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Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. [ABSTRACT FROM AUTHOR]

This paper presents an annotated checklist of the Basidiomycota taxa (including lichenicolous fungi and the subdivision Pucciniomycotina) from the Azores archipelago and reviews the published records to account for their taxonomic status. The number of Basidiomycota species recorded in the Azores has increased considerably during the 20th century and now stands at 544 species. This study provides distribution data and includes changes in the nomenclature of the listed taxa. Sampling campaigns contributed to 116 new records of Basidiomycota for the Azores archipelago. In addition, there were new records for eight islands: 162 species found for the first time on São Miguel Island, 55 species new to Santa Maria Island, 33 species new to Flores Island, 15 species new to Terceira Island, 9 species new to Pico Island, 17 species new to São Jorge Island, 4 species new to Graciosa Island, and 2 species new to Corvo Island. The transformation of vegetation cover in the archipelago has been very drastic, and this is reflected in the presence of many foreign fungal species on the islands. From these data, we conclude that within Macaronesia, the diversity of Basidiomycota in the Azores is more similar to that in Madeira than in the Canary Islands. [ABSTRACT FROM AUTHOR]

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the 'Google Earth Engine' database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city's land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations. [ABSTRACT FROM AUTHOR]

This paper presents the first results of palynological research from the Spila nad Procjepom cave, situated in the Mljet National Park, Croatia. The palynological data obtained, enables a partial insight into the local vegetation cover, temporal changes in the vegetation during the accumulation of studied deposits (at ca. 3500 cal years BP), and post-depositional processes that influenced the palynomorph assemblage. Results of palynofacies analysis indicate changes from fluvial (channel deposits), through palustrine to terrestrial environments. Although the interpretation of changes in plant cover, due to the lack of statistical significance, should be taken with caution, preserved pollen types confirm the dominance of the Mediterranean evergreen forest vegetation on Mljet island. Moreover, a high proportion of non-arboreal pollen (NAP) indicates some level of forest degradation, ranging from Mediterranean open forest to degraded maquis. Abundant charcoal additionaly confirms that the cave was inhabited by humans. [ABSTRACT FROM AUTHOR]

Criminal investigations aiming to track the route walked by missing persons and fugitives (MPFs) usually involve intelligence analysts, military planners, experts in mobile forensics, traditional investigative methods, and sniffer dog handlers. Nonetheless, when MPFs are devoid of any technological device and move in uninhabited rural areas devoid of tele cameras and densely covered by vegetation, tracking the route walked by MPFs may be a much more arduous task. In the XVIII century, the expert Georg Popp was able to link a homicide suspect to a sequence of different sites of criminal interest, located in the countryside, by studying the stains of soils found on the footwear and trousers of the suspect. In such complex cases, a very efficient approach for tracking the route walked by MPFs may consist of comparing the geological traces found on the MPFs and their belongings with soils exposed in the event scenes. In particular, the search for peculiar or rare particles and aggregates may strengthen the weight of the geological forensic evidence comparisons. A match of mineralogical, textural, and organic matter data may demonstrate the provenance of the traces from the soil of a specific site, thereby linking the MPFs to the scene of events. Based on the above, the present paper reports geological determinations accomplished for a "mediatic" casework. The results allowed a general high degree of compatibility among traces collected on the MPFs and on the soil from the scene of events to be ascertained. The most significant positive matches, based on the finding of ten peculiar and rare particles and assemblages, allowed the reconstruction of a route about 1.1 km long, as the crow flies, on the event site. Although this procedure was extremely time consuming and available only in a backwards reconstruction linked to the MPFs' findings, it was of uttermost importance in strengthening the inferences proposed, and for which other methods could not provide any information. [ABSTRACT FROM AUTHOR]

Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth's vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase. [ABSTRACT FROM AUTHOR]

The Taiyuan Xishan Ecological Restoration Zone is located in the west of Taiyuan City and belongs to the Xishan Coalfield. Due to the resource development activity of coal mining, which is caused by coal gangue accumulation, surface vegetation degradation, bare surfaces, and other phenomena, it is most common in this area. These have an impact on the surface ecology; however, after ecological restoration, the surface ecology has been greatly improved. There are many extraction models of vegetation coverage based on pixel dichotomology combined with multispectral vegetation index, but we believe that the combination of visible light vegetation index to construct models is relatively unexplored. The main problem of how to use the RGB image data in order to quickly and accurately extract vegetation coverage information is still under investigation and needs researchers' attention. In this paper, through selecting the vegetation coverage as the evaluation index of ecological restoration effect, a new RGB vegetation coverage CIVE calculation model is innovatively proposed to solve the above problem, and on the basis of this model, the vegetation cover change analysis is carried out in the Xishan ecological restoration area of Taiyuan. According to the analysis of vegetation coverage change, relevant paper data, and the characteristics of multiple historical remote sensing images, the ecological restoration area of Taiyuan Xishan is divided into six typical areas. Through empirical evaluation, we summarize and analyze these six typical areas, which can provide typical demonstration roles for other ecological restoration areas. Our findings suggest that the proposed CIVE model realizes the extraction of vegetation cover information and long-term series dynamic monitoring of vegetation coverage. [ABSTRACT FROM AUTHOR]

A systematic understanding of the spatio-temporal changes and driving factors in the Qinghai-Tibet Plateau holds significant scientific reference value for the future of ecological sustainable development. This paper utilizes MODIS normalized difference vegetation index (NDVI) and meteorological data to investigate the spatio-temporal changes and driving factors of vegetation coverage in the Qinghai-Tibet Plateau from 2001 to 2020. Methods employed include the dimidiate pixel model, trend analysis, partial correlation analysis, and residual analysis. The results demonstrate a generally fluctuating upward trend in vegetation coverage across the Tibetan Plateau over the past two decades, with spatial expansion occurring from northwest to southeast. Vegetation coverage exhibits a positive correlation with climate factors. Approximately 60.7% of the area showed a positive correlation between vegetation fractional cover (FVC) and precipitation, with 8.66% of the area demonstrating extremely significant (p < 0.05) and significant (p < 0.01) positive correlation. Human activities, on the whole, have contributed to the enhancement of vegetation cover in the Qinghai-Tibet Plateau. The areas where human activities have positively impacted vegetation cover are primarily situated in north-central Qinghai and north of Ngari, while areas experiencing degradation include certain grassland regions in central-eastern Yushu, Nagqu, and Lhasa. [ABSTRACT FROM AUTHOR]

This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named "CEU Paz" (CP) and "Parque Santa Madalena I" (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork. [ABSTRACT FROM AUTHOR]

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region's net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance. [ABSTRACT FROM AUTHOR]

Predicting the behaviour of wildfires can help save lives and reduce health, socioeconomic, and environmental impacts. Because wildfire behaviour is highly dependent on fuel type and distribution, their accurate estimation is paramount for accurate prediction of the fire propagation dynamics. This paper studies the effect of combining automated hyperparameter tuning with Bayesian optimisation and recursive feature elimination on the accuracy of three boosting (AdaB, XGB, CatB), two bagging (Random Forest, Extremely Randomised Trees), and three stacking ensemble models with respect to their ability to estimate the vegetation cover type from cartographic data. The models are trained on the University of California Irvine (UCI) cover type dataset using five-fold cross-validation. Feature importance scores are calculated and used in recursive feature elimination analysis to study the sensitivity of model accuracy to the different feature combinations. Our results indicate that the implemented fine-tuning procedure significantly affects the accuracy of all models investigated, with XGB achieving an overall accuracy of 97.1% slightly outperforming the others. [ABSTRACT FROM AUTHOR]

High urbanization and a consequent change in land cover can lead to a deterioration in air quality and generate impacts on public health. The objective of this paper is to provide a multitemporal analysis of the influence of particulate matter ≤ 10 μm (PM10) on human mortality from the land cover variation in a Latin American megacity. Six monitoring stations (monitoring daily PM10 concentration, increases in daily mortality (IDM), and land cover) were established throughout the megacity. The results suggest that for every 10% increase in vegetation cover, the daily PM10 concentration and IDM decreases by 7.5 μg/m3 and 0.34%, respectively. Moreover, it is evident that the monitoring station with the lowest vegetation cover (8.96 times) shows an increase of 1.56 times and 4.8 times in the daily PM10 concentration and IDM, respectively, compared with the monitoring station with the highest vegetation cover (46.7%). It is also suggested that for each increase of 100 inhabitants/hectare in population density, the daily PM10 concentration and IDM increases by 9.99 µg/m3 and 0.45%, respectively. Finally, the population densification of the megacity possibly implies a loss of vegetation cover and contributes to the increase in PM10 and IDM. [ABSTRACT FROM AUTHOR]

In this paper, integrating field surveys and literature data, an analysis of Ophioglossum lusitanicum plant communities and related 92/43/EEC habitats are reported for Italy. Two new syntaxa, Euphorbio exiguae-Ophioglossetum lusitanici ass. nova hoc loco and trifolietosum scabri subass. nova hoc loco of the Rumici bucephalophori-Ophioglossetum lusitanici were described in the Apulia and Campania regions.Both types of vegetation identified in Apulia, Campania, and Sicily regions represent two different aspects of the same priority habitat: "pseudo-steppe with grasses and annuals of the Thero-Brachypodietea" (habitat code 6220*). A phytosociological and ecological dataset of the literature and new field surveys highlighting the soil type as parameters affecting the vegetation cover of this small fernlike plant, with the Trachynion distachyae Rivas–Martínez, 1978 alliance on calcareous soils and Helianthemion guttati Br.-Bl. in Br.-Bl. et al., 1940 alliance on volcanic soils. Many species of other types of annual meadows have been identified within Ophioglossum communities due to the very small patches of land, where they have been found, and ecological conditions that facilitate this phenomenon of the transgression of other therophytes species. [ABSTRACT FROM AUTHOR]

The paper presents the results of research on competitive relations between sessile oak seedlings and the most important competing species of woody vegetation, as well as ground flora that grows under a shelterwood, after the preparatory - seed cut of the shelterwood cutting. The research was conducted in sessile oak (Quecus petraea/Matt./Liebl.) stand in the area of northeastern Serbia. The studied stand is located at 320 to 350 m above sea level, with a slope up to 25° and western exposure. In terms of phytosociology, the stand is defined as an association of sessile oak with hairy sedge (Carici pilosae - Quercetum petraeae B. Jov. 1989). After the preparatory-seed cut, 124 trees per ha remained in the stand, with a basal area of 11.9 m2/ha and a volume of 129.1 m3/ha. The obtained results in the paper indicate the competitive relations between sessile oak and the most important competing woody species, as well as the species of ground vegetation. The most common competing woody species are silver linden (Tilia tomentosa Moench.) and hornbeam (Carpinus betulus L.), while other species (Fraxinus excelsior L., Fraxinus ornus L., Crataegus monogyna Jacq., Acer campestre L., Cornus mas L.) are represented individually. In addition to a significant presence, competing species of woody vegetation are characterized by very intensive development, which significantly reflects on the presence and development of sessile oak seedlings. In contrast, eight-year-old sessile oak seedlings have outgrown blackberries in the studied stand, which no longer competes with them. The obtained results indicate that during the regeneration of sessile oak forests, special attention must be focused on the presence and dynamics of the development of competing vegetation, as well as that the success of the regeneration largely depends on controlling this factor. [ABSTRACT FROM AUTHOR]

This paper details the Metamorphosis Shelter, an unprecedented record of the archeological wealth of Paraná. Located in the municipality of Piraí do Sul, in the Campos Gerais region of Paraná, this archaeological site surprises not only by the quantity of panels and paintings, but also by the good state of conservation of some groups and by the diversity of representations and styles used. All the panels with rock paintings were photo registered and digital decals were made from the filtered images treatment on DStretch software using the correlation matrix technique in three phases image editing program - automatic, semi-automatic and manual. The rock panels were mapped and geospatialized by speleological topography based on the Union Internationale de Spéléologie (UIS) method of grading and speleometric conventions. Thirty-two panels were documented, amounting to 887 representations ranging from zoomorphic, anthropomorphic, geometric figures, built structures, enigmatic forms, and vestiges. Although the site is reasonably well preserved, urgent actions are needed for the effective protection of this site to avoid degradation of the archaeological content by burning, free circulation of cattle, vegetation cover, and uncontrolled visitation. [ABSTRACT FROM AUTHOR]

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation. [ABSTRACT FROM AUTHOR]

The present research paper aims to study and shed light on the spatial change of vegetation coverage in the Algerian steppe region. The investigation is based on remote sensing and geographic information systems through the analysis of satellite visuals in calculating the vegetation coverage index (Ndvi) for three different periods 1985 and 2022, with consideration to remote sensing and information systems. Geography is among the most important digital means that work to collect, analyze and study the geographical field in all its dimensions, and it also works to follow up on the changes taking place in it. The latter shows the most important changes in the vegetation cover in the study area. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive semi-distributed water balance model is presented for basins under anthropogenic impacts. It benefits from data in different time resolutions for estimation of surface and groundwater exchanges and water withdrawals for various purposes. Ground-based data such as vegetation cover and topography have been used to determine the model parameters. The performance of the proposed model was tested for 38 sub-basins in Iran representing various climate conditions, vegetation covers, and agricultural practices. The results demonstrated a good performance in terms of estimating streamflows at the basin outlet, with more than 90% of sub-basins having a correlation coefficient above 0.9. However, the model showed a relatively weaker performance in the estimation of annual aquifer storage volume change. Correlation coefficients above 0.6 were calculated for more than half of the aquifers in the study area. [ABSTRACT FROM AUTHOR]

The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time. [ABSTRACT FROM AUTHOR]

The Grain for Green Project (GGP) has considerably improved the vegetation cover of the Loess Plateau, as well as changed the carbon and water coupling process of local vegetation to a certain extent. Water use efficiency (WUE) is a crucial measure for evaluating ecosystem responses to global climate change and is a key indicator of the carbon–water coupling between terrestrial ecosystems and the environment. A comprehensive understanding of the impact of vegetation reconstruction on WUE on the Loess Plateau is of great significance to the vegetation growth and contribution to sustainable of the Loess Plateau. In recent years, scholars have gained a more comprehensive understanding of the distribution and drivers of WUE on the Loess Plateau. However, through the study of carbon and water coupling in the Loess Plateau, it is found that the effects of different vegetation restoration levels on WUE are still to be studied in depth in terms of spatial and temporal heterogeneity and long timeseries. In this paper, we analyzed the trends of Normalized Difference vegetation cover (NDVI) and WUE from 2001 to 2010 and 2011 to 2020, respectively, to research at the WUE of the vegetation in this area in relation to vegetation restoration. It was found that the Loess Plateau's vegetation WUE rose from 2001 to 2020 at a rate of 0.023 g C kg−1 H2O per year, and that the increase from 2011 to 2020 was more significant than the growth from 2000 to 2010. The Loess Plateau's area with a growing trend in vegetation water use rate increased from 77.12% in 2001–2010 to 88.63% in 2011–2020, with the majority of the increased area occurring in the northeastern Inner Mongolia region. After 20 years of the reforestation project, the area where NDVI and WUE increased simultaneously accounted for 71.54% of the Loess Plateau, the area where NDVI increased but WUE decreased accounted for 10.95% of the Loess Plateau, and the area where NDVI increased but WUE decreased accounted for 7.15% of the Loess Plateau. The correlation between temperature precipitation and WUE was not significant for the whole Loess Plateau, further indicating that the increase in vegetation cover was the main reason for the increase in vegetation water efficiency. Therefore, the effect of vegetation cover on WUE should be fully considered when vegetation restoration is carried out on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation's colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices. [ABSTRACT FROM AUTHOR]

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is proposed. In this paper, a summer suitable index (SSI) for Zhejiang is calculated, including four secondary indices: a summer cool index (SCI), a comfort days index (CDI), a good air days index (GADI) and a vegetation cover index (VCI). Using a local evaluation criterion, summer climate suitable areas are distinguished objectively according to the SSI. The results show that especially suitable regions account for 4.97% of Zhejiang Province, very suitable regions account for 22.2%, suitable regions account for 39.58%, and general regions account for 33.25%. The summer climate suitable areas are located mainly in high mountains and hills and coastal island areas while plain areas cannot be considered a suitable destination for summer tourism. By comparison and discussion, the SSI is demonstrated to capture summer climate suitability well. In contrast to a fixed evaluation index, benchmark values obtained for the SSI depend on the local climate and the index is straightforward to apply. [ABSTRACT FROM AUTHOR]

As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai–Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales. [ABSTRACT FROM AUTHOR]

In early studies, northern vegetation response to global warming recognised both increases in biomass/cover and shrinking of species' distributional ranges. Subsequent field measurements focussed on vegetation cover and biomass increases ("greening"), and more recently decreases ("browning"). However, satellite observations show that more than 50% of arctic vegetation has not changed significantly despite rapid warming. While absence of change in remote sensing data does not necessarily mean no ecological change on the ground, the significant proportion of the Arctic that appears to be stable in the face of considerable climate change points to a greater need to understand Arctic ecosystem stability. In this paper, we performed an extensive review of the available literature to seek balances or imbalances between research focussing on "greening", "browning" and "stability/no change". We find that greening studies dominate the literature though two relatively small areas of the Arctic are disproportionately represented for this main change process. Critically, there are too few studies anywhere investigating stability. We highlight the need to understand the mechanisms driving Arctic ecosystem stability, and the potential longer-term consequences of remaining stable in a rapidly changing climate. [ABSTRACT FROM AUTHOR]

Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

In this paper we present the importance of landslide susceptibility mapping for territorial planning applications on local and regional transportation networks. The case study is focused on the Timiş-Cerna Valley (Romania), along the Orient-East Mediterranean TEN-T Corridor. We developed the prediction model based on a landslide inventory produced with high-resolution aerial imagery, field study observations and information available in similar geomorphological studies. A selection of determining factors, such as lithology, slope aspect and inclination, fault distance, hydrological parameters and vegetation cover has been cross-referenced with landslide-affected regions in order to draw relevant conclusions. The landslide susceptibility map was based on the relative importance of these parameters, determined by means of cartographic algebra techniques and use of bivariate statistical methods in GIS environment. The results of the prediction analysis, validated with the Receiver Operating Characteristic (ROC) curve, were satisfactory. Finally, the overlay analysis of landslide-susceptible areas and the transportation network provided insight related to the most vulnerable areas at the level of territorial administrative units (TAUs). The importance of landslide zonation map was also featured with respect to the design of high-speed transportation routes across the Timiş-Cerna Corridor. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) estimation using coarse resolution satellite data, like Scatsat-1 scatterometer data (2 km spatial resolution), is still challenging due to various land cover classes present within a single pixel. To estimate SM from coarse resolution data, there is a necessity to study the contribution of different land covers to the total backscatter signal of the single pixel. The estimation of SM is very much dependent on the accuracy of the backscatter signal from the soil component. Therefore, this paper proposes a modified water cloud model to compute the soil-backscattering signal by correcting the backscatter contribution due to urban and vegetation fraction cover. The backscattering signal from the soil surface is then used to retrieve SM. The retrieved SM is compared with the ground truth SM using the error metrics such as root mean square error (RMSE), mean bias (b), correlation coefficient (r), and average relative error (ARE). The proposed model is tested in three conditions: first, when only vegetation correction is applied, RMSE, mean bias, r, and ARE are found to be 0.086 m3/m3, 0.70 m3/m3, 0.534, and 0.24, respectively, and results show an overestimation in SM. Second, when both vegetation and urban correction are applied using the actual fraction, the accuracy is improved with an RMSE, mean bias, and ARE of 0.071 m3/m3 , 0.056 m3/m3, and 0.20, respectively. However, in the third condition, when the urban fraction cover is considered twice the actual value, the RMSE and mean bias are found to be 0.22 m3/m3 and −0.10 m3/m3, respectively, and results show an underestimation in SM. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies. [ABSTRACT FROM AUTHOR]

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate the C factor at a regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiments, and needs the coverage ratio data of croplands, woodlands, and grasslands at a standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. The results show that: (1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands, and grasslands on a km-sized grid, and takes the C factor formula obtained from the standard plot experiment and applies it to a regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas; (2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage; (3) The C factor of the study area is smaller in the middle, southern, and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands, and the valleys affected by human activities; (4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on a large scale and provide strong support for regional soil and water conservation planning. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) surveying of a construction site using an image-based method may produce incorrect ground elevation results at vegetation-covered regions, because the light rays are reflected on the surface of vegetation in front of the "truth" ground. This paper presents a convolutional neural network (CNN) method to identify and locate static vegetation using drone-based high-resolution orthoimages. The developed CNN-based image classification models are supplemented with an overlapping disassembling algorithm to generate 8×8-pixel , 16×16-pixel , 32×32-pixel , or 64×64-pixel small-patches as model inputs. The training datasets are 10 pairs of 1,536×1,536-pixel orthoimage and label-image dataset. Experimental results show that cropping a high-resolution image into 9,025 overlapped 32×32-pixel small-patches (with a site size of 17.28×17.28 cm2) for image classification, and assembling the small-patch label-image predictions to a patch-wise label-image prediction, has the average pixel accuracy of 92.6% in identifying objects on the experimental site. In addition, a vegetation-removing algorithm is designed to divide the label-image prediction into 36,864 nonoverlapping 8×8-pixel patches and traverse them in 192 row-loops and 191 column-loops. The testing results show vegetation in label-images are modified with the "truth" ground elevation and verified with two datasets obtained on different dates. In addition, the measured elevation differentials are close to the measured vegetation heights on the experimental site. This research has advanced the drone-based orthoimaging method in construction site surveying, which can automatically identify the static obstacles and determine the ground elevations more accurately. Furthermore, an approach of using a CNN model to segment a construction site has been proven feasible. [ABSTRACT FROM AUTHOR]

The process-based model 4C (FORESEE) has been developed over the past 20 years to study climate impacts on forests and is now freely available as an open-source tool. The objective of this paper is to provide a comprehensive description of this 4C version (v2.2) for scientific users of the model and to present an evaluation of 4C at four different forest sites across Europe. The evaluation focuses on forest growth as well as carbon (net ecosystem exchange, gross primary production), water (actual evapotranspiration, soil water content), and heat fluxes (soil temperature) using data from the PROFOUND database. We applied different evaluation metrics and compared the daily, monthly, and annual variability of observed and simulated values. The ability to reproduce forest growth (stem diameter and biomass) differs from site to site and is best for a pine stand in Germany (Peitz, model efficiency ME=0.98). 4C is able to reproduce soil temperature at different depths in Sorø and Hyytiälä with good accuracy (for all soil depths ME > 0.8). The dynamics in simulating carbon and water fluxes are well captured on daily and monthly timescales (0.51 < ME < 0.983) but less so on an annual timescale (ME < 0). This model–data mismatch is possibly due to the accumulation of errors because of processes that are missing or represented in a very general way in 4C but not with enough specific detail to cover strong, site-specific dependencies such as ground vegetation growth. These processes need to be further elaborated to improve the projections of climate change on forests. We conclude that, despite shortcomings, 4C is widely applicable, reliable, and therefore ready to be released to the scientific community to use and further develop the model. [ABSTRACT FROM AUTHOR]

The Bolsa Verde Program is a pioneering activity in the state of Minas Gerais, Brazil, established by State Law 17.727/2008 and regulates State Decree 45.113/2009. It focuses on the preservation of the native vegetation covering of the state through the Payment for Environmental Services that rewards rural proprietors to preserve and recover priority areas defined by the authorities. Current paper investigates the principles and bases of the program to discover its weak points and the motives for its non-effectiveness. Legislation, including resolutions and decrees, was analyzed, and an investigation of government data and academic works was provided. Although the Bolsa Verde program is not the sole public policy program to solve the environmental problems of the state, it is a strategic program since it stimulates the maintenance and recovering of native vegetation coverage. In fact, the Bolsa Verde may promote environmental and citizen conscience-awareness and improve the life style of the people concerned. Aims were compromised since there was no continuity, or rather, with only two maintenance modalities and nonpayment. The reactivation of the program is required and it is demanded that the state of Minas Gerais complies with the established clauses, particularly payment so that policies of conservation support are respected and valorized by society. [ABSTRACT FROM AUTHOR]

Dexing City is an important mining city in China, abounding in copper ore, lead ore, zinc ore, and other metal resources, and there are two large open-pit mines in its territory, Dexing Copper Mine and Yinshan Mine. The two open-pit mines have been expanding their mining production scale since 2005, with frequent mining activities; and the expansion of the pits and the discharge of solid waste will undoubtedly increase the land use and cause the destruction of vegetation. Therefore, we plan to visualize the change in vegetation cover in Dexing City from 2005 to 2020 and the expansion of the two open-pit mines by calculating changes of the Fractional Vegetation Cover (FVC) in the mining area using remote sensing technology. In this study, we calculated the FVC of Dexing City in 2005, 2010, 2015 and 2020 using data from NASA Landsat Database via ENVI image analysis software, plotted the FVC reclassified maps via ArcGIS, and conducted field investigations in the mining areas of Dexing City. In this way, we can visualize the spatial and temporal changes of vegetation cover in Dexing City from 2005 to 2020, and appreciate the situation of mining expansion and its solid waste discharge in Dexing City. The results of this study showed that the vegetation cover of Dexing City remained stable from 2005 to 2020, as the expansion of mining scale and mine pits was accompanied by active environmental management and land reclamation, setting a positive example for other mining cities. • The change in FVC from 2005 to 2020 was used to visualize the impact of mining expansion in Dexing City, China. • The paper presented Dexing City as an example of a mining city that has achieved "healthy" development. • This paper described the environmental hazards associated with open-pit mining. [ABSTRACT FROM AUTHOR]

Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects. • An innovative and interesting example of urban environmental management is presented. • A model for environmental management of the urban mine, Dartford Eastern Quarry, is reported. • This paper visualized mine land reclamation and urban construction progress using remote sensing. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the impact of forest cover dynamics on the erosion at Kharouba watershed in the Moroccan Central Plateau. Our methodology is based on the diachronic analysis of the land cover between 1986 and 2016, using treatments of aerial photographs from 1986 and a Google Earth satellite imagery at a high resolution from 2016. The land cover maps obtained were used to establish erosion status maps between 1986 and 2016, based on methodology PAP/RAC. Our study highlights the regressive evolution of vegetation cover. This decline has stressed the increased water erosion risk in Kharouba watershed causing the spread of the areas that are vulnerable to water erosion from 36% in 1986 up to 41% in 2016. [ABSTRACT FROM AUTHOR]

This paper aims to present the use of various treatments for the differential application of nitrogen in the precision farming system. To assess the state of the vegetation cover, both ground-based observations and associated remote sensing methods were used. Assessment of the state of plants (Menkovsky of AFI, 2009-2011) was carried out according to the phases of their growth and development using an N-tester in the field, as well as analysis of plant samples in a specialized laboratory. Remote sensing was carried out at the time of the analysis of plant samples using unmanned aerial vehicles equipped with cameras that allow shooting in different areas of the spectrum. The test sites with predetermined doses of nitrogen fertilizers for decrypt the obtained images were used. It is shown that for differential application of nitrogen fertilizers in spring wheat crops it is advisable to use the optical characteristics of the state of plants performed using calibration test sites. We have found that the maximum yield in the differentiated nitrogen applications treatment (TK-4) was 4510 kg per hectare (kg ha-1). At the same time, the minimum in the TK-1 treatment was 3780 kg ha-1. On average, over the years of research differentiated fertilizer application increased the collection of protein per hectare by 15-17%. In the TK-4 treatment for three years, a grain was obtained that corresponded to the first quality class (exceed 14%) in terms of protein content. Our novel research has shown that for characterizing the state of plants an assessment is given which was performed using calibration test sites. [ABSTRACT FROM AUTHOR]

This paper aimed to optimize the method of obtaining the intrinsic vulnerability index of the aquifers to the pollution (IVIAP) conferring to the variable infiltration and recharge the matrix combination between the slope intervals and the relative density of vegetation cover. In addition to the collection of hydrolytic data from the aquifer units, the Geographic Information System (GIS) was used to map 34 springs in Rondonópolis, MT, compile data from the groundwater table of tubular wells, and correct satellite and radar images from medium-high spatial resolutions. The IVIAP was identified as an alternative, simple and effective method to qualify vulnerabilities to aquifer pollution, especially in regions where there is a shortage of hydrological information. [ABSTRACT FROM AUTHOR]

In the context of recent climate changes and their impact on the vegetation cover in the southern shore of the Mediterranean, this paper deals with the potential distribution in the present and the future of the Aleppo pine in the north‐east of Algeria. Accordingly, this study focuses on modelling of the spatiotemporal distribution of the Aurasian Aleppo pine, by means of MaxEnt program. The models developed with MaxEnt showed good predictivity with AUC > 0.80 for the RCP 4.5 and RCP 8.5 scenarios projected, respectively, for 2055 and 2085. The results showed that the annual thermal amplitude followed by altitude appear to be the main factors of the spatiotemporal distribution of Aleppo pine in the study area. Under current conditions, only 20% of the territory seems favourable to the presence of the Aleppo pine. However, future predictions expect an extension of the areas classified as "moderately favourable" to the Aleppo pine. In response to climate changes, the Aurasian Aleppo pine may display two contrasted tendencies: a progressive evolution in the north, and a regressive evolution in the south of the Aurès region as well as a displacement of suitable areas for Aleppo pine to the north. [ABSTRACT FROM AUTHOR]

The Abbay River Basin, which originates in Ethiopia, is a major tributary and main source of the Nile River Basin. Land cover and vegetation in the Abbay River Basin is highly susceptible to climate change. This study was conducted to investigate the trends of climate change for a period of thirty-six years (1980–2016) within selected stations of the basin by using the innovative trend analysis method, Mann–Kendall test, and Sen's slope estimator test to investigate the mean annual precipitation and temperature variables. Changes in land cover and vegetation in the Abbay River Basin were studied for a period of thirteen years (2001–2013) by using remote sensing, GIS analysis, land cover classification, and vegetation detection methods to assess the land cover and vegetation in the basin. In addition, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Transformation Matrix were employed to analyze the spatial and temporal patterns of land cover and vegetation impacted by changes in climate. The result reflects that the trend of average annual temperature was remarkably increased (Φ = 0.12, Z = 0.75) in the 36-year period, and the temperature was increased by 0.5°C, although precipitation had slightly decreased during the same period. In the thirteen years' period, forest land and water resource decreased by 3429.62 km2 and 81.45 km2, respectively. In contrast, an increment was observed in grassland (2779.33 km2), cultivated land (535.34 km2), bare land (43.08 km2), urban land (0.65 km2), and wetland (152.66 km2) in the same period. In the study, it was also observed a decrease of an NDVI value by 0.1 was observed in 2013 in the southern part of the basin. The findings of the present study illustrate a significant change in eco-hydrological conditions in the ARB with an adverse impact on the environment. Hydroclimatic changes caused the increase in temperature and decreasing trend in precipitation which significantly impacted the land cover and vegetation in the basin. The changes in land cover were mostly caused by global and local climate influence which mainly affects the hydroclimate and eco-hydrology systems of the basin. The result is consistent with that of the previous studies conducted elsewhere. The findings of this paper could help researchers to understand the eco-hydrological condition of the study basin and become a foundation for further studies. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A new method allows distortion free vegetation cover imagery to be collected. • 4 m2 area plots are rapidly imaged using only a handheld camera. • Utilising object based image analysis a classification accuracy of 81% is achieved. • Inclusion of a vegetation height image band improves overall classification accuracy. Abstract Percent vegetation cover is important variable used in understanding ecosystem processes, vegetation health and productivity. Downward looking images captured using a handheld camera have been demonstrated as a viable option for rapidly capturing in situ information to assess vegetation cover. This technique, however, is prone to perspective distortions biasing cover estimates towards taller vegetation elements. In this paper we present a new approach to generate imagery for use in vegetation cover estimation utilising multiple overlapping photographs and structure from motion algorithms to produce a 3D point cloud representation of the target plot. This point cloud is then converted into an orthoimage consisting of four bands—red, green, blue and vegetation height—which is free from perspective distortions. The approach is trialled in two Eucalypt forests in South Eastern Australia to produce an estimate of change in cover of all vegetation elements following a prescribed burn. Orthoimages are generated with 2.5 mm resolution and classified using object-based image analysis and random forests into broad vegetation and fuel classes. Utilising this approach an overall classification accuracy of 81% is achieved with the resulting estimates of cover agreeing with visual point based interpretation to within 6% across all classes. [ABSTRACT FROM AUTHOR]

Abstract In the arid Hexi Corridor of northwest China, vegetation cover plays a pivotal role in sustaining the unique terrestrial ecosystem. In this paper, vegetation changes during growth season from April to October were investigated through examining the trends in the Normalized Difference Vegetation Index (NDVI) across the Hexi region. Based on the GMMIS NDVI 3g.v1 dataset, NDVI trend and its dependency on elevation and land cover were analyzed for the period 1982–2015 according to multiple statistical tests. Results showed that NDVI exhibited a significantly increasing trend in ∼70% of the vegetated area, in contrast with a negative trend only in 2.85%. The resulting distinct groups with respect to decreasing, increasing and no trends presented significant differences in elevation and land cover composition, and the correlation between elevation, land cover and NDVI trend magnitude was subjected to precipitation and temperature change. The elevation and grassland cover were found to mainly account for variations in NDVI trend, and increase in elevation and various types of land cover excluding impervious and bare land would facilitate the trend magnitude. The dependency of NDVI trend on elevation and land cover was very vulnerable to increasing air temperature, which triggered an improvement in the vegetable activity to adapt to climate change, especially grass and forest. The contribution of crop and shrub to NDVI change was sensitive to precipitation trend change, but the crop was primarily influenced by human activities. The identified patterns of vegetation change would help to gain insights into the adapting mechanism of the fragile ecosystems in arid areas to changing environmental conditions. Graphical abstract Image 1 Highlights • A significantly increasing trend in NDVI was detected at 70% of the vegetated area. • The NDVI trend was strongly related to characteristics of elevation and land cover. • Significant variations in elevation and grass land imply the NDVI trend dependency. • The NDVI trend dependent on elevation and land cover is subject to climate change. • Effect of precipitation or temperature change was distinguished on the land cover. [ABSTRACT FROM AUTHOR]

Major earthquake occurred in mountainous areas usually cause large number of landslides that lead to severe impact to local vegetation cover and growth. The negative influence to vegetation may last for many year and vegetation recovery may experience dynamic fluctuation. Existing methods for vegetation recovery diagnosis face difficulty in capturing the dynamic behaviours both within and between years that makes the interannual comparison impossible. This paper proposes a new method to diagnose regional vegetation recovery after a major earthquake by defining a difference measurement index (DMI) using MODIS NDVI time series at 8-day interval. This differs from many existing methods in its quantification of the difference between the studied time series and historical samples, by using a proposed algorithm consisting of lower bound distance and dynamic time warping. This algorithm can better differentiate vegetation disturbance from its natural fluctuation. Second, the method investigates relatively regional vegetation recovery via a dynamic index, the DMI. Vegetation conditions in different years can be compared with a historical benchmark and measured by DMI. This makes it possible to diagnose dynamic vegetation recovery and generate a series of interannual spatial distributions of regional vegetation state. [ABSTRACT FROM AUTHOR]

This paper presents the non-stationarity and autocorrelation (with a Moran’s I index score of 0.75) of the MODIS-retrieved aerosol optical depth (AOD) of the Wuhan agglomeration (WHA) in Central China, using geographically weighted regression (GWR) to identify the spatial relationships between AOD and its impact factors. In addition to the socio-economic factors, i.e., GDP and population, vegetation cover, elevation, land-use density and landscape metrics are also considered. Faced with the rapid process of urbanization and the impact of land-use change on AOD, which has been confirmed in previous studies, we propose an AOD prediction method, combining a land-use change simulation model, a cellular automata and Markov chain (CA-Markov) model, and spatial relationships built by GWR to represent the spatial distribution of AOD in 2030. The results suggest that the GWR model is able to address the spatially varying relationships, with an R-squared value, corrected Akaike’s information criterion (AICc), and standard residual better than those of the ordinary least squares (OLS) model. Land-use simulation, with an accuracy of 89.76%, indicates that an increase in the built-up area and a decrease in the forest area will be the major trends of land-use change and will lead to increased AOD. The AOD simulation results indicate that the most developed areas, i.e., the cities of Wuhan and Huangshi, will be the AOD increase hot spots in the WHA. This study provides an alternative method to identify the varying spatial relationships between AOD and its impact factors. A spatial prediction method for AOD is developed from the perspective of land-use change, which will help land-use planners in decision making. [ABSTRACT FROM AUTHOR]

In this proof of concept paper, we address the potential role of forests and vegetation cover as an adaptation tool. In contrast to work addressing the carbon benefits of afforestation, we highlight the potential usefulness of forests as major contributors to the water cycle. Integrating water and carbon cycle benefits into a common objective enhances discussions about the role and value of forests. Herein, we describe a method for increasing regional precipitation in semi-arid environments, thereby potentially helping to increase scarce water resources. The approach capitalizes on observed interactions between forest cover and the hydrologic cycle, with the express aim of returning much needed water resources to an increasingly vulnerable region. The natural processes behind “precipitation recycling” (PR), and vegetation based cross continental transport of atmospheric moisture form the core of this solution. Induced precipitation recycling (IPR) initiates these processes by irrigating afforested land using locally available surplus water. This paper discusses the underlying processes and a proposed demonstration project that functions as both a “proof of concept” and a research testing ground, providing potential validation for promoting future expansion to the wider region. The proposed IPR project utilizes treated wastewater and surplus storm run-off, thus averting additional burdens on the existing water supply, while performing additionally valuable environmental and ecosystem services. IPR provides an alternative approach to supplement existing and typically far costlier plans to address regional water shortages and handle wastewater treatment. The proposed integrated solution would not only contribute positively to regional water supply, but would also provide additional eco-system services and end-products that add value and utility, thereby improving the project’s potential economic viability. This project should be of particular interest to land use and water management planners in the Los Angeles Basin area for whom it is conceived, as well as to those along much of the coastal region in California where climate change-driven drought cycles have been increasing in length and intensity. [ABSTRACT FROM AUTHOR]

This paper introduces and presents the Spatial Processes in HYdrology (SPHY) model (v2.0), its development background, its underlying concepts, and some example applications. SPHY has been developed with the explicit aim of simulating terrestrial hydrology on flexible scales, under various physiographical and hydroclimatic conditions, by integrating key components from existing and well-tested models. SPHY is a spatially distributed leaky bucket type of model, and is applied on a cell-by-cell basis. The model is written in the Python programming language using the PCRaster dynamic modeling framework. SPHY (i) integrates most hydrologic processes, (ii) has the flexibility to be applied in a wide range of hydrologic applications, and (iii) on various scales, and (iv) can easily be implemented. The most relevant hydrological processes that are integrated into the SPHY model are rainfall-runoff processes, cryosphere processes, evapotranspiration processes, the dynamic evolution of vegetation cover, lake/reservoir outflow, and the simulation of root-zone moisture contents. Studies in which the SPHY model was successfully applied and tested are described in this paper, including (i) real-time soil moisture predictions to support irrigation management in lowland areas, (ii) climate change impact studies in snow- and glacier-fed river basins, and (iii) operational flow forecasting in mountainous catchments. [ABSTRACT FROM AUTHOR]

The objective of this study was to map and discriminate variations of vegetation net primary production (NPP) from the effects of precipitation variability and thereby identify the effects of grazing on NPP variations in the Xilingol Grassland. Satellite-based simulations of NPP collected over 14-year period and residuals method were used to normalize the effects of interannual precipitation on NPP. A fixed-size moving-window method was developed in this paper to quantify the annual grazing pressure (defined as grazing pressure index, GPI) on the regional scale, using vegetation cover images from 2001 to 2014. Temporal and spatial analysis methods were conducted to show patterns and trends of GPI and NPP residuals (NPP res ), and further to examine the effects of grazing pressure changes on variations of NPP res . Our results showed 80.7% of the study area experienced increasing NPP res and 18.5% with significantly increasing trends ( p  < 0.05). 76.7% of the study area showed decreasing grazing pressure, and 11.8% of areas experienced significantly decreasing trends ( p  < 0.05), with a small patches scattered distribution pattern. The negative correlations between NPP res and GPI were found in 94.6% of the study area, and 41.9% with significant correlation ( p  < 0.05); and long-term decreasing grazing pressure had positive impacts on vegetation NPP. They indicate grazing is a main driving factor of variations in vegetation NPP other than precipitation in Xilingol Grassland. The spatial patterns and temporal trends of NPP res , grazing pressure and responses of NPP res to grazing pressure changes varied among three main grassland types. [ABSTRACT FROM AUTHOR]

Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75. [ABSTRACT FROM AUTHOR]

Landscape responses to degradation caused by aridification bring the landscape system into a new equilibrium state. The system transformation may entail irreversible changes to its constituting parameters. This paper analyses the impact of aridification on landscape degradation processes in the sand-covered landscapes of the Hungarian Danube-Tisza Interfluve region at the regional, landscape, and local site scales. Changes in groundwater level (well data), lake surface area (Modified Normalized Difference Water Index) and vegetation cover (Enhanced Vegetation Index) were analysed over time periods of 12-60 years. Significant regional variation in decreasing groundwater levels is observed and limits the regional applicability of this indicator. Applying the lake surface area parameter from remote sensing data demonstrated greater utility, identifying several local lakes in the landscapes which have dried out. Analysis of the vegetation response indicated minor changes over the 2000-2014 time period and did not indicate a landscape system change. Landscape degradation as a result of changes in groundwater, vegetation, land cover and land use is clearly identified exclusively in local lake areas, but at the landscape scale, changes in the water balance are found in phases of system stability and transformation. Thresholds are identified to support policy and management towards landscape degradation neutrality. [ABSTRACT FROM AUTHOR]

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = -0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to -0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the key variables in the water and energy exchange between land surface and atmosphere. This paper develops an end-member-based two-source approach for estimating land surface ET (i.e., the ESVEP model) from remote sensing data, considering the differing responses of soil water content at the upper surface layer to soil evaporation and at the deeper root zone layer to vegetation transpiration. The ESVEP model first diverges the soil-vegetation system net radiation into soil and vegetation components by considering the transmission of direct and diffuse shortwave radiation separately from the transmission of longwave radiation through the canopy, then calculates the four dry/wet soil/vegetation end-members with the diverged soil and vegetation net radiations, and last separates soil evaporation from vegetation transpiration based on the two-phase ET dynamics and the four end-member temperatures. The model can overall produce reasonably good surface energy fluxes and is no more sensitive to meteorology, vegetation, and remote sensing inputs than other two-source energy balance models and surface temperature versus vegetation index ( TR –VI) trapezoid models. A reasonable agreement could be found with a small bias of ±8 W/ \textm^2 and a root-mean-square error within 60 W/ \textm^2 (comparable to accuracies published in other studies) when both model-estimated sensible heat flux and latent heat flux from MODIS remote sensing data are validated with ground-based large aperture scintillometer measurements. [ABSTRACT FROM PUBLISHER]