Search

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

Blue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are increasingly recognized as natural climate solutions. Evaluating the current extent, losses, and gains of BCEs is crucial to estimating greenhouse gas emissions and supporting policymaking. Remote sensing approaches are uniquely suited to assess the factors driving BCEs dynamics and their impacts at various spatial and temporal scales. Here, we explored trends in the application of remote sensing in blue carbon science. We used bibliometric analysis to assess 2193 published papers for changes in research focus over time (1990 – June 2022). Over the past three decades, publications have steadily increased, with an annual growth rate of 16.9%. Most publications focused on mangrove ecosystems and used the optical spaceborne Landsat mission, presumably due to its long‐term, open‐access archives. Recent technologies such as LiDAR, UAVs, and acoustic sensors have enabled fine‐scale mapping and monitoring of BCEs. Dominant research topics were related to mapping and monitoring natural and human impacts on BCEs, estimating vegetation and biophysical parameters, machine and deep learning algorithms, management (including conservation and restoration), and climate research. Based on corresponding author affiliations, 80 countries contributed to the field, with United States (27.2%), China (15.0%), Australia (7.5%), and India (6.0%) holding leading positions. Overall, our results reveal the need to increase research efforts for seagrasses, saltmarshes, and macroalgae, integrate technologies, increase the use of remote sensing to support carbon accounting methodologies and crediting schemes, and strengthen collaboration and resource sharing among countries. Rapid advances in remote sensing technology and decreased image acquisition and processing costs will likely enhance research and management efforts focused on BCEs. [ABSTRACT FROM AUTHOR]

Solar‐induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three‐dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi‐sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5–10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real‐world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Lidar is an active remote sensing instrument, and it has been widely used in the detection of atmospheric environmental parameters and meteorological parameters. In recent years, climate change has been widely concerned by the public, and the corresponding atmospheric detection technology has also been developed rapidly. Atmospheric Lidar has been developed rapidly in China, and has achieved good research achievements. The research progress and development status of Lidar for atmospheric detection in recent years are introduced and summarized in this invited paper. The advantages and disadvantages of all kinds of atmospheric detection Lidars and their applications in different detection objects are comprehensively introduced in this article. Finally, the bottlenecks of Lidar technology are summarized, and the development trend of Lidar is also prospected. [ABSTRACT FROM AUTHOR]

Changes in climate patterns are dramatically influencing some agricultural areas. Arid, semi‐arid and coastal agricultural areas are especially vulnerable to climate change impacts on soil salinity. Inventorying and monitoring climate change impacts on salinity are crucial to evaluate the extent of the problem, to recognize trends and to formulate irrigation and crop management strategies that will maintain the agricultural productivity of these areas. Over the past three decades, Corwin and colleagues at the U.S. Salinity Laboratory (USSL) have developed proximal sensor and remote imagery methodologies for assessing soil salinity at multiple scales. The objective of this paper is to evaluate the impact climate change has had on selected agricultural areas experiencing weather pattern changes, with a focus on the use of proximal and satellite sensors to assess salinity development. Evidence presented in case studies for Californiaʼs San Joaquin Valley (SJV) and Minnesotaʼs Red River Valley (RRV) demonstrates the utility of these sensor approaches in assessing soil salinity changes due to changes in weather patterns. Agricultural areas are discussed where changes in weather patterns have increased root‐zone soil salinity, particularly in areas with shallow water tables (SJV and RRV), coastal areas with seawater intrusion (e.g., Bangladesh and the Gaza Strip) and water‐scarce areas potentially relying on degraded groundwater as an irrigation source (SJV and Murray‐Darling River Basin). Trends in salinization due to climate change indicate that the infrastructure and protocols to monitor soil salinity from field to regional to national to global scales are needed. Highlights: Climate change will have a negative impact on agriculture, particularly in arid regions.Proximal/remote sensors are useful to assess climate change impact on soil salinity across scales.Salt‐water intrusion, shallow water tables and degraded water reuse will increase soil salinity.Infrastructure and protocols to monitor soil salinity across multiple scales are needed. [ABSTRACT FROM AUTHOR]

Climate change threatens many well‐preserved archaeological sites in the Arctic. The paper presents the first Arctic multi‐threat assessment focusing on the Nuuk region of Greenland. The results suggest that the majority of the 336 known archaeological sites are already exposed to impacts from microbial degradation, permafrost thaw and vegetation, and that these impacts will increase over the next 80 years. Additional impacts from coastal erosion are only noted at a limited number of sites due to a predominant consolidated and uplifting coast. The applied methods represent an important first step to identify threatened sites and emphasize important data limitations. [ABSTRACT FROM AUTHOR]

Arctic terrestrial ecosystems near the treeline in river lowlands are vulnerable to the changing climate and seasonal extreme events, including flooding. We set up a simple camera monitoring system to record the timings and durations of the leafy period and the spring flush of river water at three observation sites (Boydom [B]: 70.64°N, 148.15°E; Kodac [K]:70.56°N, 148.26°E; Verkhny‐Khatistakh [VK]:70.25°N,147.47°E) in the Indigirka lowland, in Northeastern Siberia. Time‐lapse digital cameras were located at seven points across the three sites. The time intervals were 1–4 or 24 hours. The minimum and maximum monitoring periods were 2 years (July 2016 to August 2018) at B and 5 years (August 2013 to July 2018) at K. One camera documented the timings of river ice melt and open water periods from the riverbank of the Kryvaya River, one of the small tributaries of the Indigirka River. The other six cameras recorded several types of ground cover typical of the area, including larch trees (Larix cajanderi), shrubs (including Salix spp. and Betula nana), forbs, mosses and graminoids in an ecosystem of sparsely forested shrublands, wetlands and riversides. The data consists of 45,617 JPEG‐format images. This dataset can be used to detect the onset and offset of the growing season and to capture the ice melt timing and water cover periods in wetlands and riversides. It may be useful in validating satellite data such as the vegetation remote sensing index for remote and little‐known areas. These data may contribute to the study of the role of high‐latitude ecosystems in global climate changes. The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2020-02.1/jalter-en. [Correction added on 7 September 2020, after first online publication: JaLTER URL has been updated.] [ABSTRACT FROM AUTHOR]

Frost can significantly reduce production of field crops grown in Mediterranean‐type environments, where significant economic losses for Australian wheat occur annually. If non‐destructive sensors could make rapid, spatial assessment of frost damage, this could limit economic losses through timely management decisions. This paper reports on a methodology for imposing frost treatments to wheat under field conditions and the utility of canopy reflectance data for detecting early crop damage. Purpose‐built chambers using stepped additions of dry ice allowed for a range of frost scenarios to be tested when applied at wheat heading and anthesis. For frost treatments applied at anthesis, grain number and yield were reduced by 8.8% and 7.2%, respectively, for every degree Celsius below zero (down to −4°C). This effect was additive over two consecutive nights. For cold load equivalent, there was a 2.2% and 1.9% reduction in grain number and yield, respectively, per °C hr (below 0°C). For wheat, spectral indices PRI and NDVI (reflectance) and FRF_G and SFR_G (fluorescence) showed significant relationships, with cold load applied for heading treatments. Next steps include targeting frost intensities equivalent to cold loads between 20 and 80°C hr and testing the utility of these proposed indices in a commercial paddock setting. [ABSTRACT FROM AUTHOR]

Aim This paper presents a new global burned area (BA) product developed within the framework of the European Space Agency's Climate Change Initiative (CCI) programme, along with a first assessment of its potentials for atmospheric and carbon cycle modelling. Innovation Methods are presented for generating a new global BA product, along with a comparison with existing BA products, in terms of BA extension, fire size and shapes and emissions derived from biomass burnings. Main conclusions Three years of the global BA product were produced, accounting for a total BA of between 360 and 380 Mha year−1. General omission and commission errors for BA were 0.76 and 0.64, but they decreased to 0.51 and 0.52, respectively, for sites with more than 10% BA. Intercomparison with other existing BA datasets found similar spatial and temporal trends, mainly with the BA included in the Global Fire Emissions Database (GFED4), although regional differences were found (particularly in the 2006 fires of eastern Europe). The simulated carbon emissions from biomass burning averaged 2.1 Pg C year−1. [ABSTRACT FROM AUTHOR]

ABSTRACT There is a growing demand for fine-grained (e.g. 1 km) daily historical meteorological data. Traditional approaches interpolate between observations collected at existing meteorological stations; however, the demand for ever increasing fine resolutions has begun to outstrip the available ground-station data, especially for daily timescales. One promising technique known as the climate-aided interpolation ( CAI) has the advantage of incorporating climatological data to improve fine-grained spatial detail while still producing a daily product. By estimating spatial structure at a coarser timescale than the target timescale, more ground-station data can be used, leading to improved accuracy in these multi-timescale procedures. Another approach to improve fine-grained spatial structure is the incorporation of additional environmental covariates that are measured at fine grains. Elevation has long been used in this role, but other covariates, especially remotely sensed measures of land cover and land surface temperatures ( LST), are also promising. In this paper, we provide a comprehensive evaluation of three interpolation methods and rigorous evaluation of multi-timescale procedures that do and do not include additional environmental covariates including LST. We perform this evaluation on daily maximum air temperature predictions using a region with widely varying climates, the state of Oregon, USA. We find that multi-timescale procedures provide a roughly 10% improvement in prediction accuracy over single-timescale procedures. For multi-timescale approaches, we find that universal kriging has the lowest root mean square error ( RMSE) but has a lower predictive accuracy when the number of station decreases in the study area. Thus, generalized additive models ( GAM) and geographically weighted regression ( GWR) may be superior interpolation methods in areas with sparse station data. Most covariates other than elevation do not improve accuracy, but LST may be useful during summer when elevation displays low correlation with Tmax. Analyses also show that elevation and LST provide increased spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Questions Tree line ecotone regions are expected to respond swiftly to climate changes. In this paper, remote sensing- and ground-based tree population data are used to examine past and on-going changes of the tree line ecotone in a sub-arctic region characterized by precipitation increase. Questions addressed are: (1) at what rate has the tree line ecotone changed since the mid-20th century; (2) can specific temporal dynamics be identified; and (3) do combined remote sensing and tree population analyses add essential knowledge for the interpretation of tree line changes? Location Khibiny Mountains, Kola Peninsula, northwest Russia. Methods Aerial photos from 1958, high-resolution satellite imagery from 2006/2008 and age structure data for dominant tree line species (birch and pine) were used to analyse rate of change and temporal and species-specific tree line recruitment patterns. This was accomplished using digital elevation models, resolution-merging procedures, visual interpretation and dendroecological methods. Results Mean tree line advance for birch and pine was recorded as 29 and 27 altitudinal metres (0.6 and 0.5 m·yr−1), respectively. The advance was accompanied by an apparent infilling of pre-established tree populations and by recruitment beyond the tree line. Evident increased recruitment occurred in the late 1980s for birch and in the 1970s and 1990s for pine. Establishment showed no strong correlations with climate variables, but the importance of non-growing season variables was indicated. Conclusions The recorded tree line advance is modest compared to global model predictions for advance at high latitudes, but in accordance with results from a number of high-latitude areas. Concomitantly, the apparent increased recruitment is indicative of a more rapidly advancing tree line zone. Studies combining remote sensing and ground-based data minimize the risk of under- or overestimating potential tree line advance. Low detectability of small seedlings and saplings by remote sensing can cause underestimation of the current potential, while ground-based data used alone can overestimate potential advance. A balance between the two approaches is beneficial and enhances quality in production of change scenarios related to high latitudinal tree line areas at local to large regional scales. [ABSTRACT FROM AUTHOR]

Climate plays a fundamental role in agriculture because of to its influence on production. All processes are regulated by specific climatic requirements. Furthermore, European agriculture, based on highly developed farming techniques, is mainly oriented to high quality food production that is more susceptible to meteorological hazards. These hazards can modify environment–genotype interactions, which can affect the quality of production. The COST 734 Action (Impacts of Climate Change and Variability on European Agriculture), launched in 2006, is composed of 28 signature countries and is funded by the European Commission. The main objective of the Action is the evaluation of possible impacts arising from climate change and variability on agriculture and the assessment of critical thresholds for various European areas. The Action will concentrate on four different tasks: agroclimatic indices and simulation models, including review and assessment of tools used to relate climate and agricultural processes; evaluation of the current trends of agroclimatic indices and model outputs, including remote sensing; developing and assessing future regional and local scenarios of agroclimatic conditions; and risk assessment and foreseen impacts on agriculture. The work will be carried out by respective Working Groups. This paper presents the results of the analysis of the first phase of inventory activity. Specific questionnaires were disseminated among COST 734 countries to collect information on climate change analysis, studies, and impact at the European level. The results were discussed with respect to their spatial distribution in Europe and to identify possible common long- and short-term strategies for adaptation. [ABSTRACT FROM AUTHOR]

Measurements of the net CO2 flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO2 source–sink patterns, CO2 flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model-data comparison. Flux measurements made over four vegetation types were used to calibrate the land-surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land-surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO2 uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). [ABSTRACT FROM AUTHOR]

The detection and mapping of canopy disturbance caused by natural events such as cyclones in tropical rainforests is important for monitoring and understanding rainforest dynamics and recovery. In February 1999 tropical cyclone Rona crossed the coastline of Far North Queensland, Australia, causing significant damage to the rainforest canopy in some areas. This paper examines the application of multitemporal canopy digital elevation models (DEMs) generated by a NASA-operated airborne radar mapping system called TOPSAR in 1996 and 2000, for detection of canopy disturbance caused by cyclone damage. Canopy damage was mapped here by identifying areas with a significant decrease in canopy height estimated from the difference of the 1996 and 2000 TOPSAR DEMs. Conventional aerial photographs, flown shortly after the cyclone (March 1999), were used to validate the resulting map of cyclone damaged rainforest canopy. The results showed that the DEM-derived canopy damage map performed reasonably well when comparing the spatial distribution and size of damaged areas, while taking into account the time difference between cyclone damage and the second radar acquisition. Some errors were encountered on the steeper slopes which were related to terrain distortions inherent in radar images of steep terrain. The height accuracy of the DEM was close to the depth of some of the gaps being mapped, which also contributed to errors. Even so, the results demonstrate there may be potential for weather-independent, regional-scale mapping of forest canopy change from imaging radar that is not always possible from traditional optical means of measuring canopy elevation, for example, airborne laser data and stereo aerial photography. This may be possible in relatively flat areas, provided the second radar acquisition occurs within months of the damaging event. Further algorithm refinement is required to improve its robustness and the range of topographies where this approach can provide reliable estimates of the extent of canopy disturbance. [ABSTRACT FROM AUTHOR]

In the Serengeti–Mara ecosystem of East Africa, the migrating wildebeests’ ( Connochaetes taurinus) response to food resource distribution and terrain complexity impacts their movement characteristics. This manifests in varying ways such as movement speed, direction, turning frequency, and moving distance. To investigate these characteristics, indices derived from vegetation quantity (normalized difference vegetation index, NDVI) and relief (slope) were used in our previous work to quantify the relationships between migration route location versus vegetation, relief complexity, and their combination. Least cost pathways determined using these indices were representative of approximate migration routes. The simulated routes were shown to be strongly influenced by vegetation during the dry season. However the impact of climatic change (rainfall) on route location was not investigated though known to influence vegetation recovery patterns. This paper specifically addresses the impact of climatic change on route location. The mean monthly rainfall data were used to classify the rainy and dry seasons in the Serengeti, the Western, and the Mara areas as normal, drier, or wetter than normal, over the 1986–1997 period. The classification is based on the mean monthly rainfall variability about the 11-year seasonal mean. Regression analysis showed strong linear relationships between rainfall and mean NDVI for each one of the three areas. The subsequent seasonal classification based on the corresponding habitat vegetation characteristics (NDVI) revealed the relative variation of vegetation with rainfall. Using the derived general categories, migration routes are then predicted for the various categories using a “route attractivity index.” The seasonal migration routes were shown to change depending on the relative abundance of the rainfall during the dry season. Dry season migration routes tended toward areas with better vegetation activity, i.e., those characterized by higher NDVI gradients. Our results showed that during the western trek, wetter dry seasons have the effect of delaying the herds movement northwestward. During the northern trek, wetter dry seasons have the effect of delaying the tendency to move westward. However the variation in rainfall conditions during the rainy and dry season had no significant influence on the southern trek route location. We assume that predicted routes based on average general category conditions for different years are representative of main migration route patterns for similar seasons, therefore they are well suited for approximate route prediction, if the climatic characteristics of the year are known. [ABSTRACT FROM AUTHOR]