Search Results (86)

The arid and semi-arid regions of northwest China are characterized by sparse vegetation and fragile ecosystems, making them highly susceptible to the impacts of climate change and human activities. Based on observed meteorological data, the Normalized Difference Vegetation Index (NDVI), the Lund–Potsdam–Jena dynamic global vegetation model (LPJ), a vegetation recovery potential model, and the MK trend test method, this study investigated the spatiotemporal distribution of vegetation recovery potential in northwest China and its relationship with global warming and increasing precipitation. The results indicated that vegetation in northwest China significantly increased, with greening closely related to trends in warming and wetting during 1982–2019. However, the vegetation recovery potential declined due to climate change. Central and southern Xinjiang and central Qinghai exhibited higher grassland recovery potential, while the central Gobi Desert areas of northwest China had lower recovery potential. The eastern part of northwest China was highly sensitive to drought, with moderate vegetation growth and recovery potential. Remote sensing data indicated a 2.3% increase in vegetation coverage in the region, with an average vegetation recovery potential index (IVCP) of 0.31. According to the results of LPJ model, the average vegetation recovery potential index for northwest China was 0.14, indicating a 1.1% improvement potential in vegetation coverage. Overall, climate warming and wetting facilitated vegetation recovery in northwest China, particularly in mountainous areas. The findings provide valuable insights for ecological restoration efforts and offer practical guidance for combating desertification and enhancing sustainable development. Moreover, these results underline the importance of incorporating vegetation recovery potential into regional policy-making to improve environmental resilience in the face of ongoing climate change. Full article

Monitoring the location and severity of invasive plant infestations is critical to the management of their spread. Remote sensing can be an effective tool for mapping invasive plants due to its capture speed, continuous coverage, and low cost, compared to ground-based surveys. Serrated tussock (Nassella trichotoma) is a highly problematic invasive plant in Victoria, Australia, as it competes with the species in the communities that it invades. In this study, a workflow was developed and assessed for classifying the cover of serrated tussock in a mix of grazing pastures and grasslands. Using high-resolution RGB aerial imagery and vegetation field survey plots, random forest models were trained to classify the plots based on their fractional coverage of serrated tussock. Three random forest classifiers were trained by utilising spectral features (RGB bands and indices), texture features derived from the Grey-Level Co-occurrence Matrix, and a combination of all the features. The model trained on all the features achieved an overallaccuracy of 67% and a kappa score of 0.52 against a validation dataset. Plots with high and low infestation levels were classified more accurately than plots with moderate or no infestation. Notably, texture features proved more effective than spectral features for classification. The developed random forest model can be used for producing classified maps to depict the spatial distribution of serrated tussock infestation, thus supporting land managers in managing the infestation. Full article

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales. Full article

Ecological sluices were constructed along the Tarim River to supplement the ecosystem’s water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km² per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km² per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally. Full article

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management. Full article

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning. Full article

Building height (BH) estimation is crucial for urban spatial planning and development. BH estimation using digital surface model data involves obtaining ground and roof elevations. However, vegetation and shadows around buildings affect the selection of the required elevation, resulting in large BH estimation errors. In highly urbanized areas, buildings of similar heights often have similar characteristics and spatial proximity, which have reference significance in BH estimation but are rarely utilized. Herein, we propose a BH estimation method based on BIRCH clustering and a random forest (RF) model. We obtain the initial BH results using a method based on the optimal ground search area and a multi-index evaluation. BIRCH clustering and an RF classification model are used to match buildings of similar heights based on their spatial distance and attribute characteristics. Finally, the BH is adjusted based on the ground elevation obtained from the secondary screening and the BH matching. The validation results from two areas with over 12,000 buildings show that the proposed method reduces the root-mean-square error of the final BH results compared with the initial results. Comparing the obtained height maps shows that the final results produce a relatively accurate BH in areas with high shading and vegetation coverage, as well as in areas with dense buildings. Thus, the proposed method has been validated for its effectiveness and reliability. Full article

Human landscape-transforming activities contribute to the global change in vegetation in different forms. Hunting is one of the most ancient human landscape-shaping activities. Feeders for hunting are particularly disruptive to vegetation. In the present study, we conducted a vegetation survey in these highly disturbed places. We investigated the vegetation dynamics over several years in the turkey oak–sessile oak zone, in two areas with different moisture and shade conditions (forest and clearing). Important background factors are the changes in precipitation and temperature. Our results confirm that weed infestation is detectable at bait sites over a long period. The seasonal changes in field weed vegetation, as well as the increase in the number and coverage of weed species at the end of summer, resulting from lifestyle characteristics, were generally detectable in all years and locations, especially in the case of open and more strongly degraded clearings. Meteorological factors played a role in the degree of weed infestation in each year. Degradation was more significant in drought years, while regeneration was also observed in wetter periods. At baits located in the clearing, we showed a positive correlation between the amount of summer precipitation and the total coverage of weed species, as well as between the average spring temperature and the coverage of certain weed species. With the drying of the climate, the disturbed areas are constantly losing their natural value, but wetter weather is not an automatic solution either. Considering that there are approx. 30,000 bait sites in the country, and they are used regularly and very intensively, they can serve as major infection hotspots for alien species in a network. Full article

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R²: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R²: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R²: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications. Full article

The reservoir water level fluctuation zones (RWLFZs) and the natural riparian zones (NRZs) are two riparian ecosystems with dramatically opposite hydrological rhythms that notably influence the plant guilds. However, little is known about the discrepancies of the functional traits and niche characteristics of plant guilds in the RWLFZs and NRZs under different flooding rhythms. The aims of this study were to assess the divergent influence of natural and non-seasonal hydrological fluctuations on functional traits and niche characteristics of plant guilds. The results showed that 78 vascular plant species were identified in the riparian zones of the Xiangxi River basin. The dominant species were annuals in the two riparian ecosystems and their percentage increased temporally from 65.79% in the NRZs to 67.34% in the RWLFZs. Compared with the NRZs, the specific leaf area, vegetation coverage and the aboveground biomass in the RWLFZs significantly increased by 74%, 27% and 19.6%, respectively, while the water-use efficiency of the RWLFZ decreased by 59.6% and there was no significant difference in the net photosynthetic rate between them, which showed that annuals in the RWLFZs adopted the R adaptation strategy of being fast-growing with a short lifespan and quickly acquiring and investing resources by altering leaf morphology, including expanding the leaf area. The Simpson dominance index of RWLFZs was significantly higher than that of NRZ. Thus, counter-seasonally hydrological alterations had significant effects on functional traits of dominant species in the RWFLZs. Moreover, the highly adaptable and widely distributed species with larger niche breadths and high important values usually had a higher niche overlap value in the RWLFZs than in the NRZs, which showed that the competition in the plant communities after experiencing anti-seasonal flooding was much more intraspecific than interspecific and would facilitate the expansion of species niches. Our findings imply that the species with large niche breadths and high important values should be prioritized in ecological restoration efforts in the newly formed hydro-fluctuation zones of the TGR. Full article

An important consideration for UAV-based (unmanned aerial vehicle) object detection in the natural environment is vegetation height and foliar cover, which can visually obscure the items a machine learning model is trained to detect. Hence, the accuracy of aerial detection of objects such as surface landmines and UXO (unexploded ordnance) is highly dependent on the height and density of vegetation in a given area. In this study, we develop a model that estimates the detection accuracy (recall) of a YOLOv8 object’s detection implementation as a function of occlusion due to vegetation coverage. To solve this function, we developed an algorithm to extract vegetation height and coverage of the UAV imagery from a digital surface model generated using structure-from-motion (SfM) photogrammetry. We find the relationship between recall and percent occlusion is well modeled by a sigmoid function using the PFM-1 landmine test case. Applying the sigmoid recall-occlusion relationship in conjunction with our vegetation cover algorithm to solve for percent occlusion, we mapped the uncertainty in detection rate due to vegetation in UAV-based SfM orthomosaics in eight different minefield environments. This methodology and model have significant implications for determining the optimal location and time of year for UAV-based object detection tasks and quantifying the uncertainty of deep learning object detection models in the natural environment. Full article

The many publications on forests and moth communities accomplished in different sampling regions and habitat types have produced diverse results and conclusions. The multiplicity of outcomes requires regional or local investigations on forest traits and herbivores to determine optimal management methods to maintain biodiversity and ecological stability in woodlands. Our study focused on sessile oak–hornbeam forests, which are economically and ecologically significant in many European countries. Samplings were performed in 2011–2012 using portable light traps in a highly forested area in western Hungary. We used 16 variables for PCA from the sampling of vascular plants and the local forest management plan document. These newly created variables (i.e., principal components) were related (used generalized linear models) to different groups of sampled moth communities: Macrolepidoptera, Microlepidoptera, and ecological groups (according to the host vegetation layer). Based on these significant relations, thinning activity may have various effects on moth communities, through the changed light regime and microclimate conditions. Temperature growth in the gaps could lead to the increasing abundance of heat-preferred Lepidoptera species; however, the decreasing species richness of trees (as a result of thinning) is less favourable for moth assemblages. Increasing herb coverage supports moth communities in the investigated forest stands, which may also be induced by the lower canopy closure. Besides the increasing coverage in the lower vegetation layers, plant species richness is also an important element for moth communities; this was demonstrated by the negative relation between the PC4, PC6 (weighted toward coverage), and Lepidoptera groups. Our results supported the fact that a single study on forest management practice or on vegetation traits is not sufficient to indicate their exact effect on moth communities, because their influence is complex. In order to halt the loss in diversity of the examined forest type, we suggest an overall approach to define the optimal forest management practice and tree mixture rate, regarding a larger area. Full article

Northeast China’s sandy region is an arid and semi-arid zone highly susceptible to climate change. Investigating the long-term changes in the Northeast China sandy land (Northeast China sandy land, DBSL) landscape can provide an important basis for the ecological restoration of this region. This study analyzed long-term remote sensing data of the DBSL from 1980 to 2022 and explored the spatial pattern, evolution, and driving mechanisms. In 2022, vegetation was mainly distributed in the northwest, center, and southwest, covering a total area of 30,508.82 km². Areas with high and medium vegetation cover showed strong aggregation characteristics and were mainly distributed in the southwest, whereas those with low vegetation coverage were highly dispersed and widely distributed in the central region. Lakes were widely distributed in the northwest and central regions, with a total area of 2736.43 km². In the last 42 years, the vegetation cover decreased by 24.48%. Areas with high and medium vegetation coverage decreased in size, and those with low vegetation coverage first increased and then decreased, with overall decreases of 35.35%, 19.16%, and 6.88%, respectively. The overall area of the DBSL showed various degrees of degradation. Shrinking and dry lakes were concentrated in the sandy hinterland. The lake landscape changed significantly from 1990 to 2010, with a decrease in lake area of 27.41%. In contrast, the sandy area increased by 25.65%, indicating a high degree of desertification. However, from 2005 to 2022, desertification decelerated. The most important factors driving the evolution of the DBSL were socio-economic factors. The increase in human disturbance will have a certain impact on the landscape changes in the region in the short term. The national policy of returning farmland to fields and grasslands will affect the increase of vegetation and lake landscape area in the short term, and the sand area and excessive animal husbandry will be reduced. This study provides a scientific basis for ecological restoration and sustainable development in Northeast China. Full article

Land use and rainfall are two important factors affecting soil erosion processes. The red soil region of southern China is a representative region with high rainfall amounts and rapidly changing land use patterns where the water erosion process is sensitive to changes in land use and rainfall. To comprehensively understand the water erosion response to land use and rainfall in this region, a 6-year in situ experiment based on eight plots (bare land and seven typical land uses) was conducted from 2015 to 2020. The 320 rainfall events were divided into 4 types, and there were 3 main rainfall types. The runoff of different rainfall types was primarily determined by the rainfall amount, while the soil erosion of different rainfall types was primarily determined by the rainfall intensity. High-intensity rainfall contributed the most to both total runoff and soil erosion. Compared with bare land, the seven typical land uses reduced runoff and soil erosion by more than 75%. Grassland, cropland, and forest with low vegetation coverage experienced high runoff and soil erosion, while shrubland most effectively reduced runoff and soil erosion. The combination of land use and rainfall type significantly affected the annual average runoff depth, soil erosion modulus, and soil loss coefficient. Rainfall types can change the relationship between runoff and soil erosion for different land uses. The runoff and soil erosion of bare land were highly correlated with rainfall characteristics, while vegetation weakened this relationship under short- or moderate-duration rainfall. To effectively reduce water erosion, high-intensity rainfall should receive special attention, and all land uses should ensure that vegetation is well developed, especially understory vegetation. Full article

The timber industry has increased considerably in recent decades to meet human needs for wood. In Portugal, Eucalyptus plantations are the most common use of forested land, presenting the largest coverage of Eucalyptus globulus in Europe. Although it is established that this landscape can affect biodiversity patterns, it is not clear what its role in shaping small mammals’ body condition is. Here, we tested the effect of Eucalyptus plantations on small mammals’ body condition, together with vegetation structure, weather, predators/competitors’ abundance, and parasites’ prevalence, using the Scaled Mass Index (SMI) as a surrogate. Capture of small mammals took place in 11 study areas in central Portugal from 2019 to 2022. The drivers’ influence was tested using structural equation models (SEM). The response of body condition to Eucalyptus is species-specific, with Crocidura russula displaying better individual condition in native habitats (i.e., there was an indirect negative effect of Eucalyptus plantations). The overall model suggested that deer abundance, precipitation, and forest integrity promoted higher body condition levels, while wild boar abundance had an adverse effect. The management of these plantations must ensure the integrity of the remnants of native patches and control of highly abundant competitors (e.g., wild boar) to maintain a healthy and functional small mammal community. Full article