scales, offering valuable support tools for measuring regional grassland vegetation diversity.

Analysis of vegetation and associations of Amorphophallus beccarii in Tarlola Village, Batang Natal District, Mandailing Natal Regency

Abstract Amorphophallus is a genus of Araceae with 170 species distributed throughout the world, 25 species grow in Indonesia and 18 are endemic. Tarlola Village is one of the habitats of Amorphophallus beccarii. This research aims to analyze the distribution of Cindings and structure of A. beccarii in Tarlola Village and analyze the association of A. beccarii with other plants. The A. beccarii habitat study was carried out using the Nested method with a plot area of 1 ha and a square shape taken from the A. beccarii plot. The data taken is regional vegetation, including tree vegetation calculated on 1 ha plots, pole vegetation on 0.25 ha plots, sapling vegetation on 0.0625 ha plots, and seedling and understory vegetation calculated on 0.01 ha plots. Based on the research results, it was found that there were 30 A. beccarii individuals. The highest Important Value Index (IVI) was obtained at the understorey level with Selaginella deoderleinii 57.39%. The association results found at the lower plant level were 13 species with positive associations and 15 species with negative associations, at the seedling level there were 12 species with positive associations and 14 species with negative associations, at the sapling level there were 12 species with positive associations and 19 species with negative associations, at the pole level there were 9 species. positively associated and 15 negatively associated species, at tree level there are 14 positively associated species and 9 negatively associated species.

Additional far-red increases fruit yield of greenhouse sweet pepper mainly through enhancing plant source strength

Supplementary lighting is necessary for year-round greenhouse production of fruit vegetables in high-latitude regions. Far-red (FR) radiation can influence plant photomorphogenesis as well as photosynthesis. We aimed to identify the effects of supplementary FR on fruit set and yield of sweet pepper, and its underlying mechanisms via a yield component analysis. A 24-week greenhouse experiment was conducted with cultivars 'Gialte' and 'Margrethe', where FR was added to 190 μmol·m−2·s−1 of white supplementary light in four treatments: 0, 50 or 100 μmol·m−2·s−1 FR throughout the whole generative growth phase (since 8 weeks after transplanting), or 100 μmol·m−2·s−1 FR for only four weeks (12 to 16 weeks after transplanting). Fruit yield increased linearly with the cumulative amount of FR provided in supplementary light. The increased fruit dry weight with additional FR was mainly associated with an increased plant dry weight, accompanied by a marginal increase in the fraction of dry matter partitioned to fruits. The increase in plant dry weight resulted from an increased light use efficiency (plant dry weight per unit of supplementary photosynthetic active radiation (PAR) incident on top of the canopy) and an increased incident PAR (due to taller plants closer to the lamps). However, additional FR reduced radiation use efficiency (plant dry weight per unit of supplementary PAR plus FR incident on top of the canopy), indicating that additional FR was used less efficiently than PAR for biomass production. Additional FR enhanced fruit set percentage and fruit set fluctuations over time, where both long-term and short-term FR application elevated the subsequent fruit set peak after the start of FR application. Without additional FR, 17% fruits in 'Gialte' and 25% in 'Margrethe' showed medium or severe cracking. Additional FR substantially reduced this percentage to 8% in both cultivars. We conclude that additional FR improves sweet pepper fruit set and yield in greenhouses, mainly by enhancing plant source strength.

The interplay between prehistoric vegetation, climatic fluctuations and anthropogenic activities in Central China

The interaction between climate, hydrological environment, and human activities in prehistoric times has consistently attracted significant interest. However, the process and mechanism of this interaction remain unclear due to a lack of detailed materials that provide information on both human activity and environmental evolution. Here we present a comprehensive dataset of optically stimulated luminescence (OSL) dating and pollen analysis derived from lacustrine facies deposits in the vicinity of the Dongzhao Site in Central China, which is a key region for the origin and development of Chinese civilization. These data have revealed the existence of multiple cycles of environment-human interaction during prehistoric periods. The gradual improvement in the climate and minor fluctuations facilitated the settlement of prehistoric populations in the region, fostering uninterrupted cultural development for millennia since the early Holocene. The increase in population and intensification of human activities significantly affected regional vegetation, resulting in a decrease in the number of trees around 4.0 ka BP. Environmental changes influenced the prehistoric architectural style, resulting in a decrease in houses with “wood bone and mud walls” that were prevalent during the Yangshao periods (7.0–5.0 ka BP) due to a lack of trees. Our research once again highlights the perpetual cycle of interdependence and interaction between natural environments and human activities.

Modern Pollen–Vegetation Relationships: A View from the Larch Forests of Central Siberia

Understanding how pollen assemblages represent the local and regional vegetation composition is crucial for palaeoecological research. Here, we analyze 102 surface moss/soil pollen samples collected from four study regions located in various boreal forest vegetation types in Central Siberia. Despite Larix being the most prevalent tree generus in the study area, the proportion of Larix pollen can be as low as 0.6–1.5% (0.4–4.7% on average) even in localities with a high canopy density of the species. No relationship between the quantity of Larix pollen in the spectra and the abundance of Larix in the local vegetation was found. The dominant components of the pollen assemblages are Betula and Alnus fruticosa. The pollen value of Picea is low (2.6–8.2% on average), with higher proportions at sample plots where spruce is abundant in forests. Pinus is a highly prevalent pollen species within its geographical range, comprising up to 40% of pollen assemblages. Outside of the range, the ratio of Pinus pollen was higher in habitats with low canopy density and in treeless ecosystems. The composition of herbaceous pollen and spores is significantly affected by the local plant community, offering more comprehensive insights into past vegetation patterns.

High-Resolution Remotely Sensed Evidence Shows Solar Thermal Power Plant Increases Grassland Growth on the Tibetan Plateau

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity.

The Impact of Seasonal Climate on Dryland Vegetation NPP: The Mediating Role of Phenology

Dryland ecosystems are highly sensitive to climate change, making vegetation monitoring crucial for understanding ecological dynamics in these regions. In recent years, climate change, combined with large-scale ecological restoration efforts, has led significant greening in China’s arid areas. However, the mechanisms through which seasonal climate variations regulate vegetation growth are not yet fully understood. This study hypothesizes that seasonal climate change affects net primary productivity (NPP) of vegetation by influencing phenology. We focused on China’s Windbreak and Sand-Fixation Ecological Function Conservation Areas (WSEFCAs) as representative regions of dryland vegetation. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate vegetation NPP from 2000 to 2020. To extract phenological information, NDVI data were processed using Savitzky–Golay (S–G) filtering and threshold methods to determine the start of season (SOS) and end of season (EOS). The structural equation model (SEM) was constructed to quantitatively assess the contributions of climate change (temperature and precipitation) and phenology to variations in vegetation NPP, identifying the pathways of influence. The results indicate that the average annual NPP in WSEFCAs increased from 55.55 gC/(m2·a) to 75.01 gC/(m2·a), exhibiting uneven spatial distribution. The pathways through which seasonal climate affects vegetation NPP are more complex and uneven. Summer precipitation directly promoted NPP growth (direct effect = 0.243, p < 0.001) while also indirectly enhancing NPP by significantly advancing SOS (0.433, p < 0.001) and delaying EOS (−0.271, p < 0.001), with an indirect effect of 0.133. This finding highlights the critical role of phenology in vegetation growth, particularly in regions with substantial seasonal climate fluctuations. Although the overall ecological environment of WSEFCAs has improved, significant regional disparities remain, especially in northwestern China. This study introduces causal mediation analysis to systematically explore the mechanisms through which seasonal climate change impacts vegetation NPP in WSEFCAs, providing new insights into the broader implications of climate change and offering scientific support for ecological restoration and management strategies in arid regions.

The Indo–Pacific Pollen Database – a Neotoma constituent database

Abstract. The Indo–Pacific Pollen Database (IPPD) is the brainchild of the late professor Geoffrey Hope, who gathered pollen records from across the region to ensure their preservation for future generations of palaeoecologists. This noble aim is now being fulfilled by integrating the IPPD into the online Neotoma Paleoecology Database, making this compilation available for public use. Here we explore the database in depth and suggest directions for future research. The IPPD comprises 226 fossil pollen records, most postdating 20 ka but with some extending as far back as 50 ka or further. Over 80 % of the records are Australian, with a fairly even distribution between the different Australian geographical regions, with the notable exception being Western Australia, which is only represented by three records. The records are also well distributed in the modern climate space, with the largest gap being in drier regions due to preservation issues. However, many of the records contain few samples or have fewer than five chronology control points, such as radiocarbon, luminescence or Pb-210, for the younger sequences. Average deposition time for the whole database, counted as years per centimetre, is 64.8 yr cm−1, with 61 % of the records having a deposition time shorter than 50 yr cm−1. The slowest deposition time by geographical region occurs on Australia's east coast, while the fastest times are from the western Pacific. Overall, Australia has a slower deposition time than the rest of the Indo–Pacific region. The IPPD offers many exciting research opportunities to investigate past regional vegetation changes and associated drivers, including contrasting the impact of the first human arrival and European colonisation on vegetation. Examining spatiotemporal patterns of diversity and compositional turnover/rate of change, land cover reconstructions, and plant functional or trait diversity are other avenues of potential research, amongst many others. Merging the IPPD into Neotoma also facilitates inclusion of data from the Indo–Pacific region into global syntheses.

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology.

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

Assessment of Vegetation Drought Loss and Recovery in Central Asia Considering a Comprehensive Vegetation Index

In the context of drought events caused by global warming, there is limited understanding of vegetation loss caused by drought and the subsequent recovery of vegetation after drought ends. However, employing a single index representing a specific vegetation characteristic to explore drought’s impact on vegetation may overlook vegetation features and introduce increased uncertainty. We applied the enhanced vegetation index (EVI), fraction of vegetation cover (FVC), gross primary production (GPP), leaf area index (LAI), and our constructed remote sensing vegetation index (RSVI) to assess vegetation drought in Central Asia. We analyzed the differences in drought experiences for different climatic regions and vegetation types and vegetation loss and recovery following drought events. The results indicate that during drought years (2012 and 2019), the differences in vegetation drought across climatic regions were considerable. The vegetation in arid, semiarid, and Mediterranean climate regions was more susceptible to drought. The different indices used to assess vegetation loss exhibited varying degrees of dynamic changes, with vegetation in a state of mild drought experiencing more significantly during drought events. The different vegetation assessment indices exhibited significant variations during the drought recovery periods (with a recovery period of 16 days: EVI of 85%, FVC of 50%, GPP of 84%, LAI of 61%, and RSVI of 44%). Moreover, the required recovery periods tended to decrease from arid to humid climates, influenced by both climate regions and vegetation types. Sensitivity analysis indicated that the primary climatic factors leading to vegetation loss varied depending on the assessment indices used. The proposed RSVI demonstrates high sensitivity, correlation, and interpretability to dry–wet variations and can be used to assess the impact of drought on vegetation. These findings are essential for water resource management and the implementation of measures that mitigate vegetation drought.

A spectral–structural characterization of European temperate, hemiboreal, and boreal forests

Abstract. Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both the structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal, and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), the forest floor (based on in situ measurements), and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at Hovi et al. (2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9) (for laboratory and field data) and Hovi et al. (2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f) (for airborne data).

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Differences in the Effects of Three Water Elements on Vegetation in Different Climatic Regions: Insights From the Yellow River Basin, China

ABSTRACTThe effects of water on vegetation have always been a concern. It is an important support as well as a major limiting factor with respect to vegetation growth. By analysing the spatiotemporal changes and correlations between precipitation (PRE), soil moisture (SM), vapour pressure deficit (VPD) and normalized difference vegetation index (NDVI) in the Yellow River Basin, we explored the different effects of three water elements on vegetation in different climatic regions. Our findings reveal the following: (1) NDVI and the three water elements report an increasing trend in the Yellow River Basin, with NDVI increasing most significantly. 92.1% of the Yellow River Basin showed an increase in NDVI. (2) Vegetation in the Yellow River Basin was most positively affected by PRE, followed by SM and VPD. PRE mainly affected the natural vegetation on both sides of the boundary between the arid and semi‐arid regions and the semi‐humid regions. SM mainly affected the natural vegetation in the arid and semi‐arid regions, whereas VPD mainly affected the crops in the irrigation areas, and the irrigation areas in arid regions were affected the most. These findings contribute to a deeper understanding of the relationship between water elements and vegetation.

Steppe development and mammalian adaptation in the middle Miocene, North Junggar Basin, Central Asia

The Central Asian (CA) steppe ecosystem and its fauna experienced a marked transition during the Miocene Climatic Optimum (MCO), though the exact process and dynamics remain elusive. We selected the Duolebulejin (DLB) section within the Junggar Basin to reconstruct vegetation and climate changes, using proxies such as particle size, mammalian fossils, and previously reported data including pollen, δ13CTOC, and magnetic susceptibility. The results reveal three distinct phases of transformation in regional vegetation and fauna from 18.8 to 13.2 Ma. During the pre-MCO (18.8–17.0 Ma), the landscape was dominated by desert scrubland, primarily inhabited by rodents. The MCO (17.0–14.5 Ma) saw a shift towards steppe vegetation, initially with herbaceous taxa followed by the establishment of C4 plants, alongside a diversity of large mammals adapted to open habitats. This suggests the emergence of savanna-like woodland steppes. In the post-MCO (14.5–13.2 Ma), the region transitioned to a dry steppe, with diminished animal populations, although a continued high proportion of species adapted to steppe environments. These findings underscore a co-evolutionary relationship between the development of steppe ecosystems and mammalian adaptations, driven by global MCO warming and subsequent cooling and with regional hydroclimatic changes acting as a modulating factor. The steppe-fauna biome emerged in the Junggar Basin during the MCO warming, a development whose mechanisms resemble those of tropical savannas in the late Miocene. These insights enhance our understanding of early steppe ecosystem evolution through past warming periods, shedding light on the pathways to modern steppe formation.

Reconstructing urban vegetation evolution in China using multimodal deep learning and 30-years Landsat archive

To retrospect the impact of urbanization on vegetation, consistent and reliable maps that provide urban vegetation estimates are required. In this study, we make the first attempt to employ deep learning for urban vegetation mapping of 2120 cities in China using a 30-years Landsat archive (UV-30). We present a multimodal deep learning (MDL) model to combine features from 5 categories, namely reflective bands, remote sensing indices, texture variables, topographical variables, and time labels. We normalized elevation data locally to remove the inter-city differences, while retaining topographic details. We used time labels to account for spectral variability over time and Landsat sensors. The performance of the MDL models was compared with a vanilla deep neural network, random forest, and support vector regression. Results indicated that the fusion of different categories of features improved the prediction accuracy. The MDL performed best in most validation cities, and our results were superior to the fractional maps aggregated from high-resolution datasets in homogeneous urban landscapes. We further analyzed urban vegetation dynamics across the urban core-new town-outskirt gradients of different geographical regions and city sizes. We found a gradually diminishing de-greening trend in urban China, especially in urban cores and new towns. Nevertheless, we observed a more pronounced degradation of urban vegetation in the south regions of China compared to the north, and smaller cities show no evident urban vegetation increase. The suggested framework is valuable for urban vegetation assessments and facilitates a thorough understanding and management of urban ecosystems.

Relationships between pollen assemblages and modern vegetation of the southern Gaoligong Mountains region, southwest China

Comprehensive understanding of the modern pollen–vegetation relationship is crucial for utilizing fossil pollen to reconstruct palaeovegetation, especially in mountainous areas of southwestern China. In this study, we present forty-five modern surface samples collected from five different vegetation communities of the southern Gaoligong Mountains region, and the vegetation composition was investigated at each sample site. The modern pollen assemblages were analyzed through multivariate analysis to evaluate the relationships between vegetation types and pollen assemblages, and to investigate the representation of major pollen types in different vegetation types. The results indicate that the surface pollen assemblages of different vegetation types reliably represent the modern vegetation. The median R-values of dominant pollen taxa in the Gaoligong Mountains region can be ordered as follows: Pinus > Alnus > Poaceae > evergreen Quercus. The over-representation of Pinus pollen, attributed to its high productivity and strong dispersal ability, makes it the dominant component of exotic pollen. Conversely, the under-representation of evergreen Quercus suggests that the presence of its pollen within a sample might indicate the growth of the local parent plant in the study area. The human activities in the region have a recognizable effect on the surface pollen spectra and also exert a significant negative impact on pollen diversity. Our study supplements the modern pollen database of the mountain regions in southwestern China, and provides representative modern pollen analogues to interpret fossil pollen records from southwestern China and similar regions.

A Case Study on the Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. The timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and extinguish fires using scientific methods. This paper provides an analysis of models for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model (1983), which is noted for its broad adaptability in China as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model improves the existing model by adding elevation an factor and optimizes the method for acquiring certain parameters. By considering regional landforms (ridge lines, valley lines, and slopes) and vegetation coverage, this paper establishes three-dimensional visual interpretation markers for identifying hotspots; the orientation of the hotspots can be identified to simulate the spread of the fire uphill, downhill, in the direction of the wind, left-level slope, and right-level slope. Then, the data of Sentinel-2 and DEM were used to invert the fuel humidity and slope of pixels in the fire line areas. The statistical inversion data from pixels, which replaced fixed-point values in traditional models, were utilized for predicting forest fire spread speed. In this paper, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China, and verified using high-time-resolution Himawari-8 data, Gaofen-4 data, and historical data. The results demonstrate that the direction and maximum speed of fire spread for the fire lines in Baifen Mountai, Jiaguer Villageand, Muchanggou, Xujiabaozi, and Zhaizigou are uphill, 16.5 m/min; wind direction, 17.32 m/min; wind direction, 1.59 m/min; and wind direction, 5.67 m/min. The differences are mainly due to the locations of the fire lines, moisture content of combustibles, and maximum slopes being different. Across the entire fire line area, the average rate of increase in the area of open flames within one hour was 3.257 hm2/10 min (square hectares per 10 min), closely matching the average increase rate (3.297 hm2/10 min) monitored by the Himawari-8 satellite in 10 min intervals. In contrast, conventional fixed-point fire spread models predicted an average rate of increase of 3.5637 hm2/10 min, which shows a larger discrepancy compared to the Himawari-8 satellite monitoring results. Moreover, when compared to the fire spot monitoring results from the Gaofen-4 satellite taken 54 min after the initial location of the fire line, the predictions from the RS-enabled fire spread model, which integrates remote sensing interpretations, closely matched the actual observed fire boundaries. Although the predictions from the RS-enabled fire spread model and the traditional model both align with historical data in terms of the overall fire development trends, the RS-enabled model exhibits higher reliability and can provide more accurate information for forest fire emergency departments, enabling effective pre-emptive measures and scientific firefighting strategies.

Habitation conditions of the cave lion in the middle Indigirka River basin, North-East Siberia, during the Middle and Late Pleniglacial: Reconstruction based on palynological data

Pollen analyses of the sediment from skulls cavities of two cave lions Panthera spelaea from the middle Indigirka River basin, North-East Siberia, were performed to reconstruct the habitat conditions of the animals and to estimate changes in vegetation and climatic conditions in the region. Composition of the pollen and non-pollen palynomorphs indicates that the predators most likely died on the river floodplain or near the shore of the thermokarst lake, where grass/sedge meadows and shrub thickets, providing rich grazing grounds for large herbivores, existed. Palynological data indicate that both in the early Middle Pleniglacial and in the LGM the regional vegetation included forest, shrub tundra, meadow, mire and steppe communities similar to modern cold steppes in central Yakutia. During early Marine Isotope Stage (MIS) 3, the participation of arboreal vegetation was greater than in the Last Glacial Maximum (LGM) due to higher summer temperatures. In MIS 2, the role of open herbaceous cryoxerotic communities was greater, which is associated with a higher continentality of climate and a decrease in winter temperatures. This reconstruction is consistent with the general sequence of landscape and climatic changes in the Arctic and Subarctic regions of Siberia in the Middle and Late Pleniglacial, based on paleobotanical studies of the locations of mammoth fauna and multidisciplinary studies of the Late Pleniglacial sediments from a broader area.

Impact of Climate and Vegetation Dynamics on the Ecosystem Services of Subtropical Forests—A Case Study of Baishanzu National Park Area, China

Forest ecosystems, as the primary component of terrestrial ecosystems, provide essential ecosystem services (ESs) critical for sustainable human development. However, changes in climate and vegetation can alter these forest ESs. Understanding the complex relationships between regional climate, vegetation, and ESs is key to ensuring the sustainable management of forest ESs. Therefore, this study, using Baishanzu National Park as a case example, analyzed the impacts of regional climate and vegetation dynamics (vegetation coverage, forest type, and forest structure) on forest ESs, specifically water yield (WY), soil conservation (SC), net primary productivity (NPP), and habitat quality (HQ). The results indicate that from 2000 to 2020, the forest Composite Index of Ecosystem Services (CIES) in Baishanzu National Park increased. Climate and vegetation dynamics have significant effects on forest ESs. Specifically, changes in WY and SC are primarily influenced by climate change, while changes in NPP and HQ are mainly affected by changes in forest type and structure. Complex trade-offs and synergies exist among different ESs, and the driving mechanisms of climate and vegetation changes on ES variations are also complex, involving both direct and indirect effects, with significant spatial heterogeneity. This study provides important references for the sustainable management and appropriate restoration of regional forest ESs.

Evaluating the Impact of Interferogram Networks on the Performance of Phase Linking Methods

In recent years, phase linking (PL) methods in radar time-series interferometry (TSI) have proven to be powerful tools in geodesy and remote sensing, enabling the precise monitoring of surface displacement and deformation. While these methods are typically designed to operate on a complete network of interferograms, generating such networks is often challenging in practice. For instance, in non-urban or vegetated regions, decorrelation effects lead to significant noise in long-term interferograms, which can degrade the time-series results if included. Additionally, practical issues such as gaps in satellite data, poor acquisitions, or systematic errors during interferogram generation can result in incomplete networks. Furthermore, pre-existing interferogram networks, such as those provided by systems like COMET-LiCSAR, often prioritize short temporal baselines due to the vast volume of data generated by satellites like Sentinel-1. As a result, complete interferogram networks may not always be available. Given these challenges, it is critical to understand the applicability of PL methods on these incomplete networks. This study evaluated the performance of two PL methods, eigenvalue decomposition (EVD) and eigendecomposition-based maximum-likelihood estimator of interferometric phase (EMI), under various network configurations including short temporal baselines, randomly sparsified networks, and networks where low-coherence interferograms have been removed. Using two sets of simulated data, the impact of different network structures on the accuracy and quality of the results was assessed. These patterns were then applied to real data for further comparison and analysis. The findings demonstrate that while both methods can be effectively used on short temporal baselines, their performance is highly sensitive to network sparsity and the noise introduced by low-coherence interferograms, requiring careful parameter tuning to achieve optimal results across different study areas.