Variation In Vegetation Cover Research Articles

Detection of spatial and temporal variation characteristics of vegetation cover in the Lower Mekong region and the influencing factors

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen’s slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area.

EVALUATION OF DEFORESTATION IN THE MUNICIPALITY OF BRASIL NOVO IN THE STATE OF PARÁ - BRAZIL, USING MACHINE LEARNING TECHNIQUES

The agricultural frontier of the Brazilian Amazon, is undergoing significant changes in its land cover, primarily driven by the expansion of cattle ranching and cocoa cultivation. Through the analysis of Landsat 8 satellite images and the WorldCover 10m 2020 product, combined with climatic and socioeconomic data, this study investigated land use dynamics between 2015 and 2023, on the municipality of Brasil Novo. The results revealed a substantial reduction in forest cover, with the expansion of pasturelands as the main driver of deforestation. The temporal analysis of the Normalized Difference Vegetation Index (NDVI) indicated interannual variations in vegetation cover, associated with extreme weather events and anthropogenic pressure. Although the municipality still maintains 43.8% of forest cover, with the Arara Indigenous Land being a notable example, the pressure on natural resources demands the implementation of public policies and sustainable management practices to mitigate environmental impacts and ensure the conservation of Amazonian biodiversity. Keywords: Amazon, NDVI, remote sensing, climate change, livestock, land use.

The role of early life factors and green living environment in the development of gut microbiota in infancy: Population-based cohort study

ObjectiveEarly life microbial exposure influences the composition of gut microbiota. We investigated how early life factors, and the green living environment around infants’ homes, influence the development of gut microbiota during infancy by utilizing data from the Steps to Healthy Development follow-up study (the STEPS study). MethodsThe gut microbiota was analyzed at early (∼3 months, n = 959), and late infancy (∼13 months, n = 984) using 16S rRNA amplicon sequencing, and combined with residential green environment, measured as (1) Normalized Difference Vegetation Index, (2) Vegetation Cover Diversity, and (3) Naturalness Index within a 750 m radius. We compared gut microbiota diversity and composition between early and late infancy, identified significant individual and family level early life factors influencing gut microbiota, and determined the role of the residential green environment measures on gut microbiota development. ResultsAlpha diversity (t-test, p < 0.001) and beta diversity (PERMANOVA, R2 = 0.095, p < 0.001) differed between early and late infancy. Birth mode was the strongest contributor to the gut microbiota community composition in early infancy (PERMANOVA, R2 = 0.005, p < 0.01) and the presence of siblings in late infancy (PERMANOVA, R2 = 0.007, p < 0.01). Residential green environment showed no association with community composition, whereas time spend outdoors did (PERMANOVA, R2 = 0.002, p < 0.05). Measures of greenness displayed a statistically significant association with alpha diversity during early infancy, not during late infancy (glm, p < 0.05). In adjusted analysis, the associations remained only with the Naturalness Index, where higher human impact on living environment was associated with decreased species richness (glm, Observed richness, p < 0.05). ConclusionsThe role of the residential green environment to the infant gut microbiota is especially important in early infancy, however, other early life factors, such as birth mode and presence of sibling, had a more significant effect on the overall community composition.

Consistent generalization of plant-hummingbird networks despite increasing vegetation cover across a tropical urban landscape

Human activities, particularly urbanization, profoundly impact ecosystems often resulting in biotic homogenization. Whether or not urban landscapes can sustain diverse pollinator and plant communities is an important question to be addressed. Here, we investigated the influence of urbanization on plant-hummingbird interaction networks in a large tropical city, Belo Horizonte, Brazil. We recorded 13198 legitimate interactions between seven hummingbirds and 57 plant species across 12 local networks. Urban landscapes exhibited predominantly generalized networks, maintaining this pattern across varying vegetation cover and floral resource abundance. Although some functionally specialized hummingbirds with long bills were recorded performing more specialized interactions, urban environments did not generally support specialized networks. Nevertheless, network specialization did increase with the proportion of native nectar plants, emphasizing their importance for maintaining some specialized interactions. Furthermore, we observed a positive effect of plant richness, but not of flower abundance, on hummingbird abundance, indicating that it is not only the amount of flowers, but the diversity of floral resources that may be a key factor in maintaining hummingbirds. Therefore, promoting a diverse assemblage of native plants in urban green areas is crucial for sustainable pollinator communities. Our study highlights that while a biodiverse urban landscape will require careful urban vegetation planning considering both floral resource diversity and availability, vegetation cover per se may not be sufficient to mitigate the negative impacts of urbanization. Maintaining a diverse vegetation with different life forms, flowering phenology, and especially of native plants across the urban landscape is needed to create welcoming spaces for pollinators.

The effect of dynamic changes in vegetation coverage on the mechanism of organic carbon loss in inter-rill erosion

The intricate effects of dynamic variations in vegetation coverage, representing different vegetation growth states and erosion conditions, on properties (content, enrichment and loss) of sediment and soil organic carbon (SOC) fraction in inter-rill erosion have not yet been fully elucidated. By setting micro-plots with different vegetation coverage ranges (0 %, 15 ∼ 35 %, 25 ∼ 55 % and 35 ∼ 70 %), the interaction mechanisms between vegetation coverage and inter-rill erosion, particle size distribution and SOC fractions properties during the vegetation growing period were studied. The average sediment yield, runoff yield and sediment concentration were highest under the bare plot, and diminished progressively with increased vegetation coverage. The effective clay content decreased with the increasing coverage, while ultimate clay was highest in the vegetation coverage range of 25 ∼ 55 %. Clay particle was transported as aggregates, with enrichment for all treatments except for the vegetation coverage range of 35 ∼ 70 %, providing transportation potential for SOC fractions loss. The content of SOC and mineral-associated organic carbon, as well as their enrichment ratio, decreased and then increased with coverage, which is the opposite of particulate organic carbon, and the loss of all fractions decreased with increasing vegetation coverage. Mineral-associated organic carbon dominated change in SOC, with proportion in SOC content and loss decreasing first and then increasing with vegetation coverage. The structural equation model showed the impact of vegetation coverage on SOC fraction loss was mainly achieved through indirect regulation of inter-rill erosion and particle size distribution. Our results provide new insights into the complex relationship between natural slope vegetation coverage and SOC loss, particularly from a fractions perspective, as well as understanding the ultimate fate of SOC in such environments.

Spatial and temporal variations of vegetation cover on the central and eastern Tibetan Plateau since the Last glacial period

Long-term changes in vegetation cover of the Tibetan Plateau (TP) are essential for understanding vegetation change under future climate. Previous studies have mainly concentrated on the Holocene and the eastern region of the TP, but here, we establish a relationship between modern pollen data (including both pollen percentage and concentration) and vegetation cover using a random forest (RF) model based on 362 soil-surface samples from the TP, as well as using it to quantitatively reconstruct the vegetation cover history of the Dagze Co (central TP, covering the last 19.5 cal. ka BP) and Koucha Lake (eastern TP, covering the last 33.8 cal. ka BP) regions. The RF results indicate that both the models based on pollen percentages or concentrations perform similarly (former: R2 = 0.538, RMSEP = 19.772%; latter: R2 = 0.540, RMSEP = 19.723%). However, when considering the reconstructed vegetation cover of Dagze Co and Koucha Lake, the results based on pollen concentrations appear to be more reliable. Before 13.4 and 16.8 cal. ka BP, Dagze Co and Koucha Lake has low vegetation cover of 25% and 30%, respectively, dominated by alpine desert or desert steppe. After that, changes in vegetation cover show different trends. At Dagze Co, the vegetation cover reaches a high level (54%) between 13.4 and 5.3 cal. ka BP, followed by a decrease until it starts increasing again at 2 cal. ka BP, in response to the change in the Indian Summer Monsoon (ISM). At Koucha Lake, the vegetation cover fluctuates at around 60%, indicating less sensitivity to climate change. Our research highlights the importance of pollen concentrations in quantitatively reconstructing past vegetation cover and the sparse vegetation status during the LGM on the TP.

A first look into moss living tardigrades in boreal peatlands.

Tardigrades (Tardigrada) are a phylum of micrometazoans found in all biomes on Earth, but their ecology and habitat preferences remain vastly understudied. Boreal peatlands include a diversity of habitat types and high structural heterogeneity that represents an interesting system to study some of the poorly known habitat preferences of tardigrades. Here, we investigate for the first time tardigrade communities in peatland mosses and the latter's potential associations with key environmental variables. We collected 116 moss samples from 13 sites representing different peatland types and management histories. We found that tardigrades are common and diverse in boreal peatlands, as tardigrades were present in 72% of the collected samples and we identified 14 tardigrade genera. Tardigrade abundance seemed to increase alongside the increasing tree basal area and the density was higher in the microtopographic level further from the water table level, that is, hummocks (mean 117/moss gram) than in lawns/hollows (mean 84/moss gram). Furthermore, the highest tardigrade density was found in the moss taxa that are associated with forested peatland types (i.e., feather mosses) (321 mean/moss gram). Finally, we found interesting patterns regarding tardigrade functional diversity, as carnivorous tardigrades were found only in peatlands with tree basal area > 20 m2 and mostly in hummocks. Our study demonstrates that the habitat heterogeneity of peatlands (e.g., variation in moisture and vegetation cover) represents an interesting system to study tardigrade ecology and habitat preferences. However, since we found variation in tardigrade abundance and communities across peatland types and microhabitats within peatlands, our results highlight that such studies should be conducted with numerous replicate samples and a systematic study design that properly addresses the habitat heterogeneity between and within different peatland types.

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth.

As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed. This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m). Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

Temperature and palaeolake evolution during a Middle Pleistocene interglacial–glacial transition at the Palaeolithic locality of Schöningen, Germany

The Middle Pleistocene Reinsdorf sequence at the Lower Palaeolithic sites of Schöningen offers the opportunity to reconstruct a rarely well‐preserved post‐Holsteinian environmental transition from an interglacial into a glacial phase along with its highly dynamic interjacent climatic oscillations. Combining biological proxies, element composition and stable isotope ratios of two lakeshore sequences at excavation site 13 II, we demonstrate repeated variations in climate, hydrology and catchment vegetation cover. New ostracod‐based quantitative mean summer and winter air temperature reconstructions with the Mutual Ostracod Temperature Range (MOTR) method provide the first detailed information about the temperature evolution. The interglacial temperature maximum, probably corresponding to Marine Isotope Stage 9e, is followed by a first dry phase and, during the younger part of the Reinsdorf sequence, by a second dry period. Both were marked by lower precipitation/evaporation ratios, reduced vegetation cover in the catchment and increased surface inflows from springs. Temperature reconstructions of these two steppe (open woodland) phases yield very narrow ranges for mean January (−4–0 °C) and July (+17–19 or +17–21 °C) air temperatures, demonstrating that, while summers were similar to those of today, winters were at least 1 °C colder, hinting at a more pronounced continental climate. Precise temperature estimates for the interjacent woodland and steppe (woodland) phase are hindered by generally wider ranges produced by the MOTR method (January mean −4–3 °C, July mean +15–21 °C). The development of a more extensive vegetation cover, reducing surface runoff and erosion in favour of increased river and groundwater discharge, as indicated by a shift in microfossil and stable isotope records, suggests generally more humid climates with higher precipitation/evaporation ratios as well as reduced seasonal temperature variations.

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000–2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

Dust model sensitivity to dust source mask, sandblasting efficiency, air density, and land use: Implications for model improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared with a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 12 times more dust than does a physics-based expression. Simulations using model-predicted versus constant air density differ by only 8%. However, this difference (often overlooked) could range between 12% and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The difference between simulations within each of the four pairs is generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Urban expansion and vegetation dynamics: The role of protected areas in preventing vegetation loss in a growing mega city

The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustainability. Protected areas (PAs) are the most effective tools to prevent the loss of urban vegetation cover and to control urban expansion. Hence, this study aims to assess the importance of PAs in protecting urban vegetation and the urban expansion in the mega city of Delhi. For this, Landsat datasets were used for land use and land cover (LULC) mapping and then land cover change rate (LCCR) and land cover intensity (LCI) were calculated. For assessing urban expansion dynamics, mean landscape expansion index (MLEI) and the area-weighted LEI (AWLEI) were calculated. To evaluate the significance of PAs in protecting vegetation cover, kernel density estimation (KDE) was applied to assess the spatial variation and concentration of vegetation cover under different PAs. The result shows that urban expansion in Delhi was initially characterized by edge expansion during 1991–2001, followed by outlying expansion of built-up area during 2001–2021, while infilling of open and vegetated areas by built-up area was consistent during 1991–2021. Vegetation cover on the other hand, has followed a fluctuating trend in the city, but has overall it has declined from 13.36% to 9.30% during 1991–2021. The vegetation cover has declined significantly in eastern, northern, and western parts of Delhi but has increased significantly in central and southern parts, especially during 2001–21. This is because the central and southern parts of Delhi are well planned and have several PAs while the western, northern, and eastern parts of Delhi are unplanned regions and have only a few PAs. The KDE chart shows that the PAs have played an important role in protecting the vegetation cover in Delhi with R2 value > 0.70. Hence, this study suggests to give special emphasis on preservation and expansion of PAs in urban planning for the long-term conservation of urban vegetation cover and sustainable urban development.

Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003-2022) in Nepal.

Understanding the vegetation dynamics and their drivers in Nepal has significant scientific reference value for implementing sustainable ecological policies. This study provides a comprehensive analysis of the spatio-temporal variations in vegetation cover in Nepal from 2003 to 2022 using MODIS NDVI data and explores the effects of climatic factors and anthropogenic activities on vegetation. Mann-Kendall test was used to assess the significant trend in NDVI and was integrated with the Hurst exponent to predict future trends. The driving factors of NDVI dynamics were analyzed using Pearson's correlation, partial derivative, and residual analysis methods. The results indicate that over the last 20 years, Nepal has experienced an increasing trend in NDVI at 0.0013 year-1, with 80% of the surface area (vegetation cover) showing an increasing vegetation trend (~ 28% with a significant increase in vegetation). Temperature influenced vegetation dynamics in the higher elevation areas, while precipitation and human interventions influenced the lower elevation areas. The Hurst exponent analysis predicts an improvement in the vegetation cover (greening) for a larger area compared to vegetation degradation (browning). A significantly increased area of NDVI residuals indicates a positive anthropogenic influence on vegetation cover. Anthropogenic activities have a higher relative contribution to NDVI variation followed by temperature and then precipitation. The results of residual trend and Hurst analysis in different regions of Nepal help identify degraded areas, both in the present and future. This information can assist relevant authorities in implementing appropriate policies for a sustainable ecological environment.

Mosquito population dynamics is shaped by the interaction among larval density, season, and land use.

Understanding how variation in key abiotic and biotic factors interact at spatial scales relevant for mosquito fitness and population dynamics is crucial for predicting current and future mosquito distributions and abundances, and the transmission potential for human pathogens. However, studies investigating the effects of environmental variation on mosquito traits have investigated environmental factors in isolation or in laboratory experiments that examine constant environmental conditions that often do not occur in the field. To address these limitations, we conducted a semi-field experiment in Athens, Georgia using the invasive Asian tiger mosquito ( Aedes albopictus ). We selected nine sites that spanned natural variation in impervious surface and vegetation cover to explore effects of the microclimate (temperature and humidity) on mosquitoes. On these sites, we manipulated conspecific larval density at each site. We repeated the experiment in the summer and fall. We then evaluated the effects of land cover, larval density, and time of season, as well as interactive effects, on the mean proportion of females emerging, juvenile development time, size upon emergence, and predicted per capita population growth (i.e., fitness). We found significant effects of larval density, land cover, and season on all response variables. Of most note, we saw strong interactive effects of season and intra-specific density on each response variable, including a non-intuitive decrease in development time with increasing intra-specific competition in the fall. Our study demonstrates that ignoring the interaction between variation in biotic and abiotic variables could reduce the accuracy and precision of models used to predict mosquito population and pathogen transmission dynamics, especially those inferring dynamics at finer-spatial scales across which transmission and control occur.

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.&#x0D; &#x0D;

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.