Long-term Ecological Monitoring Research Articles

Genome-wide sequencing reveals geographical variations in the thermal adaptation of an aquaculture species with frequent seedling introductions

Climate change and human activity are essential factors affecting marine biodiversity and aquaculture, and understanding the impacts of human activities on the genetic structure to increasing high temperatures is crucial for sustainable aquaculture and marine biodiversity conservation. As a commercially important bivalve, the Manila clam Ruditapes philippinarum is widely distributed along the coast of China, and it has been frequently introduced from Fujian Province, China, to other regions for aquaculture. In this study, we collected four populations of Manila clams from different areas to evaluate their thermal tolerance by measuring cardiac performance and genetic variations using whole-genome resequencing. The upper thermal limits of the clams showed high variations within and among populations. Different populations displayed divergent genetic compositions, and the admixed population was partly derived from the Zhangzhou population in Fujian Province, implying a complex genomic landscape under the influence of local genetic sources and human introductions. Multiple single nucleotide polymorphisms (SNPs) were associated with the cardiac functional traits, and some of these SNPs can affect the codon usage and the structural stability of the resulting protein. This study shed light on the importance of establishing long-term ecological and genetic monitoring programs at the local level to enhance resilience to future climate change.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

Long-term ecological monitoring in South Korea: progress and perspectives

Long-term ecological monitoring in South Korea: progress and perspectives

Enhancing the Long-Term Ecological Management and Monitoring of Landscapes: The L-TEAM Framework

Long-term monitoring and adaptive ecological management are essential to the conservation of biodiversity. Yet, achieving successful long-term ecological monitoring and management, especially at the landscape level, has proven challenging. In this paper, we address the hurdles faced in sustaining long-term monitoring and management for landscape-scale efforts by offering three promising conceptual and methodological developments that support such initiatives. Then, we introduce L-TEAM, a long-term ecological adaptive monitoring and management framework that integrates those three components using four tools: a conceptual model, clearly defined and measurable objectives, scientifically robust experimentation, and decision support tools. Finally, using a case study, we demonstrate L-TEAM’s effectiveness in supporting the long-term monitoring and management of a landscape conservation project with diverse habitat types and multiple management objectives. This structured decision framework not only facilitates informed decision making in management practices, but also ensures the implementation of scientifically grounded long-term monitoring. Additionally, L-TEAM holds the potential to enhance our understanding of ecosystem functioning and biodiversity responses to disturbances and management actions.

Vegetation communities and summer net ecosystem CO2 exchange on western Axel Heiberg Island, Canadian High Arctic

Climate change is expected to result in the Arctic transitioning from a carbon sink to a carbon source environment, with models predicting half of the carbon stock of the upper 3 m soil layer to be released by the year 2300 (van Huissteden and Dolman 2012). However, uncertainty in latitudinal warming and changes in Arctic ecosystem functions, such as gross carbon ecosystem exchange (GEE), are poorly understood, in part a reflection of a high variability in vascular plant community diversity that is dependent upon and sensitive to physiographic controls, such as soil moisture, topography, and seasonal active layer depth (Walker et al. 2005). This heterogeneity complicates assessments of carbon fluxes on a landscape scale and how they will change in the future (Shaver et al. 2007), especially given their sensitivity to local changes in climate, such as warming and higher rates of rainfall (Bintanja 2018, Bintanja and Andry 2017). As part of the creation of a long-term ecological and environmental monitoring program at the McGill Arctic Research Station at Expedition Fiord, western Axel Heiberg Island, field-based studies in 2021-2022 of plant surveys and summer net ecosystem CO2 exchange monitoring were undertaken to: define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. The Expedition Fiord area is recognized as a polar oasis, with high plant species richness existing within an environment of heterogeneous physiography. At the moment, five vegetation communities have been identified (xeric dwarf shrub barren, xeric-mesic dwarf shub barren, mesic dwarf shrub tundra, cassiope heath, and sedge meadow) that varied as a function of species diversity, percent cover, soil moisture, and net ecosystem carbon exchange. Barren vegetation communities having stronger respiration fluxes (i.e., carbon source environments) while more vegetated communities have stronger photosynthesis fluxes (i.e., carbon sink environments). Landcover classification revealed with high accuracy (79.3%) that barren ground and barren vegetation communities cover a much larger area compared to wetter habitats. Upscaling summer season measured carbon fluxes based on the landcover map revealed that Expedition Fiord is a carbon source environment, with an average efflux of +94.6 g CO2/day. Ongoing work focuses on the expansion of carbon flux and subsurface monitoring locations, as well as studies of soil carbon and microbial diversity across the different land cover classifications, which will help to better resolve how soil microorganisms, plant detritus, labile organic carbon, soil moisture, slope, aspect, and bedrock geology influence CO2 fluxes throughout the summer season in this high Arctic setting.

A dataset of soil moisture in Xishuangbanna tropical seasonal rain forests (2009–2017)

As the carrier of material circulation and energy flow, soil moisture plays an important role in terrestrial forest ecosystems. Observing soil moisture is beneficial for unveiling the hydrological characteristics, water conservation capacity and ecosystem service function of forest ecosystems. The tropical rain forest is among the most biodiverse ecosystems. There are large areas of tropical rain forests in Xishuangbanna, where the tropical seasonal rain forest represents a prominent vegetation type. Conducting continuous, long-term and high-quality ecological monitoring is one of the main tasks of China Ecosystem Research Network (CERN). Soil moisture content is an important indicator in the long-term site-specific water environment observations within the terrestrial ecosystems monitored by CERN. This dataset comprises soil moisture content data collected from January 2009 to December 2017 at the Xishuangbanna Station for Tropical Rainforest Ecosystem Studies of the Chinese Academy Sciences (also known as National Forest Ecosystem Research Station at Xishuangbanna, BNF for short), with a total of 2,879 entries. The determination method used for this dataset is fixed-point continuous observation using Time Domain Reflectometry (TDR) instruments; observations were conducted at multiple, ranging from 0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm, 50–70 cm, 70–90 cm and 90–110 cm. The observation frequency consists of 3 times per month, respectively at the beginning, middle and end of each month. The observations were conducted strictly according to the CERN observation and quality control protocols. The dataset is readily available for online sharing and serves as essential foundational data for conducting research on tropical forest hydrology under the global climate change and different vegetation environments.

Assessing the Impact of Insect Decline in Islands: Exploring the Diversity and Community Patterns of Indigenous and Non-Indigenous Arthropods in the Azores Native Forest over 10 Years

The ongoing decline of insect populations highlight the need for long-term ecological monitoring. As part of the “SLAM—Long Term Ecological Study of the Impacts of Climate Change on the Natural Forests of Azores” project, we investigated changes in arthropod diversity and community structure over a ten-year period (2012–2022) in the native forest of the island of Terceira (Azores). Focused on two arthropod assemblages (indigenous and non-indigenous species) monitored with SLAM traps, we asked if there was a distinguishable pattern in the diversity and structure of the studied arthropod subsets in a pristine island native forest. Species richness remained relatively constant. Endemic arthropods dominated and remained stable over time, indicating the forest’s ecological stability. In contrast, the assemblage of native non-endemic arthropods underwent changes, including increased hyperdominance and decreased biomass. The introduced arthropod assemblage showed more erratic dynamics driven by species turnover. Results suggested that temporal variation in each subset may be due to different ecological processes and that niche filtering may limit the establishment and spread of introduced arthropods. This research contributes to our understanding of the temporal dynamics of arthropods in native island forests and highlights the need for ongoing conservation efforts to protect these fragile ecosystems.

Using long-term ecological monitoring to evaluate how climate and human-induced disturbances impact nekton communities in a Northern Gulf of Mexico estuary

Apalachicola Bay, a river-fed estuary in the northeast Gulf of Mexico is experiencing impacts from anthropogenic and climate-induced changes in freshwater inputs and sea level rise. These synergistic pressures have resulted in a historic oyster fishery collapse and the expansion of mangroves, which are displacing native salt marsh. Understanding how these natural and man-made-driven changes have affected the nekton community is essential for the sustainable management of this coastal region. To evaluate changes in nekton occurrence and distribution in Apalachicola Bay, we analyzed 20 years of biological monitoring data. Our results indicated that spatial variation in species’ populations was driven by differences in salinity, while seasonal changes were driven by temperature. Freshwater inflow is the primary driver of salinity, and these differences demonstrated that river flow reductions have the potential to alter nekton communities. The effects of climate-driven droughts and anthropogenic freshwater flow reductions on estuarine community structure highlight the importance of maintaining sufficient river flow for nursery areas, which provide ecosystem services for the region. Ultimately, these findings support the continued need for monitoring programs that track accelerating ecosystem change and provide a clearer understanding for how community composition will respond to global change.

Regulation of marine plankton respiration: A test of models

Plankton respiration is a major process removing oxygen from pelagic environments and constitutes one of the largest oxygen transformations in the sea. Where the O2 supplies due to dissolution, advection and oxygenic photosynthesis are not sufficient, hypoxic, or anoxic waters may result. Coastal waters with limited water exchange are especially prone to have low oxygen levels due to eutrophication and climate change. To support marine environmental management in a period of rapid climate change, we investigated the current knowledge of regulating plankton respiration based on field and experimental studies reported in the literature. Models for regulation of plankton respiration was tested on a three-year field data set. Temperature is the most reported predictor positively influencing plankton respiration (mean r2 = 0.50, n=15). The organic carbon supply driven by primary production has a similar coefficient of determination but fewer reported relationships (mean r2 = 0.52, n=6). Riverine discharges of dissolved organic carbon can override the influence of primary production in estuaries precluding effects of nutrient reductions. The median predictions of respiration regulation produced by current models vary by a factor of 2 from the median of observed values and extreme values varied even more. Predictions by models are therefore still too uncertain for application at regional and local scales. Models with temperature as predictor showed best performance but deviated from measured values in some seasons. The combined dependence of plankton respiration on temperature, phytoplankton production and discharge of riverine organic carbon will probably lead to increased oxygen consumption and reduced oxygen levels with projected climate change. This will be especially pronounced where increased precipitation is expected to enhance riverine discharges of carbon compounds. The biologically mediated transfer of carbon for long-term storage in deeper layers will slow down. Implementation of plankton respiration measurements in long-term ecological monitoring programs at water body and basin scales is advocated, which would enable future multivariate analyses and improvements in model precision across aquatic environments.

Glacial stream ecosystems and epilithic algal communities under a warming climate

Climate change is accelerating the global loss of glaciers with potentially striking consequences for downstream ecosystems. However, there exists limited evidence of the ecological impacts of glacier loss in meltwater streams, particularly in those outside of North America and Europe. We provide a review of the abiotic conditions in glacial streams that are potential factors of their ecosystem function and biodiversity with an emphasis on their key primary producers, namely rock-attached algae or “epilithon”. Here, shrinking glaciers discharge over time less turbid melt waters, resulting in slower moving and more transparent stream conditions that are also warmer and more chemically dilute. We hypothesize that these environmental changes will stimulate epilithic algal growth while also shifting its community structure towards larger and less nutritious taxa. Although such an increase in algal growth may benefit the productive harvestable fish capacity of certain mountain streams, a potential negative trade-off involves the proliferation of nuisance algae (e.g., Didymosphenia geminata), which thrives under clear, nutrient-poor mountain conditions. We advocate the use of long-term ecological monitoring programs and experiments coordinated across global mountain ranges to better predict and understand the ecological consequences of loss of glaciers on mountain stream ecosystems.

Assortative mating for reproductive timing affects population recruitment and resilience in a quantitative genetic model.

Quantitative models that simulate the inheritance and evolution of fitness-linked traits offer a method for predicting how environmental or anthropogenic perturbations can affect the dynamics of wild populations. Random mating between individuals within populations is a key assumption of many such models used in conservation and management to predict the impacts of proposed management or conservation actions. However, recent evidence suggests that non-random mating may be underestimated in wild populations and play an important role in diversity-stability relationships. Here we introduce a novel individual-based quantitative genetic model that incorporates assortative mating for reproductive timing, a defining attribute of many aggregate breeding species. We demonstrate the utility of this framework by simulating a generalized salmonid lifecycle, varying input parameters, and comparing model outputs to theoretical expectations for several eco-evolutionary, population dynamic scenarios. Simulations with assortative mating systems resulted in more resilient and productive populations than those that were randomly mating. In accordance with established ecological and evolutionary theory, we also found that decreasing the magnitude of trait correlations, environmental variability, and strength of selection each had a positive effect on population growth. Our model is constructed in a modular framework so that future components can be easily added to address pressing issues such as the effects of supportive breeding, variable age structure, differential selection by sex or age, and fishery interactions on population growth and resilience. With code published in a public Github repository, model outputs may easily be tailored to specific study systems by parameterizing with empirically generated values from long-term ecological monitoring programs.

Climate Change Helps Polar Invasives Establish and Flourish: Evidence from Long-Term Monitoring of the Blowfly Calliphora vicina.

The isolated sub-Antarctic islands are of major ecological interest because of their unique species diversity and long history of limited human disturbance. However, since the presence of Europeans, these islands and their sensitive biota have been under increasing pressure due to human activity and associated biological invasions. In such delicate ecosystems, biological invasions are an exceptional threat that may be further amplified by climate change. We examined the invasion trajectory of the blowfly Calliphora vicina (Robineau-Desvoidy 1830). First introduced in the sub-Antarctic Kerguelen Islands in the 1970s, it is thought to have persisted only in sheltered microclimates for several decades. Here, we show that, in recent decades, C. vicina has been able to establish itself more widely. We combine experimental thermal developmental data with long-term ecological and meteorological monitoring to address whether warming conditions help explain its current success and dynamics in the eastern Kerguelen Islands. We found that warming temperatures and accumulated degree days could explain the species' phenological and long-term invasion dynamics, indicating that climate change has likely assisted its establishment. This study represents a unique long-term view of a polar invader and stresses the rapidly increasing susceptibility of cold regions to invasion under climate change.

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

Climate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard’s index: 0.87 ± 0.07; Dice index: 0.93 ± 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Niño events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.

Active Processes in Modern Russian: Possibilities of Ecological and Linguistic Study

The article considers possibility of applying the ecological-and-linguistic approach to the study of active processes that characterize the current state of the Russian language, text production, including electronic communication. The specificity of the ecological approach in linguistics, its distinction from the orthological approach, as well as the structural analysis of linguistic units are displayed, visible active processes that dominate in phonetics, vocabulary, graphic encoding and text creation are registered. The authors consider the usefulness of the ecological approach in linguistics, its distinction from the orthological approach, as well as the structural analysis of linguistic units. The analysis made provision for defining language democratization as the major trend, which is noted in word functioning, changes in phrase and sentence structures; numerous borrowings of words and parts of words from the English language support the globalization trend. There is a tendency in strengthening the subjective aspect of the text content in public communication that is marked by the shift towards evaluative-expressive-emotional colloquial speech style. The opposing trend is text intellectualization, it appeals to background knowledge of the reader, the use of abbreviated forms in information coding. The authors conclude that a long-term ecological monitoring of these trends and the use of the ecological-and-linguistic approach to classification of linguistic phenomena are required.

Temporal patterns in coral reef fish assemblages with varying disturbance levels: A tale of two National Parks in Hawai´i

Long-term ecological monitoring of coral reefs can reveal the state of the reef communities and responses of different reef components to various pulse and press disturbance events. Here, we describe long-term monitoring of reef fish assemblages from two U.S. National Parks in Hawai‘i, Kaloko-Honokōhau National Historical Park (KAHO) and Kalaupapa National Historical Park (KALA) over the past decade. These two parks experience different anthropogenic disturbances as KAHO is highly accessible with multiple types of adjacent land use while KALA is relatively inaccessible, surrounded by large expanses of native forest, and known for having the highest documented fish biomass within the main Hawaiian Islands. The fish assemblages in KAHO were numerically dominated by small-bodied damselfish Chromis spp. and herbivorous surgeonfishes Acanthurus nigrofuscus, Ctenochaetus strigosus and Zebrasoma flavescens, while those in KALA were numerically dominated by Chromis vanderbilti, A. nigrofuscus, Thalassoma duperrey and Paracirrhites arcatus. Multivariate control charts detected anomalies in the fish assemblages in some years. In KAHO, gradual shifts in the assemblages were detected in recent years due to changes (both increases and decreases) in relative abundances of numerically dominant species including Chromis agilis, C. vanderbilti, A. nigrofuscus, C. strigosus and Z. flavescens. There was a documented reduction in live coral cover in KAHO due to a bleaching event in 2015, and potential delayed effects of the coral mortality on the fish assemblages were found particularly in the North and South regions of the park. In KALA, increases in the abundances of Acanthurus (A. blochii, A. leucopareius, A. nigrofuscus, A. thompsoni and A. triostegus) and C. strigosus were found in 2014, likely due to a large recruitment event that had not been well reported. As localized thermal stress events were detected in both KAHO and KALA in 2019, continuous monitoring of these two parks is critical to assess how the coral-reef ecosystems under two different human-use scenarios respond to repeated heating events and whether there are immediate as well as delayed effects on reef organisms resulting from losses of live coral tissue and a subsequent decrease in structural complexity provided by the coral.

Ecological Environment Evaluation Based on Remote Sensing Ecological Index: A Case Study in East China over the Past 20 Years

East China is one of the most active regions in terms of economic and social development, and with the accelerated urbanization process, environmental problems are becoming increasingly prominent. The objective, quantitative, and timely evaluation of spatial and temporal changes in ecological quality is of great significance for environmental protection and decision making. The remote sensing ecological index (RSEI) is an objective, fast, and easy ecological quality monitoring and evaluation technique which has been widely used in the field of ecological research, but it often involves problems of cloud occlusion and stitching difficulties when used to conduct large-scale and long-term monitoring. In this paper, based on the Google Earth Engine (GEE) platform, an RSEI was constructed using MODIS data products to evaluate the spatial and temporal changes in ecological quality in East China over the past 20 years. The study shows the following: (1) The mean RSEI values in 2000, 2005, 2010, 2015, and 2020 were 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, with the mean values first decreasing and then showing a stable increasing trend. In Shanghai and Jiangsu, the mean RSEI values show a fluctuating characteristic of “falling and then rising”, and large respective decreases of 32.4% and 25.8% throughout the monitoring period. The RSEI values in Fujian Province showed a relatively stable upward trend during the study period (19% increase). (2) The RSEI spatially correlated clustering maps of the local indicators showed that the regions with a high degree of clustering are mainly located in Quzhou City, Zhejiang Province, Ningde City, Fujian Province, and northern Anhui Province (Bozhou and Huabei). With the promotion of ecological civilization and the enhancement of environmental protection awareness, the vegetation cover has significantly increased, which has led to the rise in RSEI values. The low values are mainly distributed in densely populated areas with more human activity, such as the central-eastern part of Jiangsu Province, central Anhui Province, Shanghai, and northern Zhejiang Province. With the development of cities, impervious surfaces occupy more and more ecological land, which eventually affects the regional RSEI values. (3) This research provides a promising method for the evaluation of spatial and temporal changes in ecological environment quality based on an RSEI and GEE. The image processing, based on GEE cloud computing, can help overcome the problems of missing remote sensing data, chromatic aberrations, and spatial and temporal inconsistency, which could greatly improve the efficiency of image processing and extend the application of the remote sensing ecological index to large-scale, long-term ecological monitoring. The research results can provide a reference for improving the applicability and accuracy of remote sensing ecological indices and provide a theoretical basis for ecological conservation and land management in the context of rapid urbanization.

Long-term monitoring projects of Brazilian marine and coastal ecosystems.

Biodiversity assessment is a mandatory task for sustainable and adaptive management for the next decade, and long-term ecological monitoring programs are a cornerstone for understanding changes in ecosystems. The Brazilian Long-Term Ecological Research Program (PELD) is an integrated effort model supported by public funds that finance ecological studies at 34 locations. By interviewing and compiling data from project coordinators, we assessed monitoring efforts, targeting biological groups and scientific production from nine PELD projects encompassing coastal lagoons to mesophotic reefs and oceanic islands. Reef environments and fish groups were the most often studied within the long-term projects. PELD projects covered priority areas for conservation but missed sensitive areas close to large cities, as well as underrepresenting ecosystems on the North and Northeast Brazilian coast. Long-term monitoring projects in marine and coastal environments in Brazil are recent (<5 years), not yet integrated as a network, but scientifically productive with considerable relevance for academic and human resources training. Scientific production increased exponentially with project age, despite interruption and shortage of funding during their history. From our diagnosis, we recommend some actions to fill in observed gaps, such as: enhancing projects' collaboration and integration; focusing on priority regions for new projects; broadening the scope of monitored variables; and, maintenance of funding for existing projects.

Instability of remote sensing ecological index and its optimisation for time frequency and scale

Evaluating regional ecological environmental quality (EEQ) using remote sensing is important for achieving sustainable development. The remote sensing ecological index (RSEI) is a typical EEQ evaluation model used to comprehensively reflect regional ecological quality. However, owing to its complete dependence on remote sensing image information, the RSEI also has inherent issues, including unstable time series and inconsistent resolutions of its four sub-indices. To address these problems, this study used the Google Earth Engine (GEE) to propose an optimisation notion for the harmonic analysis of time series (HANTS) coupled random forest (RF) model and determined the accuracy and image quality of the optimised RSEI (RSEIo) for the Yangtze River Basin (YRB). Based on the findings, HANTS could fill the gaps in the images, effectively reducing the noise and discrete degrees of the four indices and thereby improving the stability of the RSEI. The correlation coefficient (R) between the RSEI and RSEIo was 0.93, with a root mean square error (RMSE) of 0.13, indicating that RSEIo is reliable. The image quality assessment (based on contrast and information entropy) indicates that HANTS combined with the RF model can produce RSEIo images with higher definitions and richer information at a constant spatial resolution. Further, the pixel-by-pixel coefficient of variation evaluation indicates that the RSEIo image was highly stabilised, yielding higher numbers of effective RSEIo images without changing the temporal resolution. Compared with traditional RSEI calculations, the optimisation proposal herein could highlight ecological differences caused by topographic changes in the YRB, which would produce an RSEI closer to actual surface conditions. Further, this proposed method could be used to obtain more detailed ecological information at a constant spatiotemporal resolution, thereby meeting the needs of long-term ecological monitoring in large-scale regions.

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6% and remaining surface components with MAEs between 3.0 and 11.2%. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2% per decade for the entire study area and + 4.2% per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome.

Remote Sensing Land Use Evolution in Earthquake-Stricken Regions of Wenchuan County, China

Earthquakes and their secondary geological disasters have a certain impact on the land cover, which leads to the degradation of the ecological environment and the stability of the ecosystem. At present, there are few studies on the spatial–temporal evolution characteristics of land-use change in earthquake-stricken regions, especially the lack of quantitative evaluation of the impact of earthquakes on land use at the micro-scale. The “5·12” Wenchuan ms8.0 earthquake caused serious damage to the surface resources in the disaster area. The study on the spatial–temporal evolution characteristics of land-use change in the disaster area can provide a reference for the remote sensing dynamic monitoring of the ecological environment. Therefore, based on geographical big data, this paper used a land-use comprehensive degree index, land-use transfer matrix, and landscape ecological index to explore and analyze the spatial–temporal evolution characteristics of land use in Wenchuan County before and after the earthquake. The results showed that the types of cropland, forest, built-up, and bare land changed greatly before and after the earthquake. During the earthquake recovery period, the comprehensive index of land use in the study area basically showed an increasing trend. Under the effect of artificial measures and natural restoration, land use was continuously improved, and vegetation was restored well. After 2008, the Patch Density (PD) and Landscape Shape Index (LSI) values of most landscape types decreased, and the Aggregation Index (AI) values increased, indicating that the ecological environment of the whole region showed a benign development in the post-earthquake period. The results not only contribute to the establishment of scientific ecological environment management in earthquake-stricken regions but also contribute to the formulation of long-term ecological environment monitoring and ecological restoration planning according to the law of land-use change.