Non-vegetated Areas Research Articles

Changes in Vegetation Distribution around the King Sejong Station in Antarctica: A Comprehensive Analysis of Time-Lapse Electrical Resistivity Data, Meteorological Data, and Vegetation Data

Abstract The impact of buildings around the King Sejong Station (KSS), South Korea’s first scientific station in Antarctica, has locally altered snowfall accumulation and vegetation distribution. Areas with high snowfall accumulation exhibited sparse vegetation, whereas areas with low snowfall showed distinct vegetation. This study conducted a comprehensive analysis using various data sources to understand the causes of changes in vegetation distribution. Meteorological data, including air temperature, soil temperature, soil moisture, and wind, were analyzed to determine the impact of station buildings on snow cover changes. The changes in vegetation distribution were more clearly visible through results of measured Normalized Difference Vegetation Index. Additionally, time-lapse electrical resistivity data were collected throughout 2020 to analyze variations in the subsurface electrical resistivity distribution. Electrical resistivity surveys utilized both dipole–dipole and Wenner arrays to gather data, with subsurface electrical resistivity information obtained through inversion process. The active layer, which is characterized by low electrical resistivity and is conducive to vegetation growth, is distributed in the upper layers and changes over time, only in vegetated area. In contrast, the development of the active layer was not observed in nonvegetated area. In conclusion, the time-lapse electrical resistivity data effectively reveal the temporal changes in the distribution of the active layer in the study area. When the electrical resistivity data were interpreted in conjunction with meteorological data, it provided a good understanding of the causes of changes in the distribution of vegetation around the KSS.

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.

Moderate Red-Edge vegetation index for High-Resolution multispectral remote sensing images in urban areas

Urban green space (UGS) monitoring is significant in optimizing urban planning, protecting the ecological environment, and improving residents’ quality of life. However, in urban environments, shadow interference and the emergence of new construction materials pose challenges to monitoring green vegetation in high-resolution imagery. This study found that existing vegetation indices (VIs), such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), perform inadequately in extracting vegetation in urban areas, resulting in significant omissions and errors. By conducting in-depth analysis and quantitative experiments on the reflectance of typical urban ground objects, this study developed a new vegetation extraction method, the moderate red-edge vegetation index (MREVI), to enhance the extraction accuracy of UGS vegetation from unmanned aerial vehicle (UAV) high-resolution multispectral remote sensing (RS) images. Experimental results demonstrate that MREVI performs exceptionally well in complex urban environments, significantly suppressing non-vegetation areas, achieving an overall accuracy (OA) of 98.6% and a Kappa coefficient of 0.97. This study supports for urban planning, UGS monitoring, and the evaluation of urban plant carbon sequestration capacity.

Different types of greenspace within urban parks and depressive symptoms among older U.S. adults living in urban areas

Access to greenspace in the form of urban parks is frequently used to study the mental health benefits of nature and may alleviate depression. However, there is a lack of research that considers the different types of vegetated and non-vegetated spaces that parks can provide. Our aim was to investigate whether different types of accessible park area, grassy; tree covered; and non-vegetated, were associated with depressive symptoms among older (≥50 years) urban US adults. We used interviews from the Health and Retirement Study spanning 2010 through 2016 as our primary data source. We calculated total grassy, tree covered, and non-vegetated park space accessible to participants using a comprehensive national database of US parks and a high resolution (10 m) landcover dataset. To measure depressive symptoms, we used the CESD-8 analyzed as a continuous scale. We used Poisson regression to estimate the percent difference in CESD-8 scores comparing quartiles of accessible park space. To control for confounding, we adjusted for sociodemographic characteristics, geography, and climate. Aggregated accessible park area was not substantively associated with depressive symptoms. However, having grassy park area near the home was associated with as much as 27 % fewer depressive symptoms. In contrast, non-vegetated park area was associated with up to 54 % more depressive symptoms. Our findings were robust to adjustment for air pollution, environmental noise, and artificial light at night. Different types of accessible park space may have disparate effects on mental health among older urban US adults.

The Effects of Climate Change on Normalized Difference Vegetation Index (NDVI) in the Al-Hawizeh Marsh

This study aims to classify Landsat 7,8 satellite data using Normalized Difference Vegetation Index (NDVI) thresholds. Initially, visible and near infrared bands of Landsat 8 satellite were used to derive (NDVI) image. Vegetation, non-vegetation and water areas were then analyzed where thresholds for separating them are carefully determined with the aid of ground truth information of the study area. Also, The Mann-Kendall test was conducted to analyze the trend of temperature and precipitation in the study area. The results of the analysis showed a positive trend with an increase in temperature. On the contrary, a negative trend in precipitation remained constant, as it was not statistically significant. The study correlated the historical trends of climatic elements as a central factor in the degradation of swamps (vegetation cover) from 2000 to 2010 and (2015-2023). The results showed that the year 2015 witnessed an increase in the area of NDVI compared to the years studied, as the total area of vegetation cover during this year amounted to about 537.47 km2, by 79.4%. While the year 2023 was one of the worst years of the study area, as the marshes witnessed an increase in the area of dry lands at the expense of the marsh waters, as the area of these lands reached about 354.69 km2 by 52.40%. The findings of this study are that the application of the three indices is useful for evaluating land, especially NDVI in wetlands. So it is necessary to develop and integrate with other remote sensing indices.

Semi-Arid to Arid Scenario Shift: Is the Cabrobó Desertification Nucleus Becoming Arid?

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis.

Modeling drivers of satellite-derived net primary productivity: inclusion of non-vegetated areas can be potential source of error

ABSTRACT This study modelled the drivers of satellite-derived Net Primary Productivity (NPP). The NPP drivers used were human foot print (HFP), soil organic carbon (SOC), Total Nitrogen (TN), Total Phosphorus (TP), Potential evapotranspiration, (PET), and Aridity Index (AI). The results showed that inclusion of non-vegetated land cover types (LCT) in the modelling leads to establishment of wrong relationships between the NPP and its drivers, and wrong relationships among the drivers. Inclusion of the non-vegetated LCT revealed that 1) HFP had no relationship with NPP, CUE, and SOC; 2) PET had no relationship with all the three soil variables (SOC, TN, and TP), AI and the CUE; 3) PET and NPP were strongly and positively correlated; and 4) there was no relationship between the NPP and all the three soil variables and the AI. While exclusion of the non-vegetated LCT revealed that 1) HFP had negative relationship with NPP, CUE, and SOC; 2) PET had negative relationship with the CUE, AI and all the three soil variables; 3) PET and NPP were strongly negatively correlated; and 4) there was positive correlations between the NPP and all the three soil variables and AI. However, results of both the inclusion and exclusion of the non-vegetated LCT indicated that: 1) HFP did not correlate with TN, TP, PET, and AI; and 2) there was strong and positive correlation among all the three soils and AI. It is worth noting that exclusion of the non-vegetated LCT in the modelling established the most accurate results on the relationship between the NPP and its drivers; and on the relationships among the drivers. However, there is a need to replicate this study in different geographical regions to guarantee global generalization of the results.

Bioindicators for Assessing Soil Quality in Ecuador’s Jun Jun Micro-Watershed

The evaluation of soil quality in different altitudinal gradients, with vegetative and non-vegetative subareas, is crucial for proper soil functioning and optimal crop growth, thus contributing to the sustainability of agroecosystems. Although the altitudinal gradient significantly influences soil quality, the ability to predict this quality, expressed through an index, in soils with and without vegetative cover, is still insufficiently explored. This study employed the simple additive index (SQI) method to evaluate soil quality in Ecuador’s Jun Jun micro-watershed, Tungurahua region. Three altitude categories (<2800, 2800–2900, >2900 masl) were investigated, with 24 soil samples collected across both vegetated and non-vegetated areas. The indicators used included chemical and biological parameters such as soil organic carbon (OC), earthworm density (WD), earthworm biomass (WB), organic matter (OM), pH, and total nitrogen (TN). The results revealed that in areas with altitudinal gradients below 2800 masl, the soil quality index values were higher compared to other altitudes. In vegetated areas, a decrease in index values was observed as the altitudinal gradient increased, indicating a deterioration in soil quality with increasing altitude. These findings are significant in providing a quantitative assessment of the effects of altitudinal gradient and vegetative cover influence on soil quality.

Numerical study of the effects of vegetation stem thickness on the flow characteristics of curved channels

ABSTRACT A curved channel is a common flow form in nature, often hosting aquatic vegetation along rivers. The stem thickness of this vegetation affects its resistance to flow and subsequently influences flow characteristics. To explore the impact of stem thickness on river flow in curved channels, we developed a Reynolds stress model based on real river flow conditions and vegetation data. The flow characteristics were analyzed in terms of flow velocity, Reynolds stress and turbulence intensity by varying the stem thickness of the vegetation in the vegetated area. The results of the study reveal that: (1) Water velocity in the vegetated area is significantly reduced compared to the non-vegetated area, with a greater reduction observed for thicker stems. Increasing the vegetation diameter by 3 mm resulted in a velocity decrease of 2.31–26.55%. (2) Thicker vegetation stems lead to more intense energy exchange in water flow. A 3 mm increase in vegetation diameter increased Reynolds stress by 91.81–139.70%. (3) Turbulent kinetic energy in the vegetated area is significantly higher than in the non-vegetated area, with greater turbulence intensity observed for thicker vegetation stems. Increasing the vegetation diameter by 3 mm resulted in a turbulent kinetic energy increase of 115.19–218.55%.

Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine

Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrated to enhance the accuracy of mapping forest resources. This study presents a novel method for mapping the natural distribution of highland bamboo (Oldeania alpina) using spectral bands and three machine learning algorithms, namely random forest, gradient tree boosting, and classification and regression tree. First, spectral bands, vegetation indices and textural features including contrast, entropy and inverse difference moment were derived from the Sentinel-2 elevation data. Second, observations were categorized as the dry season (December–February), short rainy season (March-May), main (long) rainy season (June-August), wet season (September–November) and annual composite. Third, the machine learning algorithms were tested using 1882 ground control points collected from field and high spatial resolution images. Finally, the best-performing machine learning algorithm was used to classify and map bamboo forests. The five land cover categories identified were bamboo stands, natural forest, other vegetation, non-vegetated areas and water bodies. The result shows that the random forest classifier is a robust algorithm with an overall accuracy of 94% considering all the annual composite’s spectral, vegetation and textural variables. The highland bamboo coverage (91 km2) in the Andracha district provided valuable insights. Bamboo stands were mostly distributed in the southern and southeastern parts of the district. Here, we show that image compositing and multiple input parameters using machine learning techniques can overcome challenges facing land cover classification, which can hinder accurate mapping of Afromontane forests. We conclude that the incorporation of vegetation indices and textural features in land cover classification increases accuracy of mapping natural highland bamboo coverage and distribution. The application of this new method is a promising prospect not only in Ethiopia but also in Afromontane forests elsewhere in Africa.

Control of Vibrio vulnificus proliferation in the Baltic Sea through eutrophication and algal bloom management

Due to climate change the pathogenic bacterium Vibrio vulnificus proliferates along brackish coastlines, posing risks to public health, tourism, and aquaculture. Here we investigated previously suggested regulation measures to reduce the prevalence of V. vulnificus, locally through seagrass and regionally through the reduction of eutrophication and consequential formation of algal blooms. Field samples collected in the summer of 2021 covered the salinity and eutrophication gradients of the Baltic Sea, one of the largest brackish areas worldwide. Physico-, biological- and hydrochemical parameters were measured and variables explaining V. vulnificus occurrence were identified by machine learning. The best V. vulnificus predictors were eutrophication-related features, such as particulate organic carbon and nitrogen, as well as occurrence of potential phytoplankton blooms and associated species. V. vulnificus abundance did not vary significantly between vegetated and non-vegetated areas. Thus, reducing nutrient inputs could be an effective method to control V. vulnificus populations in eutrophied brackish coasts.

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future.

Modelling jaguar gene flow in fragmented landscapes offers insights into functional population connectivity

ContextPreserving functional connectivity is a conservation priority to secure the long-term viability of geographically dispersed subpopulations, such as the jaguar (Panthera onca) populations in Central America. However, managing connectivity in this region is limited due to the scarcity of local assessments of the connectivity between existing populations, some of which exhibit low levels of gene flow and genetic admixture.ObjectivesWe selected the jaguar as a model species to understand how gene flow of large carnivores is shaped in a heavily human-impacted landscape in the Neotropics. We assessed the impact of landscape features and spatial scale on jaguar gene flow across northern Central America, predicted synoptic, landscape-wide functional connectivity across the region; and compared connectivity predictions based on models of gene flow, habitat suitability, and expert knowledge.MethodsWe employed genetic data based on 335 faecal samples collected from 72 individual jaguars. We parameterized gene flow resistance surfaces using linear mixed effects models and the maximum likelihood population-effects method. We evaluated nine landscape variables at six spatial scales, selecting the optimal scale and transformation for each variable according to univariate models and AIC. To predict jaguar gene flow, we developed multivariate models and implemented resistant kernels to forecast functional connectivity between jaguar populations across the study region under three dispersal distance scenarios. Furthermore, we compared the connectivity estimates based on gene flow against those based on habitat suitability and the corridors delineated by expert knowledge in the region.ResultsLow resistance to jaguar gene flow was associated with greater tree cover and vegetation, lower areas of built-up, and intermediate distances from water bodies. Notably, tree cover affected jaguar gene flow on a smaller scale compared to the rest of variables. Higher connectivity, indicated by lower resistance, was found within and around the largest jaguar conservation units (JCUs) such as the Reserva-de-Biosfera-Transfronteriza, Selva-Maya and Maya-Mountains. This contrasted with the smallest JCUs, like Sierra-Santa-Cruz and Sierra-de-las-Minas in eastern Guatemala, and Cordillera-Nombre-de-Dios in Honduras. Across the region, lower connectivity was observed in the Caribbean connection between eastern Guatemala and midwestern Honduras, as well as in the Honduran and Nicaraguan Miskito area. Models based on gene flow and habitat suitability were similar in their predictions of areas of high connectivity; however, the habitat suitability models predicted larger areas of low connectivity than did the gene flow models. Moreover, the expert knowledge corridors were consistent with areas of high and medium connectivity as predicted by the gene flow model.ConclusionsGene flow of jaguars is positively driven by the presence of forest and water sources, while human impact (built-up and non-vegetation areas) has a negative effect. Areas of lowest resistance largely correspond to the location of JCUs, which serve as crucial reservoirs of high-quality jaguar habitat within the region. While the largest JCU in the region displayed high connectivity, the low connectivity between the smallest JCUs (i.e. Sierra-Santa-Cruz, Sierra-de-las-Minas and Cordillera-Nombre-de-Dios) underscores the need for conservation attention in these areas. Conservation and management actions such as habitat loss-prevention/restoration and anthropogenic impact mitigation should be prioritized in the binational region of Guatemala-Honduras, a key connectivity bottleneck between the species’ northern and southern ranges. Similarly, attention is warranted in the Honduran-Nicaraguan Miskito area.

Effect of Intertidal Vegetation (Suaeda salsa) Restoration on Microbial Diversity in the Offshore Areas of the Yellow River Delta.

The coastal wetlands in the Yellow River Delta play a vital role in the ecological function of the area. However, the impact of primary restoration on microbial communities is not yet fully understood. Hence, this study aimed to analyze the bacterial and archaeal communities in the soil. The results indicated that Marinobacter and Halomonas were predominant in the bacterial community during spring and winter. On the other hand, Muribaculaceae and Helicobacter were prevalent during the core remediation of soil, while Inhella and Halanaerobium were predominant in non-vegetation-covered high-salinity soil. The bacterial Shannon index showed significant differences in vegetation-covered areas. For archaea, Salinigranum, Halorubrum, and Halogranum were dominant in vegetation areas, while Halolamina, Halogranum, and Halorubrum were prevalent in non-vegetation areas. The colonization of Suaeda salsa led to differences in the composition of bacteria (22.6%) and archaea (29.5%), and salt was one of the significant reasons for this difference. The microflora was more diverse, and the elements circulated after vegetation grounding, while the microbial composition in non-vegetation areas was similar, but there was potential competition. Therefore, vegetation restoration can effectively restore soil ecological function, while the microorganisms in the soil before restoration provide germplasm resources for pollutant degradation and antimicrobial development.

Numerical simulation of landscape ecological river flow structure based on vegetation patch distribution and fragmentation.

The self-organizing biological characteristics of vegetation and human activities lead to the disruption of the continuous spatial attributes of natural watersheds, which are significant factors affecting river wetland ecosystems. To clarify the landscape ecological flow characteristics of vegetation patch distribution and fragmentation, this study used the three-dimensional Reynolds stress turbulence model in ANSYS Fluent software. The model considered three vegetation patch coverages under two different submersion states and four fragmentation types of vegetation patches under the same coverage conditions within specific vegetation areas. The flow characteristics of longitudinally discontinuous rigid vegetation patches, occupying half of the width of the river channel, were numerically simulated. The model's applicability was verified by indoor open-channel flume experiments. The results indicated that: (1) The streamwise velocity in vegetated areas is significantly lower than in non-vegetated areas, and the difference in flow capacity between vegetated and non-vegetated areas increases with patch coverage and fragmentation degree. (2) In the non-submerged state, the maximum Reynolds stress in the vegetated area is located at the bottom of the vegetation and is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the maximum Reynolds stress is located near the top of the canopy and is positively correlated with both patch coverage and fragmentation degree. (3) The longitudinal turbulent kinetic energy in the vegetated area is significantly higher than in the non-vegetated area. In the non-submerged state, the turbulent kinetic energy in the vegetated area is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the turbulent kinetic energy of the longitudinal distribution in the free layer of the vegetated area is positively correlated with patch coverage, negatively correlated with fragmentation degree, and is only reflected in the upstream vegetation area.

Evaluation of urban ecological benefits utilizing vegetated areas metrics in Nanjing, China

Vegetation areas are vital for enhancing the sustainability of urban ecosystems. However, ongoing urbanization gradually reduces these areas in cities, leading to a decline in ecological benefits. This study integrates fractional vegetation cover and land surface temperature in understanding the changes in vegetated areas and ecological benefits from 1990 to 2020 in eight central districts of Nanjing using the CITYgreen model and geographically weighted regression. The findings show that human intervention resulted in a reduction of urban vegetated areas by 22.84%, while non-vegetated areas have increased by 1.24 times. The total value of ecological benefits was reduced by approximately 57.41%. The landscape pattern experienced gradual fragmentation, evidenced by a decrease of approximately 62% in the largest patch index (LPI). Both the mean shape index (SHAPE-MN) and landscape shape index (LSI) showed significant increases, indicating a rise in the complexity of landscape patches due to human intervention.

Vegetation change analysis using Normalized Difference Vegetation Index (NDVI) in Sumedang Regency

Vegetation is a crucial element of livable and healthy cities and has been linked to a number of advantages, such as enhanced human health, habitat provision, and natural system regulation. Planning sustainable cities requires an understanding of and documentation of changes in urban vegetation. The Normalized Difference Vegetation Index (NDVI)'s spatial variance and driving force are useful for managing natural resources and protecting ecological environments. Using Sumedang Regency as the research area, the normalized vegetation index (NDVI) was computed using Landsat-7 ETM and Landsat 8 OLI/TIRS data from 2003 to 2023. The findings show that the high greenness index first declined and subsequently increased between 2003 and 2023. Sumedang Regency's high greenness index shrank in area between 2003 and 2018. In 2003, 144793.17 ha was categorised as high greenness index, but in 2018 the high greenness index was only 122392.08 ha. Furthermore, the index with non-vegetated land increases every year. This shows that Sumedang Regency continues to experience land use change into non-vegetated areas, such as settlements and bare land. This research can provide assistance for the development of a sustainable natural environment in Sumedang Regency.

Characterization of the Energy Balance of Wheat Grown under Irrigation in the Hot, Arid Environment of Sudan

An analysis of the crop microclimate is essential for assessing the climate’s appropriateness for cultivation. Here, the Bowen ratio (BR) was used to characterize the energy balance in an irrigated wheat field in a hot, arid environment in Sudan. The hourly BR was calculated using micrometeorological data, including net radiation (Rn) and soil heat flux (G), observed in the 2021–2022 and 2022–2023 growing seasons (December–March) and used to compute hourly daytime latent heat (LE) and sensible heat (H) fluxes during the days before and after irrigation. In both seasons, the observed significant evaporative cooling effect of irrigation led to a daily maximum temperature difference of 2.5–5.7 °C between the wheat field and a nearby meteorological station in a non-vegetated desert area. The energy balance calculation results showed that LE was dominant over H and G. Because BR tended to be negative, H was negative; thus, LE was larger than Rn because of sensible heat advection from the surrounding area. Further, a decrease in G after irrigation indicated an alteration in the soil’s thermal properties. These results provide insights into the micrometeorological response of irrigated wheat to a hot, arid environment and useful information for understanding soil–plant–atmosphere interactions in hot, dry environments.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Multi-habitat carbon stock assessments to inform nature-based solutions for coastal seascapes in arid regions

Coastal ecosystems are integral to global carbon cycling and are increasingly recognised for their role in mitigating climate change. Within these ecosystems, the dynamics of carbon storage are diverse, varying significantly across different habitats. However, existing management strategies often focus predominantly on vegetated habitats neglecting the contributions of non-vegetated areas. We address this knowledge gap by providing a quantitative spatial assessment of carbon storage across coastal seascapes varying in plant biomass. Our comprehensive multi-habitat inventory of carbon stocks in the United Arab Emirates confirmed that mangroves are the largest carbon-storing habitat per hectare (94.3 t/ha), followed by saltmarshes (63.6 t/ha), microbial mats (51.6 t/ha), mudflats (46.8 t/ha), seagrass (32.5 t/ha), and coastal sabkha (31.0 t/ha).Mean carbon content in the top 50 cm of mangrove soils (53.9 t/ha) was similar to saltmarshes (52.7 t/ha), microbial mats (51.6 t/ha), and mudflats (46.8 t/ha). We highlight the importance of including non-vegetated habitats in carbon accounting and management strategies. Our findings suggest that a more context-specific whole-system approach is essential for guiding effective ecosystem management and designing ecologically meaningful Nature-based Solutions (NbS). Adopting this broader perspective in NbS can ensure more comprehensive conservation and restoration outcomes, which not only protect and enhance blue carbon ecosystems but also contribute to broader ecological and social benefits. This approach is pivotal for advancing our understanding of interconnected coastal ecosystems and their role in climate change mitigation.