Variation In Vegetation Cover Research Articles

Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe

Abstract. Reanalysis soil moisture products are valuable for diverse applications, but their quality assessment is limited due to scale discrepancies when compared to traditional in situ point-scale measurements. The emergence of cosmic ray neutron sensors (CRNSs) with field-scale soil moisture estimates (∼ 250 m radius, up to 0.7 m deep) is more suitable for the product evaluation owing to their larger footprint. In this study, we perform a comprehensive evaluation of eight widely used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40, GLEAM and SMAP L4 datasets) against 135 CRNS sites from the COSMOS-UK, COSMOS-Europe, COSMOS USA and CosmOz Australia networks. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products generally exhibit good temporal correlation with the measurements, with the median temporal correlation coefficient (R) values spanning 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies time series, with R values varying from 0.46 to 0.66. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate data in arid climates and underestimate data in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high bias are detected in some sites from the west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CRA40, CFSv2, SMAP L4 and GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend that ERA5-Land and CFSv2 could be used in humid climates, whereas SMAP L4 and CRA40 perform better in arid regions. SMAP L4 has good performance for cropland, while GLEAM is more effective in shrubland regions. Our findings also provide insights into directions for improvement of soil moisture products for product developers.

Association of temporal variation of land surface temperature and vegetation cover at the Mount Babor forest area, Algeria: a geospatial modeling approach

Association of temporal variation of land surface temperature and vegetation cover at the Mount Babor forest area, Algeria: a geospatial modeling approach

Revealing climatic and groundwater impacts on the spatiotemporal variations in vegetation coverage in marine sedimentary basins of the North China Plain, China

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP.

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.

Spatial Variation Of Vegetation Cover In Ilaro Forest Reserve, Ipake Ogun State

The study assessed the vegetation of Ilaro Forest Reserve through remote sensing approach for years, 2015, 2019 and 2022. Satellite images of the corresponding years were downloaded and analyzed to compute the Normalized Difference Vegetative Index (NDVI) and to produce an NDVI map. The result showed that in 2015, there were four land covers which are; Builtup Areas, Shrubs and Grasslands, Sparse vegetation and Dense Vegetation while in 2019 and 2023, five land covers were observed which include Builtup Areas, Shrubs and Grasslands, Sparse vegetation, Dense vegetation and Barren Land. Furthermore, each of the land covers varied from 2015 through 2022. There was increase in barren land (0.01%), dense vegetation (0.72%) and sparse vegetation (5.35%) while builtup areas and shrubs and grasslands decreased by 0.08% and 5.99% respectively between 2015 and 2019. However, from 2019 to 2022, shrubs and grasslands decreased further by 3.66% while barren land, built-up areas, dense vegetation, and sparse vegetation increased by 0.02%, 0.06%, 0.03% and 3.53% respectively. The overall coverage of the land showed that from 2015 to 2022, there was a general increase in barren land (0.03%), dense vegetation (0.75%) and sparse vegetation (9.12%) while a decline in built-up areas (0.02%) and shrubs and grasslands were observed. The study showed that there was vegetation gain in the forest reserve due to reforestation project sponsored by the State government in 2017. The project impacted positively on the forest reserve and more of these projects should be encouraged by government.
  
  

Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index (NDVI)

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area. The current state of the oasis is influenced by both expansion and degradation factors. Therefore, it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources. This study used Landsat satellite images in 1987, 2002, and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis, applied multi-temporal Normalized Difference Vegetation Index (NDVI) data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis, and investigated the factors influencing these variation. This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021, with increase of NDVI values being higher than 0.10. In 2021, the highest NDVI value is generally above 0.70, while the lowest value remains largely unchanged. However, there is a remarkable increase in NDVI values between 0.20 and 0.30. The area of low NDVI values (0.00–0.20) has remained almost stable, but the region with high NDVI values (above 0.70) expands during 1987–2021. Furthermore, this study finds that in 1987–2002, the increase of vegetation cover is most notable in the northern region of the study area, whereas from 2002 to 2021, the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area. From 1987 to 2021, NDVI values exhibit the most pronounced variation, with a significant increase in the “green” zone (characterized by NDVI values exceeding 0.40), indicating a substantial enhancement in the ecological environment of the oasis. The NDVI classification is validated through 50 ground validation points in the study area, demonstrating a mean accuracy of 92.00% in the detection of vegetation cover. In general, both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987, 2002, and 2021. Finally, this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Spatiotemporal evolution characteristics and driving forces of vegetation cover variations in the Chengdu-Chongqing region of China under the background of rapid urbanization.

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China's economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

Cloud-Based Remote Sensing and Analysis of Vegetation Cover Changes in Key Regions of Oceania

The main objective of this research is to use cloud platforms to conduct a shallow survey of vegetation cover variations in Oceania. It is well-known that compared with the northern hemisphere, Oceania has a more unique ecosystem. The geographic position of Oceania is close to the equator. Therefore, the climate of some regions in Oceania is warm and has full precipitation, which is different from that in the Northern Hemisphere. This considerably influences changes in vegetation cover and makes comprehending vegetation cover tendency important for environmental protection measures. The change in vegetation cover from 2015 to 2022 was analyzed using cloud technology and remote sensing data. My findings mainly include the expansion and reduction of vegetation cover in major regions of Oceania. These results provide valuable insight into the dynamic properties of Oceanian ecosystems. This study contributes to a deeper understanding of the ecological environment in Oceania and highlights the potential of cloud platforms in remote sensing and ecological monitoring. These findings have important implications for environmental policies, biodiversity conservation, and sustainable resource management in the region.

Variance reflects resilience to disturbance along a stress gradient: Experimental evidence from coastal marshes.

Quantifying ecosystem resilience to disturbance is important for understanding the effects of disturbances on ecosystems, especially in an era of rapid global change. However, there are few studies that have used standardized experimental disturbances to compare resilience patterns across abiotic gradients in real-world ecosystems. Theoretical studies have suggested that increased return times are associated with increasing variance during recovery from disturbance. However, this notion has rarely been explicitly tested in field, in part due to the challenges involved in obtaining long-term experimental data. In this study, we examined resilience to disturbance of 12 coastal marsh sites (five low-salinity and seven polyhaline [=salt] marshes) along a salinity gradient in Georgia, USA. We found that recovery times after experimental disturbance ranged from 7 to >127 months, and differed among response variables (vegetation height, cover and composition). Recovery rates decreased along the stress gradient of increasing salinity, presumably due to stress reducing plant vigor, but only when low-salinity and polyhaline sites were analyzed separately, indicating a strong role for traits of dominant plant species. The coefficient of variation of vegetation cover and height in control plots did not vary with salinity. In disturbed plots, however, the coefficient of variation (CV) was consistently elevated during the recovery period and increased with salinity. Moreover, higher CV values during recovery were correlated with slower recovery rates. Our results deepen our understanding of resilience to disturbance in natural ecosystems, and point to novel ways that variance can be used either to infer recent disturbance, or, if measured in areas with a known disturbance history, to predict recovery patterns.

A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China

Accurate soil type maps provide an important basis for agricultural decision-making and land degradation control. In soil classification studies, the various environmental covariates are often selected based on the soil-forming framework. Since the mapping area and available observation data are limited, meteorological and vegetation cover factors have not been fully developed, and their role in soil classification needs to be evaluated. In addition, whether deep learning has out-performance in soil classification remains to be tested. The aim of this paper is to evaluate the accuracy of deep learning modelling techniques in classifying soil type using different combinations of input variables, and evaluate the importance of soil-forming variables in soil type classification. Therefore, we collected commonly used environmental covariates in Northeast China (NEC), including multiple meteorological factors and adopted a satellite-based biophysical model (Boreal Ecosystem Productivity Simulator, BEPS) to enrich vegetation cover factors. Next, four modeling strategies were developed: the soil-forming factors of soil and relief were considered as traditional environmental covariates (T), as well as combined with meteorologic variables (T + C), vegetation cover variables (T + V) and all available environmental covariates (T + C + V). Then, the effectiveness of different modeling strategies for soil classification was explored with convolutional neural network (CNN) model and multi-layer random forest (MRF) model based on soil separability. Finally, a 30 m resolution soil type map was established. The results demonstrated that both MRF and CNN can achieve high accuracy soil classification, while the CNN model performs better. The descending order of classification accuracy based on different modeling strategies of the CNN model is shown as T + C + V: 91.08%, T + V: 88.84%, T + C: 86.82%, and T: 83.96%. Meanwhile, the separability of different soil-forming factors for soil classification is soil properties, vegetation cover, temporal variation, meteorologic and relief in descending order. For Castanozems and Brown soils, MRF has higher classification accuracy, while CNN has better performance in Meadow soils and Fluvo-aquic soils. The methodology proposed in this paper aims to achieve high accuracy soil classification, provide an approach to understand the importance of soil-forming factors for the region, as well as for different soil types, and provide references for facilitating the interpretation of misclassified areas. Our results are accurate in the core areas, and therefore, this work facilitates researchers to be able to focus more on areas where different soil types intersect, thus significantly improves efficiency and saves resources, and promises to be a useful tool for future soil surveys.

Comparative assessment of the change in the species composition of vegetation on the territory of the western field of RSAU-MTAA

The article presents the analysis of changes in the floral composition of the western field of the Ecological Station of the Russian State Agrarian University – Moscow Timiryazev Agricultural Academy for 4 years. The research object is located in the center of the megalopolis, it is subject to high anthropogenic load, as it is surrounded on both sides by a road network, and at the same time it is part of the facies of the Forest Experimental Dacha. The site is characterized by close occurrence of groundwater, high density of upper soil horizons, heterogeneous soil structure and mesorelief, resulting of in flooding. As a result of a significant number of anthropogenic and abiotic factors, it is necessary to analyze the vegetation composition and the species richness of the area in order to assess the dynamics of variability of these conditions and to predict the potential stability of the biological diversity of the area. The changes in the species composition were analyzed by describing the vegetation and evaluating quantitative indicators: frequency of occurrence, abundance of species, degree of their dominance according to the O. Drude scale, as well as the Shannon index and indicators of species richness and saturation.The studies carried out showed a pronounced variability in vegetation cover from 2018 to 2022. During the study period, there was a change in plant families and an increase in species diversity: richness and saturation indices. In 2022, there was an increase in the number of species and an increase in the uniformity of their numbers compared to 2018.

Variation of Satellite-Based Suspended Sediment Concentration in the Ganges–Brahmaputra Estuary from 1990 to 2020

The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

[Spatiotemporal Evolution and Quantitative Attribution Analysis of Vegetation NDVI in Greater Khingan Mountains Forest-Steppe Ecotone].

It is of great significance to explore the dynamic variations in vegetation cover and to identify its driving factors for the restoration and sustainable development of the regional ecological environment. Based on MODIS NDVI data from 2000 to 2020 and contemporaneous meteorological, DEM, land use type, and other data, the spatiotemporal variation characteristics of vegetation in the Greater Khingan Mountains forest-steppe ecotone were deeply analyzed, and its future evolution pattern was predicted by using the methods of Sen+Mann-Kendall trend analysis and Hurst index. At the same time, the influence degree and mechanism of each detection factor and its interaction on vegetation spatial differentiation at the scale of the whole area and different physical geographic divisions were quantitatively revealed by introducing the GeoDetector model. The results showed that:① In terms of spatiotemporal variation, the spatiotemporal heterogeneity of NDVI in the Greater Khingan Mountains forest-steppe ecotone was obvious from 2000 to 2020. Temporally, NDVI fluctuated growth at a rate of 0.002 a-1 (P < 0.05) and underwent an upward mutation in 2011. Spatially, NDVI showed a distribution pattern of "increasing from southwest to northeast," and the NDVI grade transfer was mainly "medium vegetation cover→medium-high vegetation cover" during the 21 years, and the area of vegetation improvement was much larger than that of degradation. ② In terms of trend prediction, the future variation trend of NDVI in the Greater Khingan Mountains forest-steppe ecotone was mainly continuous improvement, accounting for 37%, but was mostly weakly sustained. ③ In terms of driving mechanism, the wind speed, evaporation, and relative humidity had the most significant influence on the spatial differentiation of NDVI over the whole area. The influence of natural factors has been decreasing over the past 21 years, whereas the influence of human factors has been increasing, and the main driving factors of NDVI spatial differentiation were quite different in different vegetation, climate, soil, and geomorphic zones. The synergistic effect between each factor at different spatial scales all showed two-factor or non-linear enhancement relationships, which was significantly enhanced compared with the single-factor effect. This study contributes to clarifying the causes of ecological fragility in the forest-steppe ecotone in the northern cold region and provides scientific support for formulating differentiated protection and management plans for vegetation resources under different environmental conditions.

Employing NDVI as vegetation correction variable to improve soil moisture measurements of mobile cosmic-ray neutron sensor near the Qilian Mountains

Cosmic-ray neutron sensors (CRNS) are a powerful tool for measuring soil moisture on a hectometer scale, and mobile cosmic-ray neutron technology holds significant importance for upscaling soil moisture. However, vegetation strongly affects with CRNS soil moisture measurements. Variations in vegetation over time change how the CRNS is affected, which is more challenging than a stable vegetation cover, particularly during CRNS roving, where one crosses many vegetation covers. To correct vegetation, we hypothesized that Normalized Difference Vegetation Index (NDVI) can representvegetation hydrogen pools. From 2020 to 2023, at the 74 plots with varying vegetation cover near the Qilian Mountains, the experiments for soil moisture measurements were conducted in shrubs, forests, deserts, farmlands, and grasslands using a Cosmic-ray Neutron Rover. The measured neutron intensity clearly varied across different landscapes, and the calibrated parameter N0 differed among 74 plots. The variations in vegetation cover resulted in low measurement accuracy for CRNS. All three NDVI vegetation correction methods (NDVI-θNDVI method, NDVI-NDVIave method, and NDVI-N0 method) offered by the study could improve accuracy of soil moisture measurements. In the optimal NDVI-N0 method, the relationship between NDVI and N0 was established using a power function y = 373.9 (x + 0.015)−0.136 (R2 = 0.77), which had the capability to decrease the root mean square error between oven-dried and measured soil moisture from 0.093 to 0.032 g g−1 in our study area. Employing NDVI instead of biomass for CRNS vegetation correction can substantially reduce workload and effectively enhance CRNS soil moisture measurements, showing the potential for upscaling soil moisture.

Relationships Between Land Use and Terrestrial Organic Matter Transfer to the Baltic Sea Over the Last 500 Years

AbstractTerrestrial organic matter (OM) plays a key role in coastal organic carbon burial. However, few studies focus on the relationship between land use in the watershed and the transport of terrestrial OM to coasts from a long‐term perspective. In this study, we compared terrestrial OM deposition between an inlet of the Baltic Sea and an upstream lake within the same watershed over the last 500 years, using lignin biomarkers in the sediments. In combination with pollen‐based quantitative land cover reconstruction, we assessed the impacts of semicentennial‐scale changes in land use on terrestrial OM export. The results indicated that the concentration, composition, and degradation state of the lignin‐derived OM differed substantially between the two sites. The lake received larger amounts of lignin‐derived OM during periods of intensified agriculture, but the coastal site did not. The composition of lignin in the coastal sediment did not directly reflect variations in vegetation cover in the watershed. The reason could be that the OM was settled in the upstream basins. Furthermore, the terrestrial OM that did reach the coastal sediments was modified through degradation during the transport, and only the refractory component was deposited at the coast in a relatively unaltered form.

Analysis on Temporal and Spatial Variation of Vegetation Coverage in Linxi County in Recent 30 Years

Analysis on Temporal and Spatial Variation of Vegetation Coverage in Linxi County in Recent 30 Years

[Spatio-temporal variations of vegetation cover in semi-arid regions and its response to climate change: A case study of Xilin Gol, Inner Mongolia, China].

The quantitative analysis of spatio-temporal variations of vegetation cover and its correlation with climate are of great significance for understanding of ecological environment, ecological civilization construction, and sustainable development in semi-arid areas. We investigated the spatio-temporal variations of normalized difference vegetation index (NDVI) and its response to climate change during 2000-2020 in Xilin Gol, Inner Mongolia, by using trend analysis, regression analysis and partial correlation analysis based on the data of MODIS-NDVI, tempe-rature, precipitation, digital elevation model. The results showed that vegetation cover in Xilin Gol had been increased from 2000 to 2020, which generally included three phases, i.e., stable fluctuation, rapid growth, and steady growth. The mean NDVI showed a zonal increasing distribution from southwest to northeast, and had a strong correlation with elevation and population density in Xilin Gol region. The high values of NDVI were mainly in the east, with a significant increasing trend, and the low values were in the southwest, with a local degradation. The sensitivity of vegetation cover to climate change showed spatial and temporal variations. The spatial variation of vegetation was more sensitive to temperature and the interannual variation was sensitive to annual precipitation. In summary, vegetation cover improved overall in Xilin Gol, but there was degradation in some areas. We should formulate differentiated and precise vegetation restoration and ecological environmental protection policies.

Modeling suitable habitats of maleo (Macrocephalon maleo sal. müller 1846) in Gorontalo

Maleo (Macrocephalon maleo) is one of the endangered bird species in Indonesia. This avifauna species is an endemic bird to Sulawesi Island. It is distributed from the south to the north of Sulawesi, including Gorontalo. Currently, information on suitable habitat models for M. maleo is very limited, while this information is required to support the conservation of M. maleo. This study aimed to model the potential habitat for M. maleo using species distribution modeling (SDM) with vegetation cover variables as predictors. The model was built based on the M. maleo occurrence points. The suitable habitat was then evaluated using area under the curve analysis and the receiver operating characteristic curve (AUCROC). Based on the model, the AUC is valued at 0.729, which is considered reasonable and indicates that the model can be used to depict the potential habitats for the species. In this study, most of the west and east parts of Gorontalo were considered not suitable for Maleo. While the coastal areas of Gorontalo were considered very suitable. This was confirmed for both the north and south coastal areas of Gorontalo. Then it is strongly recommended to conserve and protect most of those coasts to ensure the Maleo conservation.

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses.