Cover Area Research Articles

Coupling and Coordination Analysis of Land Use Function and Ecological Quality in Yellow River Basin, Henan Province, China

As economic development and urbanization continue to accelerate, the Yellow River Basin experiences increasing challenges in balancing land use with ecological environmental protection. Understanding their interactions is crucial for sustainable regional development. This study adopts an integrated evaluation system and a coupling model to examine the dynamic interactions between land use functions and ecological quality in the Yellow River Basin section of Henan Province, China, from 2000 to 2020. The primary findings are as follows: (1) Land use functions improved from 0.276 to 0.303, with high-land-use-function areas expanding eastward. (2) Ecological quality initially declined but subsequently improved, with areas having good and excellent ecological quality increasing from 44.47% to 72.61%. (3) Coupling coherence stabilized, with moderate coordination covering 69.80% of the area by 2020. (4) The fractional vegetation cover and leaf area index were identified as critical influencing factors. Overall, these results highlight the importance of balanced land use planning and targeted ecological conservation strategies. This study provides valuable insights for policymakers aiming to enhance sustainable regional development, emphasizing the importance of integrating ecological security with economic growth in rapidly urbanizing areas.

Effects of land cover and protected areas on flying insect diversity.

Widespread insect losses are a critical global problem. Mitigating this problem requires identifying the principal drivers across different taxa and determining which insects are covered by protected areas. However, doing so is hindered by missing information on most species owing to extremely high insect diversity and difficulties in morphological identification. To address this knowledge gap, we used one of the most comprehensive insect DNA metabarcoding data sets assembled (encompassing 31,846 flying insect species) in which data were collected from a network of 75 Malaise traps distributed across Germany. Collection sites encompass gradients of land cover, weather, and climate, along with differences in site protection status, which allowed us to gain broader insights into how insects respond to these factors. We examined changes in total insect biomass, species richness, temporal turnover, and shifts in the composition of taxa, key functional groups (pollinators, threatened species, and invasive species), and feeding traits. Lower insect biomass generally equated to lower richness of all insects and higher temporal turnover, suggesting that biomass loss translates to biodiversity loss and less stable communities. Spatial variability in insect biomass and composition was primarily driven by land cover, rather than weather or climate change. As vegetation and land-cover heterogeneity increased, insect biomass increased by 50% in 2019 and 56% in 2020 and total species richness by 58% and 33%, respectively. Similarly, areas with low-vegetation habitats exhibited the highest richness of key taxa, including pollinators and threatened species, and the widest variety of feeding traits. However, these habitats tended to be less protected despite their higher diversity. Our results highlight the value of heterogeneous low vegetation for promoting overall insect biomass and diversity and that better protection of insects requires improved protection and management of unforested areas, where many biodiversity hotspots and key taxa occur.

Reliability of the Strontium Isotope Stratigraphy approach in marginal seas: Insights from the Miocene Paratethys

The robustness of the Strontium Isotope Stratigraphy (SIS) as a chemostratigraphic tool was tested in the Miocene epicontinental Central Paratethys using new 87Sr/86Sr results obtained for 129 foraminiferal tests and various marine invertebrate shells that were converted to numerical ages using the global 87Sr/86Sr curve for Phanerozoic open oceans. These ages were compared with those expected from either magnetostratigraphic, radiometric or biostratigraphic studies applied to selected sections from different Central Paratethys basins. The results from different basins showed variations in 87Sr/86Sr values for specific time intervals; therefore, it is not possible to construct a unique 87Sr/86Sr curve for the Central Paratethys region that can be used for chemostratigraphic purposes. The 87Sr/86Sr signal in semi-enclosed basins tends to reflect the lithology of the source areas. a: For example, marine basins surrounded by areas of Mesozoic carbonate rock cover are associated with a reduced 87Sr/86Sr signal compared to the global 87Sr/86Sr signal primarily due to the drainage of these Mesozoic carbonate rocks and transfer of their specific Sr signal. By contrast, areas affected by continental runoff from crystalline terrains in combination with limited connection to surrounding marine domains were found to show an elevated 87Sr/86Sr signal compared to the global one. Additionally, it was demonstrated that infaunal, eutrophic and hypoxic species (foraminiferal genus Uvigerina and molluscs) show a shift towards a relatively higher 87Sr/86Sr signal. Therefore, our data show that variable 87Sr/86Sr patterns occur in semi-enclosed domains, primarily reflecting local settings and thus hampering reliable SIS ages. In particular, the best SIS results in the studied Paratethys area occur during periods of maximum marine connectivity, when other (biostratigraphic) methods can be applied, and then become unreliable as connectivity diminishes.

Exploring the causal relationships between environmental factors and benthos distributions in an intertidal algal reef ecosystem via a structural equation model

Algal reefs are unique and valuable yet poorly understood marine ecosystems. The causal relationships between environmental driving forces and biotic community distributions in an algal reef ecosystem were assessed to fill large scientific gaps. Using a structural equation model (SEM) coupled with factor analysis, we established a well-fitted algal reef SEM. This model revealed that there were two subsystems, one operating primarily above the reef laminated substratum and the other beneath the surface. The crustose coralline algae (CCA) and non-CCA cover areas were affected, with an inverse trend between the two, by river inputs, as indicated by NH3-N concentrations, and by the seasonal rhythm of riverine NO3-N concentrations. The trends revealed spatial competition between the two algal groups. The CCA coverage and epifaunal gastropod density responded positively to the presence of pebble habitat. Notably, sand coverage and its variation did not hinder the coverage of the two macrophytes. However, the epifaunal gastropod and arthropod densities were negatively regulated by the sand coverage variability. The infaunal arthropod density was positively influenced by the sediment heavy metal content. Among the biotic communities, the non-CCA cover area negatively affected the infaunal polychaeta density. Furthermore, the increasing infaunal polychaete density increased those of sipunculans, bivalves, and arthropods, implying co-occurrence. This SEM revealed that riverine nutrients, pebble habitat type and sand coverage variability were significant environmental drivers in organizing the biotic communities in an algal reef ecosystem.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Evaluation of the Drought Situation Using Remote Sensing Technology, an Applied Study on a Part of North Wasit Governorate in Iraq

Drought presents a substantial threat to both ecological and agricultural systems. Agriculture in Iraq is predicated on precipitation, which is a major contributor to the likelihood of drought resulting from even marginal fluctuations in precipitation. Furthermore, research suggests that Iraq suffers an approximate annual loss of 100,000 acres of arable land due to drought. NDVI and VCI, two significant indices, were utilized in this research to assess and monitor the severity of the drought in the northern region of Wasit province in Iraq. For the period from 1993 to 2023, drought intensity maps were generated utilizing NDVI-based VCI and the Geographic Information System (GIS), an extremely effective spatial data management instrument. NDVI results evidenced that the vegetation cover area was the highest in 1993 and 1998 and declined until it reached the lowest levels in 2023. The vegetation area was concentrated in the southwest parts. In contrast, VCI results demonstrated the extreme drought through the years from 2003 to 2023, which can be attributed to higher temperatures, evaporation, and lower amounts of rainfall. Throughout the thirty-year analysis period, extreme drought conditions were prevalent, especially in the last two decades. Furthermore, this drought should prompt the government to implement preventative measures to avert it. Implementing soil and water conservation measures, such as the establishment of percolation basins, contour bunds, and check dams, can also enhance drought management.

Impact of Climate Change on Spatiotemporal Patterns of Snow Hydrology: Conceptual Frameworks, Machine learning versus Nested Model

Snow accumulation in mountainous watersheds plays a paramount role in the hydrological cycle and environmental stability. In the current research, three different snow modeling approaches including conceptual models, Random Forest (RF) and their nested (Nested-RF) model have been implemented to project climate change. Also, three distinct calibration scenarios employing Snow Cover Area (SCA) and Snow Water Equivalent (SWE) have been used. These scenarios involve using SCA and SWE data individually or combined to calibrate both conceptual snowpack models and machine learning methods. Considering comparative analysis in the mountainous and complex topographic area of Tashk-Bakhtegan watershed as a case study, the Guo model (calibrated using both SCA and SWE data) achieves the highest prediction accuracy in predicting SCA/SWE using conceptual method. Considering various statistics, RF using the same calibration scenario demonstrates high accuracy in estimating SWE and shows promising results in detecting SCA. Considering the feature importance analysis and statistics of the RF and Nested-RF models indicate that the feeding conceptual outputs as extra information into the Nested-RF had minimal influence and even slightly reduced accuracy. Future projections using RF indicate significant reductions in mean annually SCA/SWE, especially under SSP8.5, with a 28.4% decrease in SCA and a reduction in SWE to 13.62 mm from a historical baseline of 19.4 mm. Lower emission scenarios (SSP2.6 and SSP4.5) show less drastic declines. While minor differences were observed between the best conceptual model and the RF approach in capturing spatiotemporal and annual snow patterns, these discrepancies did not reach statistical significance.

Assessment and Optimization of Forest Aboveground Biomass in Liaoning Province

Forests are the largest terrestrial carbon reservoirs and the most cost-effective carbon sinks. Accurate estimation of forest aboveground biomass (AGB) can significantly reduce uncertainty in carbon stock assessments. However, due to the limitations of timely and reliable forestry surveys, as well as high-resolution remote sensing data, mapping high-resolution and spatially continuous forest AGB remains challenging. The Global Ecosystem Dynamics Investigation (GEDI) is a remote sensing mission led by NASA, aimed at obtaining global forest three-dimensional structural information through LiDAR data, and has become an important tool for estimating forest structural parameters at regional scales. In 2019, the GEDI L4A product was introduced to improve AGB estimation accuracy. Currently, forest AGB maps in China have not been consistently evaluated, and research on biomass at the provincial level is still limited. Moreover, scaling GEDI’s footprint-based data to regional-scale gridded data remains a pressing issue. In this study, to verify the accuracy of GEDI L4A data and the reliability of the filtering parameters, the filtered GEDI L4A data were extracted and validated against airborne data, resulting in a Pearson correlation coefficient (ρ) of 0.69 (p < 0.001, statistically significant). This confirms the reliability of both the GEDI L4A data and the proposed filtering parameters. Taking Liaoning Province as an example, this study evaluated three forest AGB maps (Yang’s, Su’s, and Zhang’s maps), which were obtained as nationwide AGB product maps, using GEDI L4A data. The comparison with Su’s map yields the highest ρ value of 0.61. To enhance comparison accuracy, Kriging spatial interpolation was applied to the extracted GEDI footprint data, yielding continuous data. This ρ value increased to 0.75 when compared with Su’s map, with significant increases also observed against Yang’s and Zhang’s maps. The study further proposes a method to subtract the extracted GEDI data from the AGB values of the three maps, followed by Kriging interpolation, resulting in ρ values of 0.70, 0.80, and 0.69 for comparisons with Yang’s, Su’s, and Zhang’s maps, respectively. Additionally, comparisons with field measurements from the Mudanjiang Ecological Research Station yielded ρ values of 0.66, 0.65, and 0.50, indicating substantial improvements over direct comparisons. All the ρ values were statistically significant (p < 0.001). This study also conducted comparisons across different cities and forest cover types. The results indicate that cities in eastern Liaoning Province, such as Dalian and Anshan, which have larger forest cover areas, produced better results. Among the different forest types, evergreen needle-leaved forests and deciduous needle-leaved forests yielded better results.

Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning

Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1–SC6, combining 5 m resolution data, and SC7–SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.

River invertebrate biodiversity benefits from upstream urban woodland

In urban environments, invertebrate communities are subjected to a broad mixture of impacts, including diffuse pollution. Pollutant mixtures and habitat degradation can combine to apply stress on community diversity. Water quality is influenced by the assemblage and mosaic of catchment land cover. Amongst a wider suite of Nature-Based Solutions, the value of urban woodland is increasingly recognized as having potential to support a range of ecosystem services. Despite an increasing focus on establishing urban woodland for aquatic conservation, its actual influence is yet to be manifested. Therefore, we explored trees’ location in riparian and upstream catchment, within and outside of the urban area. We conducted a combination of systematic literature review and statistical analysis to better understand the woodland influence. Despite the wide range of bioindicators studied and broad worldwide spectrum of geo-climatic regimes covered, literature evidence for benefits were found in at least half the cases. With a focus on the overall family richness and the sensitive orders Ephemeroptera, Plecoptera and Trichoptera family richness as bioindicators, the statistical analysis comprised a national study in England covering 143 sites with substantial urban cover, totaling 4226 invertebrate community observations over 30 years. Two satellite-derived land cover maps were used to enable discrimination between urban and extra-urban woodland. The analysis supported the literature evidence that impervious land had negative effects and woodland positive effects. In the urban and upstream catchment, woodland was more important than pasture or cropland. There was some evidence of those woodland effects being more advantageous when trees are located within the urban area itself. Benefits attributable to woodland were distinctly apparent against a backdrop of improving macroinvertebrate diversity found to be synchronous with long-term reductions in urban pollution signatures. The presence of sparse land, even in small amounts, was detrimental to macroinvertebrate diversity. These areas of low vegetative cover might be detrimental due to high sediment input and legacy industrial contamination. Given the increasing accessibility of land cover data, the approach adopted in this case study is applicable elsewhere wherever macroinvertebrate community data are also available.

Analysis and prediction of land surface temperature with increasing urbanisation using satellite imagery

The unplanned growth of urbanization in towns and cities has led to variations in Land Surface Temperature (LST) as the green lands are converted into impervious structures without land cover management. In this study, an effort is made to study the transformational change in natural land cover area over the years and its impact on LST in Ernakulum District of Kerala, India. As per the current statistics from one of the reports on “Observed Rainfall Variability and Changes Over Kerala State”, by Indian Metrological Department in January 2020 shows that there is a significant decreasing trend in rainy days in many districts of Kerala which has resulted in highest temperature records in recent years. To understand the change in temperature trends in the district, Landsat data is collected for four different years 2005, 2010, 2015 and 2020 that maps Land Use and Land Cover (LULC) pattern for vegetation cover and built-up areas. It is important to interpret LULC maps as converting the vegetative land in built-up areas leads to multiple reflecting surfaces and temperature trapping which serves as the most direct way to understand the influence of global warming. The major merit of this research is (i) to interpret the temporal changes in LULC for categorizing the images in different land covers, (ii) to analyze Urban Thermal Field Variance Index (UTFVI) to quantify the urban heat island (UHI) effect in the region, (iii) to assess change in temperature by analyzing the spatio-temporal variations in mean temperatures for different land cover classes, and (iv) to analyze the trend of LST wrt to vegetation spectral index i.e., Normalized Differential Vegetation Index (NDVI) and spectral building index i.e., Normalized Difference Building Index (NDBI) to predict the percentage change in temperature in successive years using the widely used regression model. The computational simulation findings show that the percentage area of vegetated land decreases from 47.91 % to 29.04 % and the LST change analysis observed for years from 2005 to 2020 is 2.55 °C. For built up land, the percentage area is increased from 6.50 % to 15.27 % and the change in LST observed from year 2005–2020 is 4.82 °C which is a significant rise in temperature. The overall accuracy achieved for four respective years comes out to be 93, 89, 90, and 92 %. It has been observed from the analysis, that there is a change in temperature over the period of time from 2005 to 2020 b 3.45 °C. This is due to unplanned use of land cover classes that show considerable change in LULC maps that can be seen in the simulated results for a given study area. If the rate of change in area appeared to constantly change in this manner, then there is a possibility of further increase in land surface temperature and that will contribute in increase of temperature. The rise in mean LST from year 2020–2025 would be 0.5 °C and 1.0 °C for year 2030.

Региональная оценка трансформации землепользования на основе геопространственных данных (на примере Ярославской области)

The main directions of land use change in the Yaroslavl Region for 1992–2019 were identified based on a conjugate analysis of multi-temporal geospatial data on land cover from the European Space Agency, with the involvement of environmental and economic-geographical materials. On this basis, the main geoecological processes characterizing the features of the current development of natural and economic systems are identified. For analysis, 22 transitions of land cover classes were selected (occupying more than 1 % of the class area). They form six transformation directions: 1) increase of tree cover area; 2) reduction of tree cover area; 3) expansion of built-up areas; 4) waterlogging; 5) increase of flooded areas; 6) reduction of flooded areas. For each direction, the main processes determined by a complex of various factors are characterized. Analysis of data in GIS showed that for 1992–2019 land cover changes occurred over an area of 2.44 thous.km2, or 6.8 % of the region’s territory. As a result of the assessment of two main (dominant in area) directions (increase and reduction of tree cover), a conclusion was drawn about a net increase in the forested area in the Yaroslavl Region over 27 years by 970 km2. At the same time, the expansion of forest lands took place mainly in the zone of coniferous-broadleaved (mixed) forests, and the reduction of forests took place in the southern taiga subzone of taiga zone. Land use transformation is an important factor that should be taken into account for the development of regionally oriented recommendations for managing natural-anthropogenic landscapes and solving environmental problems.

Автоматизированная оценка динамики площадей растительного покрова московских ООПТ и окружающих территорий с 1940 г.

This paper is devoted to the assessment of the dynamics of the vegetation cover areas of the park parts of the Pokrovskoye-Streshnevo, Voikovsky and Shchukino districts (SPNA: Pokrovskoye-Streshnevo natural and historical park and the natural monuments Vsekhsvyatskaya grove, Shchukinsky park and adjacent squares that were a single whole with it in the past and are currently cut by Volokolamsk highway). Administratively, the districts belong to the North-West and North Administrative Districts of Moscow. The paper solves the problems of assessing the suitability of cartographic materials (topographic maps of different periods) for such assessments. The earliest date of compilation of a map technically suitable for assessment is recognized as 1940, i.e. an assessment of the vegetation cover dynamics was made for 84 years. Five times layers were selected: 1940, 1952, 1968, 1996 and 2024 with high-quality topographic data. For automated assessment, it was necessary to adjust the projections of topographic maps to the projection of a single base of the modern map, which was done by selecting control points and transforming the bases of past years. Cartographic problems that hinder the achievement of the set goal were identified: the disparity of conventional symbols of different time periods, in particular, different road widths, the use of off-scale topographic conventional symbols, strong distortions of the surface in some areas of the selected territory. Using the QGIS system, the maps were brought to a single base and the areas of green spaces were calculated, which amounted to 3.114 km2 in 1940, 2.748 km2 in 1952, 2.538 km2 in 1968, 2.125 km2 in 1996, and 2.601 km2 in 2024. Thus, the green areas in the selected area have decreased by 0.513 km2 (1.2 times or 16 %) over 84 years, which can be considered the inevitability of the development of a megalopolis. The Vsekhsvyatskaya grove suffered the most. The developed methodology can be recommended for calculating the indicators of the historical dynamics of cultural landscapes.

Spatiotemporal Variability of Soil Erosion in the Pisha Sandstone Region: Influences of Precipitation and Vegetation

The Pisha sandstone area, situated in the upper and middle reaches of the Yellow River in China, is characterized by severe soil and water erosion, making it one of the most critical regions on the Loess Plateau. The rugged terrain and exposed bedrock complicate management efforts for this area, posing challenges for accurate forecasting using soil erosion models. Through an analysis of terrain, vegetation, and precipitation impacts on soil erosion, this study offers theoretical support for predicting soil erosion within the exposed Pisha sandstone area of the Loess Plateau. This has substantial implications for guiding water and soil conservation measures in this region. Focusing on China’s exposed sandstone area within the Geqiugou watershed, temporal and spatial changes in vegetation cover and land use from 1990 to 2020 were analyzed. The result shows that, from 1990 to 2020, the grassland area has exhibited a consistent downward trend, with successive reductions of 64.86% to 59.46%. The area of low vegetation cover witnessed a significant decline of 59.29% in 2020 compared to that in 1990. The moderate erosion area decreased from 84.52 to 57.17 km2. The significant reduction in soil and water loss can be attributed to the expansion of forest and grassland areas, with the implementation of the Grain for Green project serving as a key policy driver for facilitating this expansion. This study provided a good example of combining rainfall with vegetation coverage to fast estimation soil erosion. A mathematical relationship between the vegetation rainfall coupling index (RV) and soil erosion was established with strong fitting effects, enabling estimation of the soil erosion volume under varying slope conditions within Pisha sandstone areas. The main focus of future soil and water conservation in the Pisha sandstone area should be on effectively managing the channel slope and minimizing exposed bedrock areas through a combination of slope cutting, the application of anticorrosive materials, and the implementation of artificial vegetation planting.

Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)

Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.

Rising forest exposure and fire severity from climate warming amplify tree cover losses from wildfire in California

Abstract Warmer temperatures and severe drought are driving increases in wildfire activity in the western United States, threatening forest ecosystems. However, identifying the influence of fire severity on tree cover loss (TCL) is challenging using commonly used categorical metrics. In this study, we quantify regional trends in wildfire-driven TCL as the product of annual burned area, average forest exposure (pre-fire tree cover), and average fire severity (relative loss of tree cover). We quantified these trends with Landsat-based 30 m resolution fire and tree cover datasets for California wildfires from 1986–2021. Rates of TCL rose faster than trends in burned area, with the magnitude of tree cover area loss per unit of area burned increasing by 70% from 0.20 ± 0.05 during 1986–1996 to 0.34 ± 0.10 during 2011–2021. Forest exposure (pre-fire tree cover) within fires increased by 41% from a decadal mean of 23.4% ± 5.5% (1986–1996) to 33.1% ± 7.8% (2011–2021). Increasing forest exposure is associated with a recent expansion of fires in dense northern forests. Concurrently, fire severity (relative TCL) rose by 30% from a decadal mean of 50.4% ± 7.2% during 1986–1996 to 65.6% ± 6.5% during 2011–2021. We developed and applied a simple conceptual framework to quantify the combined effect of wildfires affecting denser forests and burning more severely. The combined effect of these two processes contributed to nearly half (47%) of the TCL since 1986, highlighting that recent changes in burned areas alone cannot explain observed tree cover trends. Linear regression analysis revealed that warmer summers and drier winters were significant drivers of increasing forest exposure, fire severity, and burned area (R 2 from 0.54 to 0.80, p ⩽ 0.001), particularly in the northern forests. Climate extremes had a disproportionate impact on dense forests that were once more resistant to wildfire but now face risks from a shifting wildfire regime.

Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red-Green-Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = -0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = -0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = -0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = -0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.

Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)

Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R2 = 0.71, RMSE = 4.28) and fRed (pseudo-R2 = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R2 = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R2 = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R2 = 0.38, RMSE = 0.12) and fDis (pseudo-R2 = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.

El Niño enhances snow-line rise and ice loss on the Quelccaya Ice Cap, Peru

Abstract. Tropical glaciers in the central Andes are vital water resources and crucial climate indicators, currently undergoing rapid retreat. However, understanding their vulnerability to the combined effects of persistent warming, the El Niño and La Niña climate phenomena, and interannual fluctuations remains limited. Here, we automate the mapping of key mass balance parameters on the Quelccaya Ice Cap (QIC) in Peru, one of the largest tropical ice caps. Using Landsat's near-infrared (NIR) band, we analyze snow cover area (SCA) and total area (TA) and calculate the accumulation area ratio (AAR) and equilibrium-line altitude (ELA) over nearly 40 years (1985–2023). Between 1985 and 2022, the QIC lost ∼58 % and ∼37 % of its SCA and TA, respectively. We show that the QIC's reduction in SCA and rise in ELA are exacerbated by El Niño events, which are strongly correlated with the preceding wet season's Oceanic Niño Index (ONI). Further, expansion in the QIC's SCA is observed during all La Niña years, except during the 2021–2022 La Niña. Although this is a singular event, it could indicate a weakened ability for SCA recovery and an accelerated decline in the future, primarily driven by anthropogenic warming.