Surface Vegetation Research Articles

Development of a Simple Observation System to Monitor Regional Wind Erosion

Dryland occupies about 46% of the global land surface area (except Antarctica) and is the most vulnerable area to climate change. From the conditions of vegetation and land surface wetness and blown sand phenomena, a simple observation system was developed to monitor regional wind erosion and applied to Khuld of Mongolia, which is sensitive to drought and desertification. The system was composed of instruments that observed blown sand, vegetation amount, land surface wetness, and landscape features related to regional wind erosion. Sixteen blown sand and eight sandstorm events were evaluated from 5 March to 5 June 2023 (i.e., during the Asian dust season in northeast Asia). The normalized difference vegetation index and visible images showed that the vegetation amount was considerably less, and the developed moisture index related to land surface wetness indicated dry conditions. Combining the results of blown sand, these indices, and visible images, land surface conditions during the analysis period were likely to occur with blown sand events.

Utilization of Google Earth to Identify Building Density

Land-use change is now common due to human activities, including economic, social, and cultural activities. Areas transforming for economic, social, and cultural purposes experience changes in land use, leading to a transition from green open spaces to buildings and resulting in hot daily surface temperatures due to a lack of vegetation. The purpose of this study is to examine the changes in built and non-built land in the Kartasura District. This study examines the relationship between surface temperature, vegetation, and building levels by using NDVI, NDBI, and ESG analysis, which is processed using the Google Earth Engine programming language. This study uses a qualitative descriptive method with a spatial approach, one of the approaches in the field of geography. The spatial approach in this study is more emphasized on spatial process analysis. The results indicate that there is a correlation between high surface temperature and low vegetation density, as well as high building density.

Trends in Emissions from Road Traffic in Rapidly Urbanizing Areas

The process of urbanization has facilitated the exponential growth in demand for road traffic, consequently leading to substantial emissions of CO2 and pollutants. However, with the development of urbanization and the expansion of the road network, the distribution and emission characteristics of CO2 and pollutant emissions are still unclear. In this study, a bottom-up approach was initially employed to develop high-resolution emission inventories for CO2 and pollutant emissions (NOx, CO, and HC) from primary, secondary, trunk, and tertiary roads in rapidly urbanizing regions of China based on localized emission factor data. Subsequently, the standard road length method was utilized to analyze the spatiotemporal distribution of CO2 emissions and pollutant emissions across different road networks while exploring their spatiotemporal heterogeneity. Finally, the influence of elevation and surface vegetation cover on traffic-related CO2 and pollutant emissions was taken into consideration. The results indicated that CO2, CO, HC, and NOx emissions increased significantly in 2020 compared to those in 2017 on trunk roads, and the distribution of CO2 and pollutant emissions in Fuzhou was uneven; in 2017, areas of high emissions were predominantly concentrated in the central regions with low vegetation coverage levels and low topography but expanded significantly in 2020. This study enhances our comprehension of the spatiotemporal variations in carbon and pollutant emissions resulting from regional road network expansion, offering valuable insights and case studies for regions worldwide undergoing similar infrastructure development.

Value assessment of carbon sequestration in generalized black soil distribution area of Amur River Basin, China

ABSTRACT In recent years, the understanding and resolution of imbalances in ecosystem carbon sequestration services (ECSS) have increasingly relied on robust data quantification and value-accounting mechanisms. Based on the interpretation of primary data, including surface vegetation, 30 cm soil cover, and hydrological information of the Amur River Basin, a carbon sequestration evaluation framework centered on the black soil belt of the river basin (BSBB) was constructed. The results showed that: (1) The annual average carbon sequestration of the BSBB from 2000 to 2019 is estimated to be 12.41 Gt C, with an expected economic value of approximately 607.61 billion yuan (China Yuan, CNY); (2) Daxinganling (DXAL, 3.26 Gt), Heihe (HH, 2.72 Gt) and Mudanjiang (MDJ, 1.81 Gt) were the three sub-regions with the highest annual carbon sequestration service, respectively, and the overall carbon density center has shifted from the south to the north within a decade; (3) The results from the geographic detector and partial correlation analysis showed that the forest area (q = 0.97, r = 0.4) and non-cultivated black soil area (q = 0.96, r = 0.97) were both clear driving factors and strong driving relationships. An increase in the forest area of the BSBB is conducive to upgrading the ECSS. This study helps promote the implementation of a carbon sink balance strategy on a global scale, provides theoretical support for future black soil resource management based on trade-offs and synergies, and provides a dual perspective for balancing ecological environmental protection with social and economic development.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

Relating Urban Land Surface Temperature to Vegetation Leafing using Thermal Imagery and Vegetation Indices

Detecting the influence of temperature on urban vegetation is useful for planning urban biodiversity conservation efforts, since temperature affects several ecosystem processes. In this study, the relationships between land surface temperature (LST) and vegetation phenology events (start of growing season, SOS; end of growing season, EOS; peak phenology) was examined in native savannah woodland and grass parcels of a hot climate town. For comparison, similar woodland and grass parcels on the town’s periphery, and a wetland, were used. The vegetation parcel LST values (°C) in one calendar year (2023) were obtained from Landsat-8 (L8) and Landsat-9 (L9) thermal imagery, whose combination yielded an 8-day image frequency. Phenology changes relative to seasonal air temperature and LST were determined using vegetation index (VI) values computed from accompanying 30 m resolution L8-L9 non-thermal bands: the Normalised Difference Vegetation Index (NDVI) and one improved VI, the Soil Adjusted Vegetation Index (SAVI). Higher imaging frequency, 250 m resolution NDVI and Enhanced Vegetation Index (EVI) MOD13Q1 layers supplemented the L8-L9 VIs. LST correlated highly with air temperature (p < 0.001). On nearly all L8-L9 image dates, the urban vegetation parcel’s mean LST was higher (p < 0.001) than that at its peri-urban equivalent. Improved VIs (SAVI, EVI) detected some phenology events to have occurred slightly earlier than detected by the NDVI. Associated with the higher LST, the SOS was earlier in the urban than in the peri-urban woodland. This association has scarcely been demonstrated in savannah vegetation, necessitating proactive efforts to reduce potential biodiversity effects.

The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based.

Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become an important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e. the area-based optimality) while overlooking other optimality assumptions such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e. the mass-based optimality). This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.

Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset

Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.

Do linear clearings in boreal peatlands recover? Comparing taxonomic, phylogenetic, and functional plant diversity

Land-use changes and anthropogenic disturbances are major threats to biodiversity, affecting ecosystem function and recovery. In boreal Alberta, Canada, petroleum development has led to extensive landscape fragmentation, notably through linear clearings created for seismic exploration that remove surface vegetation and microtopography. Despite partial recovery on forested seismic lines, peatland recovery is often arrested, impacting wildlife and carbon dynamics. Restoration efforts employ silviculture techniques to create microtopography and foster tree growth, but the efficacy of restoration treatments in restoring peatland plant diversity remains uncertain. We compared taxonomic, phylogenetic, and functional diversity of understory plant communities across treated and untreated seismic lines, alongside reference sites in treed bogs and fens. Treated lines generally had a twofold increase in plant height, leaf dry matter content, and foliar nitrogen and phosphorus contents compared to reference sites. In bogs, treated lines differed from reference conditions driven by increases in herbaceous taxa, while fens displayed disparities mainly in taxonomic and phylogenetic diversity. Differential responses of bogs and fens underline the necessity for tailored management strategies. Changes in plant diversity away from restoration targets have implications for ecosystem recovery, emphasizing the importance of long-term monitoring to evaluate the effectiveness of silviculture techniques in restoring boreal peatland plant communities.

Extraction of vegetation disturbance range using aboveground biomass estimated from Sentinel-2 imagery in coal mining areas with high groundwater table.

Coal mining in regions characterized by high groundwater table markedly predisposes to surface subsidence and water accumulation, thereby engendering substantial harm to surface vegetation, soil, and hydrological resources. Developing effective methods to extract surface disturbance information aids in quantitatively assessing the comprehensive impacts of coal mining on land, ecology, and society. Due to the shortcomings of traditional indicators in reflecting mining disturbance, vegetation aboveground biomass (AGB) is introduced as the primary indicator for extracting the mining disturbance range. Taking the Huaibei Coal Base as an example, Sentinel-2 MSI imagery is firstly used to calculate spectral factors and vegetation indices. Multiple machine learning algorithms are coupled to perform remote sensing estimation and spatial inversion of vegetation AGB based on measured samples of vegetation AGB. Secondly, an Orientation Distance-AGB (OD-AGB) curve is constructed outward from the center of subsidence water areas (SWA), with the Boltzmann function used for curve fitting. According to the location of the inflection point of the curve, the boundary points of vegetation disturbance are identified, and then the disturbance range is divided. The results show that (1) the TV-SVM model, utilizing total variables and support vector machine, achieves the highest estimation accuracy, with σMAE and σRMSE values of 208.47g/m2 and 290.19g/m2, respectively, for the validation set. (2) Thirty-six effective disturbance areas, totaling 29.89 km2, are identified; the Boltzmann function provides a good fit for the OD-AGB curve, with an R2 exceeding 0.8 for typical disturbance areas. (3) Analysis of general statistical laws indicates that disturbance distance conforms to the general characteristics of normal distribution, exhibiting boundedness and directional heterogeneity. The research is expected to provide scientific guidance for hierarchical zoning management, land reclamation, and ecological restoration in coal mining areas with high groundwater table.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.

Monitoring of vegetation changes based on RS monitoring in Changbai Mountain Nature Reserve

High vegetation cover helps maintain the ecosystem's stability and reduces the risk of soil erosion and natural disasters. Vegetation restoration is essential for the ecological value of Changbai Mountain Nature Reserve. In this study, the interannual changes in vegetation cover in Changbai Mountain Nature Reserve, China, from 2013 to 2022 were studied in depth based on Landsat remote sensing data and land use transfer matrix. The study includes interannual dynamic changes in vegetation types, quantitative analysis of interannual changes in surface vegetation types, and the land use transfer matrix analysis. In addition, the drivers of dynamic changes in vegetation types were analysed. The research indicates a notable advancement in ecological restoration within Changbai Mountain Nature Reserve between 2013 and 2022. There was a substantial reduction in the extent of barren land, which predominantly transitioned into grass and woodland areas. In particular, the year 2022 exhibited a significant increase in grassland area when compared to 2013, with a remarkable expansion of 27.18 square kilometers, primarily concentrated in the central-eastern sector of the study area. The findings of this investigation serve as a valuable scientific foundation for guiding ecological preservation and management decisions within Changbai Mountain Nature Reserve, contributing to a deeper comprehension of the evolving patterns in vegetation cover.

Urban Climate Change in Populated Kathmandu Valley, Nepal: A Case Study

Kathmandu Valley, the capital and commercial hub of Nepal, is experiencing rapid and unmanaged urbanization, leading to poor environmental conditions. The population, a key indicator of urbanization, has been growing at an average rate of 2.08% per year. This population growth from 1990 to 2021 has significantly altered land use, resulting in increased temperatures and transforming the valley into an Urban Heat Island (UHI). This study examines the effects of UHI by comparing normalized Land Surface Temperature (LST) in Kathmandu Valley and assessing the influence of land use changes on UHI intensity between 1990 and 2021. The result shows that built-up area has expanded by 30.7% of the total area, while the agricultural area has decreased by 33.59%. These changes in land use and land cover have caused a significant temperature increase at a rate of 0.038 °C per year. Green space density, measured by the Normalized Difference Vegetation Index (NDVI), has declined from 35.59% to 29.17%, being replaced by built-up areas. LST is inversely related to the presence of surface vegetation. The UHI phenomenon is observed to be stronger in urban centers such as Tribhuvan International Airport, Patan industrial area, Kritipur, and Kalanki, while it is weaker in semi-urban regions like Suryabinayak and Godawari. As climate change poses significant health and environmental challenges, adaptation has become a major issue for developing countries. Therefore, it is essential to implement change control and mitigation strategies specifically tailored to each urban region.

Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets.

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

Evaluating the spatiotemporal dynamics of driving factors for multiple drought types in different climate regions of China

Extreme weather events and natural hazards are increasingly prevalent with global warming, making it imperative to focus on the driving factors and dynamic changes associated with drought—a disaster with widespread impacts and significant losses. This study used a longtime-series of ERA5 reanalysis data to elucidate the dynamic change characteristics of direct and indirect driving factors across China. Ground observation data spanning an extended period were used to estimate the performance of the reanalysis data. The selected driving factors for drought included precipitation (PRE), potential evapotranspiration, soil moisture (SM), bare soil evapotranspiration (BSET), vegetation canopy evapotranspiration (VCET), subsurface runoff (SUBRO), surface runoff (SURRO), temperature (TEM), specific humidity, and near-surface wind speed (WS). Meteorological drought, hydrological drought, and agricultural drought were delineated using the Standardized Precipitation Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index. The Random Forest method was used to ascertain the relative importance of driving factors and their main contributions to different types of droughts. The findings revealed that ERA5 exhibited great simulation performance compared to observational data. However, the correlation coefficient of SM might be relatively lower than that of other driving factors across China. Notably, BSET and PRE exhibited higher relative importance degrees (RID) for meteorological drought in subregions 1, 2, and 3. At the same time, SUBRO demonstrated a more statistically significant influence in subregions 3, 4, and 5. SURRO played a statistically significant role in subregion 1. Moreover, BSET and PRE were pivotal for meteorological drought in northern China, whereas VCET assumes importance for hydrological drought in southern China and the North China Plain. Across different types of droughts, PRE, SM, and TEM generally exhibited relatively large RID values in China. The dynamic changes in RID for both direct and indirect driving factors varied for different drought types across different subregions of China. Notably, the RID displayed contrasting dynamic changes between PRE and BSET for meteorological drought in most subregions. Additionally, the degree of relative importance of near-surface WS tended to be lower for hydrological drought across various subregions. This study contributed to a deeper understanding of drought evolution and enhanced monitoring efforts.

Impact of greenery and waterbody on the cooling of city’s environment: a case of Rajshahi City

ABSTRACT Both plants and bodies of water are crucial to the health of the urban landscape. The degree to which these factors contribute to cooling appears, however, to change depending on factors such as location, time of year, and the mix of land use and land cover. The purpose of this study was to investigate the potential role of trees and water in Rajshahi City Corporation in reducing urban heat island effect. As a first step, satellite images are processed to generate the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). The second step is to produce maps of vegetation and water features by using a decision tree (DT) algorithm fed with threshold values from the indices. After that, thermal bands from the Landsat 8 OLI and MODIS Terra Earth observation data are used to create a map of the land surface temperature (LST). Finally, this study assessed the effect of vegetation and water coverage on global surface temperatures by analyzing their spatial distributions. Different kinds of land cover can be found in and around urban centers. Therefore, this study chose two types of water bodies (big and small) and two types of vegetation (sparse and attached/compact) for experiment. Water bodies produce cooler temperatures than other land cover types, as shown by the data. It has been found, however, that cooling rates differ across large and small bodies of water. The surface temperatures of large bodies of water are typically lower than those of smaller bodies of water. Accordingly, it appears that expansive areas significantly impact the cooling of metropolitan environments. The findings also demonstrate a difference in surface temperature between areas of dense and sparse vegetation. The cooling impact of the sparse vegetation is much less than that of the dense and widespread vegetation. There was a significant relationship between surface warming and vegetation cover in this study (R2 = 0.81). Contrarily, the presence of water and vegetation is strongly correlated (R2 = 0.88) with surface warming. The findings also show that surface warming differs depending on land use. According to a study of four typical RCC locations, business districts and industrial zones have a far greater impact on local surface temperatures than do residential developments. The experiment demonstrates that the residential area with a high proportion of vegetation had a lower average temperature by nearly 2°C. The study’s findings highlight the value of maintaining urban parks and water features.

Cover Crop Effects on Surface Runoff and Subsurface Flow in Rainfed Hillslope Farming and Connections to Water Quality

Abstract: Surface runoff and subsurface flow patterns were monitored in hillside runoff plots in almond and olive orchards with soils covered with spontaneous plants over two hydrological years. The experimental runoff plots were located on the south flank of the Sierra Nevada (Lanjarón, SE Spain) at 580 m a.s.l. with an area of 40 m2 (10 m × 4 m). The surface and subsurface discharge were collected and measured at different soil depths (0, 5, 10, 25, and 50 cm), and the dissolved nutrient concentrations (NO3–N, NH4–N, PO4–P, and K) were determined. According to the findings, the subsurface flow pathways drained most of the rainfall water compared with surface runoff, which was affected by plant cover. The influence of rainfall intensity (I30) on surface runoff was more meaningful than that on subsurface flow. Throughout the monitoring period, the runoff coefficients at soil depths of 0, 5, 10, 25, and 50 cm averaged 0.04, 0.11, 0.14, 0.17, and 0.18, respectively. Subsurface flow was one of the dominant pathways for N and K loss, whereas P loss mainly occurred via surface runoff. Moreover, the concentrations in subsurface flow were higher than the recommended level for standard water quality for NO3–N, NH4–N, and PO4–P. Subsurface flow was the main route of dissolved nutrient delivery, making these nutrients available to the root systems of trees, where nutrient uptake is more likely to occur. Thus, by lessening surface runoff and encouraging surface vegetation coverage to facilitate the recycling of nutrients and buffer the rainfall’s impact on the soil surface, nutrient loss control can be achieved.

Analysis of Urban Expansion and Heat-Island Effect of Hefei Based on ENVI

Urbanization is one of the most significant features of current social progress. Using Landsat TM/OLI images from 1995, 2005, and 2018, the land-use change, vegetation coverage, and land surface temperature retrieval of Hefei are studied, and the driving force of Hefei’s expansion is analyzed. The influence of urban expansion and vegetation coverage on the intensity of the urban heat-island effect is discussed. As the city develops, various factors, such as natural conditions, economic growth, demographic changes, and policy decisions, are driving the expansion of construction land in Hefei. The overall performance shows expansion to the southwest of the main urban area, the surface temperature rises as the city expands, and the area of the heat-island effect also increases, showing the trend of multi-center distribution. There is a clear negative correlation between land surface temperature and vegetation coverage. Therefore, increasing the city’s green infrastructure can effectively alleviate the severe heat-island effect.