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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]

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]

Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a significant decrease in biodiversity. With recent advancements in digital image processing and satellite imagery techniques, this study presents an approach for tracking forest cover alterations along a recently constructed highway in Brunei. Specifically, this paper harnesses the power of cutting-edge deep learning (DL) models; U-Net and Deeplabv3+, to classify forest and non-forest features from Sentinel-2 satellite images captured along the Telisai–Lumut highway between 2018 and 2022. A computational performance analysis and network comparison revealed that the Deeplabv3+ had a remarkable semantic segmentation performance, with a mean intersection over union of 0.9247, followed by an ensemble of four networks, with a mean intersection over union of 0.9171. The best model consistently outperformed other models in segmenting forest and non-forest features, with an intersection over union (IOU) of 0.9463 and 0.9031, respectively. Moreover, deploying hyperparameter optimization allowed us to assess the model's sensitivity to different hyperparameters for better results. Furthermore, forest change statistics were computed using the best model's generated forest change maps, and the results indicated a 16.7% gain in the overall forest vegetation cover along the highway, demonstrating the validity of the results. These reproducible findings will provide forest managers and ecologists with near real-time information on the status, health, and extent of forest ecosystems. This study's results have critical implications for using DL to monitor forest cover changes and will be of great interest to the scientific community as they open new avenues for future research and underline the need for continued innovation in this important field. • Reproducible forest change maps using DL & Sentinel-2 images along Brunei highways. • Generalizable baseline model for mapping forest cover change along highways. • Boosted U-Net's accuracy for forest/non-forest segmentation by the ensemble method. • Superior performance of Deeplabv3+ demonstrated and pitted against U-Nets • Model's sensitivity to hyperparameters examined for enhanced performance. [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 Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management. Graphical abstract Unlabelled Image Highlights • The convolutional neural network is able to detect woody vegetation in photographs. • Image quality is an important factor for discriminating between vegetation types. • The automatic approach is capable of identifying trends in woody vegetation regrowth. • Manual classification provides most accurate results in change detection. • Repeat photography is a powerful tool for communicating landscape changes. [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]

Abstract: This paper presents a data-flexible indicator framework for analysis of visual landscape character; the VisuLands framework. The theory-based framework encompasses currently used indicators for visual assessment based on four different data sources: land cover data, aerial photographs, landscape photographs and field observations. This paper presents a study applying the VisuLands framework in analysis of landscape change and its effect on visual scale in a landscape in Southeast Sweden. The paper provides a critical assessment of the pros and cons of the approach. It identifies the advantages and disadvantages of using different data sources as well as the applicability and sensitivity of existing indicators in detecting visible landscape change. The results show that while some of the VisuLands indicators are relatively easily applied, others are more complex and demanding in terms of interpretation. The flexibility of the VisuLands framework makes it applicable and user-friendly as it helps meet the requirements and restrictions of the users. The assessment has shown that the different data sources complement each other and that applying indicators using various data sources, when available, will enhance the comprehensiveness of visual landscape assessment. The experience of this study is that the VisuLands framework is a useful tool in landscape analysis, monitoring and planning, which provides a repeatable, systematic and transparent approach with strong links to landscape theory. [Copyright &y& Elsevier]

Abstract: Fraction of green vegetation, fg is needed as one of regular parameters for vegetation cover analysis. The paper explores the potentials of deriving this variable from thermal mapper (TM) normalized difference vegetation index (NDVI) data considering the leaf area index (LAI) of agricultural field. Geometric, radiometric and atmospheric correction of the images were performed before further analysis. According to the sub-pixel structure characteristic, we choose mosaic-pixel model for calculating percent vegetation cover. A new method was put forward to achieve LAI values of non-dense vegetation where soil line equation was considered. Two schemes are produced to obtain different LAI values and type-specific accuracy is evaluated using parameter defined in this paper. [Copyright &y& Elsevier]

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]

In this paper, we retrace the geomorphological evolution of a 6.5 km-long segment of the Blanco Este River, which drains the Calbuco volcano in southern Chile, highlighting the geomorphic signatures of the disturbances caused by the 1961 and 2015 eruptions. In particular, we track the geomorphological changes and the evolution of the geomorphological units and discuss the underlying processes. In addition, we use the NDVI (Normalize Difference Vegetation Index) to characterise the vegetation recovery at the basin and the active channel scales. The reconstruction of the geomorphological changes is performed by means of photointerpretation and supervised classification image analysis. Moreover, we quantify braiding indices and active channel widths to characterise the fluvial corridor and to describe its evolution after the two eruptive events. It is found that the evolutionary trajectories of the Blanco River immediately after the eruptive events of 1961 and 2015 exhibited strong similarities. In fact, the major geomorphological adjustment of the Blanco Este River occurred during the first period of observation (19 years) after the 1961 eruption. Subsequently the river progressed gradually towards an advanced relaxation stage in 2014. For 53 years (between 1961 and 2014), the reduction of the active channel width was the dominant process, followed by erosion, which favoured the regeneration and consequent expansion of vegetation cover within the river corridor. The 2015 eruption reset the fluvial landform and a new cycle of sedimentation, braiding and formation of central bars began; the highly dynamic pattern is still observed today. [ABSTRACT FROM AUTHOR]

As one of the most intensively eroded regions worldwide, soil erosion assessment is currently gaining further momentum in the Loess Plateau (LP), China. However, there exist very few review studies that assess soil erosion in relation to land use and land cover (LULC), climate, and ecosystem services (ESs) at multiple temporal and spatial scales in the LP. Thus, this review provides a comprehensive review of the interactions among soil erosion assessment, climate change, LULC change, and ESs at different scales. We focused on the academic literature published in the English language and analyzed the coverage of soil erosion across 109 publications. Our results highlighted three aspects: (1) studies dealing with the effects of LULC change on soil erosion are abundant. A recent surge in research interest is to explore a threshold of vegetation cover to support a sustainable eco-hydrological environment in the LP. However, the impacts of multiple stressors on soil erosion have received limited attention. (2) The short-term soil erosion assessment dominated the published studies. Cross-spatial-scale and long-term erosion assessments have received little attention. (3) 43% of reviewed papers used a field measurement method to assess soil erosion with the validation of measurement methods. 41% of reviewed papers used empirical models to assess soil erosion, but the modeling validation remains an unsolved problem. Then, three recommendations are listed as follows: (1) the combined impacts of LULC and climate change on soil erosion need to be further examined. (2) Long-term and cross-spatial-scale soil erosion assessments need to be established. (3) Soil erosion modeling accuracy on a large scale needs to be developed. • The assessment of multiple stressors on soil erosion has received limited attention. • Poor coverage of multi-spatial scale and long-term soil erosion assessment. • Large scale soil erosion modeling validation remains an unsolved problem. • A recent surge in academic interest is to explore thresholds of vegetation cover in the Loess Plateau. [ABSTRACT FROM AUTHOR]

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load. [ABSTRACT FROM AUTHOR]

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R 2 values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications. [ABSTRACT FROM AUTHOR]

In recent years, attention has been paid to the restoration and conservation of desert riparian vegetation along the middle stream of the Tarim River. Quantifying the effects of the water transport dike on the ecological habitat of this riparian vegetation is fundamental to providing additional theoretical support to both researchers and policy makers. In this study, we conduct an analysis on the changes in the fractional vegetation cover gradient on both sides of the water transport dike in the middle stream of the Tarim River. This study also investigates the spatial-temporal changes in the fractional vegetation cover along the middle stream of the Tarim River, and the spatial variation in the rate of change from 2000 to 2013. Furthermore, this paper analyses the characteristics of the fractional vegetation cover under the influence of two types of ecological brakes. The findings reveal (1) a trend of declining fractional vegetation cover on both sides of the middle stream from 2000 to 2013, with the rates along the northern the southern banks being −0.2278 yr −1 and −0.0294 yr −1 , respectively, (2) and overall, decreasing trend in the fractional vegetation cover with distance from the river channel along at two sections in the years 2000 and 2013. No obvious differences were observed inside the water transport dike. However, the effect of the water transport dike began from 1 km to 2.5 km, the range from 2.5 km to 13 km was mainly influenced by the dike on the northern bank of the Yingbazha-Osman, while the effect of the water transport dike on the southern bank of the Yingbazha-Osman, began from 1.5 km and gradually increased until 4.5 km. The area from 4.5 km–10 km was primarily affected by the water transport dike and reached a highly steady state at 10 km, which persisted until 30 km. The fractional vegetation cover in the years 2000 and 2013 were similar for Osman-Korla, especially on the southern bank, but fluctuations appeared on the northern bank. (3) fractional vegetation cover change rate showed different characteristics outside the water transport dike; different types of ecological brakes also imposed different effects on both sides of the river channel. [ABSTRACT FROM AUTHOR]

[Display omitted] • The vegetation cover status of 9 provinces in the Yellow River basin was analyzed. • The water poverty status of 9 provinces in the Yellow River basin is analyzed. • There is a U-shaped relationship between vegetation cover and water poverty. • There were differences in the effects of vegetation cover on WPI components. Vegetation plays an important role in improving the water resource environment. Faced with the serious water poverty problem in various regions of China, whether vegetation restoration can further alleviate water poverty needs to be studied urgently. Taking 9 provinces in the Yellow River basin in China as an example, this paper first uses the framework of the existing water poverty index (WPI) to calculate the WPI of each province over the years from 1999 to 2019 and evaluate the water poverty status of each province. Later, the normalized difference vegetation index (NDVI) was used to analyze the vegetation restoration status of each area. Finally, using provincial panel data for the study area from 1999 to 2019, a comprehensive feasible generalized least squares (FGLS) model was used to analyze the impact of vegetation cover on water poverty. It was found that there was no pure linear relationship between vegetation cover and water poverty; instead, there was a significant U-shaped relationship. Moreover, the influence of vegetation cover on each component of the WPI has certain differences. This research advances knowledge on the relationship between vegetation cover and water poverty and provides ideas for effectively alleviating regional water poverty. [ABSTRACT FROM AUTHOR]

• Standard indicators and measurements are key to cross-scale agroecosystem assessments. • Inconsistencies in indicator calculations produce discrepancies in meaning. • A framework for producing standard indicators from measurements is presented. S tandard indicators and measurements are key to cross-scale agroecosystem assessments. While the need to standardize indicators is broadly recognized, inconsistencies in indicator calculations from common measurements may yield discrepancies in indicator meaning and consequently reduce the accuracy of agroecosystem assessments. Here we present a framework that provides a mechanism for standardizing indicator vegetation and ground cover calculations across datasets and agroecosystems yet also provides flexibility for local or process-specific indicators. This framework, demonstrated in an R package terradactyl , 1) harmonizes standardized agroecosystem measurements into analysis-friendly datasets, 2) produces a standard, yet flexible indicator calculation approach, 3) aggregates indicators into commonly used sets of indicators or data models. This paper provides a workflow that can be applied across agroecosystem measurements and indicators globally to produce scale-able indicators that are relevant for land managers, conservation planners, and policymakers alike. [ABSTRACT FROM AUTHOR]

Climate change often leads to the vulnerability of vegetation cover, while the impact of human activities on vegetation cover is undoubtedly more complex in this context, especially in Ethiopia. This paper analyzed the spatiotemporal dynamics of vegetation growth in Ethiopia from 2003 to 2018 by the enhanced vegetation index (EVI) based on different time scales and explored the coefficient of variation and driving factors of the fractional vegetation coverage (FVC). The results indicated that the EVI mainly presents a "double peak" pattern, with large spatiotemporal differences between quarters and months in Ethiopia. The FVC increased by 0.0005 per year, but vegetation showed a browning trend after 2013. The FVC degraded area accounted for 43.9% of the total area, of which the significantly degraded area accounted for 7.51% due to human activities, mainly in northern, central, and southern Ethiopia. The effects of precipitation and maximum temperature on vegetation differed on time scales. Spatially, the vegetation on the northwest side of the Main Ethiopian Rift Valley (MERV) was dominated by a combination of maximum temperature and precipitation, while vegetation on the southeast side of MERV was mainly influenced by precipitation. However, the spatial overlay analysis with degraded and healthy vegetation zones revealed that human activities were the key driver of vegetation cover change rather than climate change. This study provides support for further development of vegetation health conservation policies in Ethiopia and monitoring of vegetation dynamics in other countries around the world. • The annual EVI values show a "double peak" pattern in Ethiopia. • The dominant climatic factors on the southeast and northwest sides of MERV differ. • Ethiopia's FVC showed an overall growth trend with an annual growth rate of 0.05%. • Human activities dominate the dynamic evolution of vegetation in Ethiopia. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

Illegal waste dumping has been widely regarded as one of the biggest source of environmental damage. Illegal landfills are a prevailing problem existing in a large number of countries. To control and better manage illegal landfills, it is necessary to know the current locations and contents of illegal landfills. This could increase efficiency in illegal landfill management and preserve the biodiversity and ecological balance. Remote sensing methods have been proven extremely effective in detecting potential illegal landfill sites. This paper investigates the relationship between the segmentation scale parameter and the detection accuracy of illegal landfill sites in urban areas that are not covered by vegetation or buried in the ground. The research showed that there is an optimal scale parameter (SP = 20) value for the used satellite image Pléiades 1B and area of interest (Novo Sarajevo municipality). The scale parameter's stated value gives maximum Kappa values and Overall accuracy coefficients for detected illegal landfills on the satellite image. • Illegal landfills are a big problem for environment and human health. • Remote sensing techniques are very effective for identifying illegal landfills. • There is an optimal value of the image segmentation scale parameter (SP). • For accurate results high-resolution satellite images are necessary. [ABSTRACT FROM AUTHOR]

Desertification causes not only a reduction of vegetation cover and surface moisture but also the expansion of sandy land. Considering that the modified soil adjusted vegetation index (MSAVI) is linearly related to canopy cover, albedo plays a significant role in the surface energy balance, and the sandy feature index (SFI) can identify sandy land. This paper proposed a desertification monitoring index (DMI) based on MSAVI, albedo and SFI to accurately monitor desertification. In addition, three classification methods (quantile grading, the susceptibility index segmentation method, and Jenks Natural Breaks) were used to divide the DMI into non-desertification, mild desertification, moderate desertification, and severe desertification. The relative exponential entropy model was adopted to select the optimal method for desertification classification. Spatiotemporal distributions of desertification in Duolun County were finally observed from 2005 to 2020. The results show that (1) although MSAVI, albedo and SFI have their own advantages in monitoring desertification, a single indicator is not suitable for various land uses. The DMI can combine the advantages of MSAVI, albedo and SFI in desertification monitoring and is more consistent with the real desertification levels. (2) Compared with quantile grading and susceptibility index segmentation methods, Jenks Natural Breaks has the smallest relative exponential entropy (2.124), with an overall classification accuracy of 88.21%, indicating that Jenks Natural Breaks is an optimal method for desertification monitoring. (3) Although approximately 1708.60 km2 of desertification areas have improved from 2005 to 2020, accounting for 44.23% of the whole area, the remaining areas are still under fluctuating or degraded conditions. This shows that the local government has made some progress in desertification protection, but the areas under fluctuating or degraded conditions still require protection or management. • Constructing the desertification monitoring index (DMI) by integrating multidimensional spatial information. • Using relative exponential entropy model to evaluate desertification classification methods. • Desertification protection categories were divided into four regions, and management strategies were suggested. [ABSTRACT FROM AUTHOR]

Logjams significantly influence watershed hydrology, flow regime, channel morphology and stability, and processes in lowland rivers. Consequently, logjams play a major role in the existence and conservation of the riparian and aquatic ecosystems along major waterways. In this paper, we attempt to detect and quantify logjams in river channels using LiDAR technology in conjunction with traditional fieldwork. To the best of our knowledge, LiDAR-based analysis has not been used to characterize logjams in streams. Overall, when applied in a lowland river environment, LiDAR-based analysis demonstrates a comprehensive solution for detecting logjams in relation to the fieldwork, with a low rate of omission. A filtered approach predicted the presence of 95% of fieldwork-reported logjams (a 5% rate of omission), but also identified six logjams not identified in the field (a 10% rate of commission). A nonfiltered approach identified 87% of field-reported logjams, producing a 13% rate of omission and a 6.7% rate of commission. Dimension measurements were more consistent in the filtered LiDAR approach, showing 53%, 34%, and 90% of R 2 improvements for the length, width, and height, respectively, over the unfiltered LiDAR values. As vegetation cover hindered accurate delineation of logjam boundaries by LiDAR, field and LiDAR measurements of nonvegetation-obstructed logjams were more highly correlated than the field and LiDAR measurements of partially and completely vegetation-obstructed logjams. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sentinel-2 and PlanetScope used for the first time for grassland FVC estimation. • Vegetation indices enhance the band spectral separability of bright soils. • Best indices based on NIR and red for Sentinel-2, NIR and red-edge for PlanetScope. • High resolution necessary to effectively describe grassland FVC spatial variability. Grassland fractional vegetation cover (FVC) accurate mapping on a large scale is crucial, since degraded grasslands contribute less to provisioning services, carbon storage, water purification, erosion control and biodiversity conservation. The spatial and temporal resolution of Sentinel-2 (S2) and PlanetScope (PS) data has never been explored for grassland FVC estimation so far and will enable researchers and agencies to quantify and map timelier and more precisely grassland processes. In this paper we compare FVC estimation models developed from Landsat-8 (L8), S2 and PS imagery. The reference grassland FVC dataset was obtained on the Paganella ski runs (46.15°N, 11.01°E, Italy) applying unsupervised classification to nadir grassland RGB photographs taken from 1.35 m above the soil. Fractional Response Models between reference FVC and 18 vegetation indices (VIs) extracted from satellite imagery were fitted and analysed. Then, leave-one-out cross validation and spatiotemporal change analysis were also performed. Our study confirms the robustness of the commonly used VIs based on the difference between NIR and the red wavelength region (R2 = 0.91 for EVI using S2 imagery) and indicate that VIs based on the red-edge spectral region are the best performing for PS imagery (R2 = 0.89 for RECI). Only medium to high spatial resolution imagery (S2 and PS) precisely mapped spatial patterns at the study site, since grasslands FVC varies at a fine scale. Previously available imagery at medium to low spatial and temporal resolution (e.g., L8) may still be interesting for analysis requiring long time-series of data. [ABSTRACT FROM AUTHOR]

Coastal foredunes are developed as a result of the interplay of multi-scale land–sea processes. Basic driving mechanisms of coastal foredune morphogenesis as well as natural processes and factors involved in shaping the foredune geometry are quantitatively studied in this paper by a numerical model. Aeolian sediment transport and vegetation growth on the subaerial part of a beach is simulated by a cellular automata (CA) approach, while the sediment budget in the subaqueous zone, serving as a sediment source/sink for the foredune ridges, is estimated in a process-based model. The coupled model is applied to a 1 km-long section of a barrier coast (Swina Gate) in the southern Baltic Sea for a 61-year (1951–2012) hindcast of its foredune development. General consistency is shown between the observational data and simulation results, indicating that the formation of an established coastal foredune results from a balance between wind-wave impacts and vegetation growth. Driven by an effective onshore wind and a boundary sediment supply, small-scale dunes develop on the backshore and migrate landward. They are then trapped in a narrow strip characterized by a large density gradient of vegetation cover which separates the hydrodynamically-active zone and the vegetated zone. Continuous accumulation of sediment in this strip induces the development of a foredune ridge. According to the simulations, the formation of an established coastal foredune has to meet three preconditions: 1. an effective onshore aeolian transport; 2. a net onshore or lateral sediment supply; and 3. a climate favoring vegetation growth. The formation of a new foredune ridge in front of an already existing foredune is determined by a combination of the sediment supply rate, the extreme wind-wave event frequency and the vegetation growth rate. Simulation results demonstrate a remarkable variability in foredune development even along a small (1 km) coast section, implying that the medium-to-long term land–sea interaction and foredune morphogenesis is quite sensitive to boundary conditions and various processes acting on multi-temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Calanchi (plural of calanco ) are typical badland landforms of the Italian landscape. They consist of dense networks of V-shaped valleys, with a sparse or absent vegetation cover, which frequently develop on unconsolidated or poorly consolidated clayey deposits. In this paper, the dimensional analysis and the incomplete self-similarity theory were used to deduce a model relating the volume of sediments eroded from the calanchi area to a set of geometric attributes of their tributary areas. The morphometric characteristics of 209 calanchi basins were used to calibrate and validate the model. The predictive skill of the model was assessed by calculating the mean square error and the Nash–Sutcliffe model efficiency coefficient. The model was found to provide a reliable prediction of eroded volume from basins entirely covered by calanchi landforms as well as from basins only partially affected by badland erosion processes. Furthermore, as the residuals between the calculated and the measured volume of the eroded material are normally distributed, the deterministic model may be considered complete, meaning that no other variables are necessary to explain the studied physical process. In other words, the model is able to predict erosion volume in calanchi landforms from a deterministic point of view. [ABSTRACT FROM AUTHOR]

This paper investigates the use of the vegetative state of natural covers as an indicator of soil moisture conditions at the end of the dry season in order to evaluate the cumulative effect of the hydrological regime. To achieve this, the three major vegetation covers in a mountainous semiarid environment in southern Spain were selected. Temporal and spatial trends of NDVI from Landsat-TM images were computed and related to the different hydrological patterns of variables in the study site, which were obtained with the hydrological WiMMed model. The heterogeneity in the hydrological behavior during the study period (914.5 mm of annual rainfall in the wettest year (2009–2010) and 284.4 mm in the driest year (2004–2005)) was reflected in the annual differences in NDVI values with steady mean NDVI values in coniferous vegetation (0.5–0.6) and more variable values in scrub cover. Both Correlation Analysis and Principal Component Analysis showed correlations among the different states of the vegetation cover, the variables involved in the soil water balance and those related to the snow dynamics of the antecedent year. Exponential fits were obtained between the mean annual soil water content and NDVI values with Pearson r 2 coefficients of over 0.7 in scrub cover. In certain years, the best fits were also found in scrub cover with r 2 values of up to 0.9. These results demonstrate the relationship between soil water content, the vigor of the natural vegetation and the hydrological characteristics of the antecedent year. The expressions obtained may serve to adjust the soil water content at the beginning of a hydrological year and to use the scrub cover as an indicator of the soil water balance in the area for a given year. [ABSTRACT FROM AUTHOR]

Abstract: Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil. [Copyright &y& Elsevier]

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• VFC is consistent with the abrupt change time of precipitation and humidity index. • VFC began to decrease in 1999, and the impact of human activities gradually increased. • MI and NL are the variables that cannot be eliminated in the regression equation. • The climate livestock index can reflect the environmental carrying capacity of local grassland. Climate change and human activities are critical to fractional vegetation cover (FVC). However, until now, their combined effects on FVC have not been well quantified. In this paper we analyze the temporal and spatial variations in the fractional vegetation cover in the Hulun Lake region from 1986 to 2017 and its response to changes in climate parameters and human activities; additionally, the impact of changes in climate parameters and human activities on FVC are discussed. The results showed that according to the Mann-Kendall (M−K) trend test analysis, 65.01% of the FVC in the Hulun Lake region decreased to varying degrees, of which 24.55% showed a significant decrease and only 8.61% increased significantly. Based on the M−K mutation test analysis of FVC and analysis of driving factors from 1986 to 2017, 1999 was the time point at which abrupt changes in FVC, annual precipitation and moisture index occurred in the Hulun Lake region, and water was an important factor affecting FVC. The regression analysis of FVC and the driving factors from 1986 to 2017 showed that the moisture index and the number of livestock could not be eliminated from the regression equation. Additionally, there was a significant correlation between the FVC and the climate livestock index (P < 0.001). Linear regression model constructed by FVC and climatic livestock index: y = 23.016ln(x)-31.454 (R2 = 0.5941), FVC increased with the increase of climatic livestock index. [ABSTRACT FROM AUTHOR]

Abstract: To preserve land quality and mitigating land degradation represent an important task for regional planning and environmental management of the Mediterranean region. Since land cover dynamics directly affect the landscape characteristics, remote sensing represents an effective tool for land quality assessment at large scale. In particular, the use of satellite-based vegetation indices, like the NDVI (Normalized Difference Vegetation Index), can provide important information when evaluating Vegetation Cover Quality (VCQ) patterns in terms of vegetation productivity and status, which represents one of the most sensitive landscape component to environmental degradation. This paper proposes an approach for the large-scale assessment of VCQ by means of an NDVI-based (functional) indicator using freely available MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery. As a case study, a complex semi-arid Mediterranean landscape (Attica, Greece) experiencing drought, land-use changes, increasing human pressure, and high vulnerability to degradation was chosen. As VCQ indicator, the NDVI-based vegetation cover classification was produced by means of unsupervised multivariate statistical techniques and compared with ancillary cartographic layers, statistical indicators, and field data related to land-use management observed in the study area. Results demonstrate that the obtained remotely sensed land characterization can be effectively considered as a proxy of the VCQ status of the examined region, especially for studies of actual land degradation. Due to the large availability over time and low cost of satellite images, the proposed approach can be applied to wider regions, e.g. covering the whole Mediterranean basin, to monitor diachronically vegetation quality and indirectly control land degradation. [Copyright &y& Elsevier]

Abstract: This paper presents an evaluation of changes in the performance of the surface of the Velká Podkrušnohorská dump, a brown coal waste dump, over a period of 25 years from 1984 to 2009, on the basis of satellite data collected by the Landsat satellite. The changes in vegetation cover, surface moisture and surface temperature were evaluated on the basis of the NDVI index (Normalized Difference Vegetation Index), the NDMI index (Normalized Difference Moisture Index) and the Landsat satellite thermal band. Due to the intense piling up of extracted material and the removal of vegetation cover, there was a significant increase in surface temperature and a decline in NDVI and NDMI after the study of the dump territory began. The maximum surface temperatures and the minimum values of both indices were established in 2000. The trend of the changes in these values has reversed since 2000, due to intensive reclamation works as well as natural succession. The results indicate a significant role of vegetation cover in the formation of the surface temperature and moisture parameters, and the transformation of solar energy at the surface. We consider that the removal of vegetation cover over vast areas can have an impact on the regional climate and hydrological regime. Moreover, we recommend that emphasis be placed on this effect when planning structures for mining purposes. [Copyright &y& Elsevier]

Abstract: This work is based on a recent UK£1.25M restoration project of a contaminated canal and adjacent brownfield land. The canal sediment contained extremely high concentrations of heavy metals and arsenic whilst adjacent brownfield land presented localised hotspots. Potential receptor risks had been identified in the context of trace element mobility to groundwater and entrainment of dust particulates; the engineering work was initiated due to concerns that the sediment was drying and was likely to release contaminants through leaching as anoxic conditions were removed. Extensive engineering of the canal site involved its narrowing through dredging, relocation of the sediment to the edges but within the canal basin, cement stabilisation, retainment by geotextile-lined gabions, then coverage with geotextile and capping with recycled soil-forming materials including green waste compost (GWC). The adjacent brownfield land received import of GWC as a soil improver to enhance biological activity, although subsequent studies indicated contaminants were already stabilised in the soil and biological activity on the brownfield site was not suppressed. The data suggest that GWC should be applied with caution due to its influence on trace element mobilisation. In this paper we question whether real ecological improvements have been achieved and have justified the high cost of the restoration. We conclude that this case study does not provide a realistic template for best restoration practice of brownfield land. A better alternative to the whole site may have been a less-engineered, more-ecological approach through enhancing natural processes of regeneration, contaminant stabilisation and attenuation. Management of the canal site to maintain a wet anoxic sediment, rather than abandonment and dereliction would provide a more sensitive and less costly solution. Vegetation cover alone may be sufficient to mitigate risk on urban brownfield land. [Copyright &y& Elsevier]

Abstract: Natural rivers are subjected to continuous adjustments in response to any change in the environment. These environmental changes may occur naturally, as in the case of climatic variation or changes in vegetative cover, or may be related to human activities including channelization, damming, bank protection, and bridge construction. Identifying the value of that discharge (‘dominant’ or ‘effective’ discharge, Q D ) considered responsible for the main changes operated by a river has been a subject of great challenge to scientists and engineers during the last decades. In fact, this threshold value is largely adopted for stream-management decisions, for predicting the stable slope upstream of grade-control structures, and for designing moderate- to large-sized hydraulic structures. In this paper, a simple concept of dominant discharge, corresponding to that value of Q D that accounts for at least 50% of the total suspended sediment load transported by the river, is introduced and discussed. The work is based on a long-term data set that includes measurements of monthly discharge and suspended sediment load in 27 stream gauge stations located in three different regions of southern Italy. The analysis showed that Q D corresponds to values of the return time, T, ranging from 1 to 5years, confirming previous findings by other authors in similar analyses. Also, as the values of Q D were well correlated with the 1.5-year peak discharges of the same investigated rivers, an empirical approach is suggested in order to estimate Q D in rivers with no sediment load measurements. [Copyright &y& Elsevier]

Abstract: For the fragile-ecosystem arid land, the macro and integral information about the land cover types and area is important for the restoration and the improvement of the ecological environment. In this paper, the arid Gaotai County of Gansu Province, China, was taken as a study case to discuss classified method of the arid land based on various indexes. Taking the advantages of different temporal images into account, two TM images acquired in October and June respectively were selected to conduct the research. Firstly, a series of indexes such as NDVI, vegetation coverage, MNDWI, Albedo, Wetness and SI were calculated. Secondly, combining Robert algorithms and artificial experience methods, the integrated index threshold for distinguishing vegetation from other land types was calculated. Thirdly, using the threshold and decision tree classifier, the middle-high and low coverage vegetation were extracted from both two images. Fourthly, the vegetation areas from two images were combined to form the total vegetation cover types (Oasis), which was used to mask October images, and the outside of mask area was continually classified into the types such as the salinization land, water, bare land and wetlands and other land cover types with decision tree classifier. At last, combining all cover types together, the land cover classification map of whole county was produced. Compared with the land cover types interpreted from the high resolution images and field investigation points, this method was found achieving the accuracy of 91.5%. [Copyright &y& Elsevier]

Abstract: In this paper, percentage of vegetation cover in different districts of Wuhan were calculated based on remote sensing images from 1988 to 2002 by employing NDVI method for dimidiate pixel model. Then the vegetation coverage maps in different periods were generated to analyze the temporal change of vegetation coverage of Wuhan. The results showed that, the average vegetation coverage of the whole area was decreased from 58.41% to 50.45%, especially in Jiangxia and urban district. From 1996 to 2002, the vegetation coverage decreased most sharply, while the vegetation coverage increased lightly from 1991 to 1996 in the whole study area. By analyzing spatial changing character in the whole study area we can see that, the whole area was in the decreasing moment, especially in Jiangxia district and the urban district, this was caused by the urban development, and may led to the malign development of the environment, worthy of all aspects of attention. [Copyright &y& Elsevier]

Abstract: Since 1997, the Department of Environmental Sciences of Ca'' Foscari University of Venice has undertaken numerous research projects aimed at deepening understanding of pedogenic processes in the Dolomites, and at highlighting the fundamental contribution that soil science can give to the conservation of natural resources and achieve sustainable management of mountain ecosystems. A total of several hundred profiles have been described, analyzed and mapped. This paper reports the results from the analysis of pedo-environmental characters of profiles developed from different parent materials, at altitudes between 1300m and 2900m and in different conditions of slope, exposure and vegetation cover. Soil forming factors, landforms and land surfaces have been interpreted to understand the soil-landscape in the mapped areas and to develop a qualitative model of soil geography into the Dolomites scenery. The application of land evaluation methods in some of the investigated territories that are subjected to intensive tourist fluxes revealed some criticisms. Collected results also highlighted the high environmental heterogeneity of soils of the Dolomites. [Copyright &y& Elsevier]

Abstract: Beijing’s mountainous areas have an important ecological significance because they are ecological conservation and water source protection areas of Beijing. The impact of precipitation on the vegetation coverage of Beijing’s mountainous areas is qualitatively analyzed using multi-temporal Landsat images obtained during the last 20 years. Firstly, the influence of external factors, such as phenology and relative radiometric correction, on the normalized difference vegetation index (NDVI) are removed by the normalization of remote sensing images for different periods. Then, the vegetation coverage is calculated using the method of the dimidiate pixel model. Finally, based on that, the relationship between precipitation in different seasons and change of vegetation coverage is discussed. The results indicate that the mean vegetation coverage has the same change trends with winter and summer precipitation, but an opposite change trend with spring precipitation. The paper provides a theoretical basis and data support for further research on driving models of natural vegetation restoration in Beijing’s mountainous areas. [Copyright &y& Elsevier]

Abstract: A thawing ditch existing in permafrost region along the Qinghai–Tibet Railway is studied with their geological features, failure characteristics, ground temperatures, and influence on permafrost roadbed in this paper. The failure shows surface subsidence and horizontal slip on the shoulder. Integrated analysis from the field-monitored data show that the thawing ditch totally subsided more than 11cm from January 2008 to (month) 2010 and the average was close to 6cm a−1. The active layer on both shoulders of the thawing ditch has been slipped inward, and the max horizontal displacement in 0.5m depth was 5–6cm a−1. The warm season is the main period of the failure expanding in scale, especially from August to October. The mean annual ground temperature in shallow layer was about 0.9°C higher in the thawing ditch bottom than in the natural ground (remove about 20m) during the past two years (2008–2009). Due to the vegetation covered on the thawing ditch was destroyed, and the heat absorbed exceeds obviously heat released. The estimated result using the data in 2008 and 2009 shows that the input of heat into the permafrost below the thawing ditch was about 114,000kJ m−2 and was more than the output of 88,000kJ m−2 in twoyears. The heat absorbing starts in mid April, ends at the beginning of November, and lasts over 230days. In addition, the lateral thermal erosion of thawing ditch would influence the surrounding permafrost and damage the adjacent engineering infrastructure. The approximate calculation shows that about 90,000kJ heat was transported from thawing ditch to adjacent permafrost in 2009. Therefore, the permafrost beneath the thawing ditch has been degrading, warming, and declining in stability. [Copyright &y& Elsevier]

Abstract: This paper presents results of a research trial for standardizing a rehabilitation protocol through developing vegetation cover to rehabilitate red mud ponds. The trial was conducted at one of the red mud ponds of Hindustan Aluminum Company Limited (HINDALCO), the largest aluminum producer of India, at Ranchi district of Jharkhand State in eastern India. The trial was set up as a factorial experiment with five tree species, four treatment combinations, two irrigation types and two pit sizes. Growth data of five tree species for six parameters (collar diameter, GBH, plant height, crown diameter, number of leaves and number of branches) were recorded over 19 months. Four-way independent ANOVA was conducted on the growth data where all main effects, interaction effects and marginal means were computed. A best fit plantation protocol was developed out of this analysis, with Acacia nilotica and Albizia lebbeck emerging as the best performing tree species. Among different treatments, 20% gypsum along with 10% vermicompost and acid producing bacteria emerged as most preferred treatment amenders. Between two irrigation types, drip irrigation provided better growth results in all the tree species. Effect of the pit size was not found to be statistically significant for this time scale. [Copyright &y& Elsevier]

Abstract: The Mountain Renosterveld vegetation of the Roggeveld is an escarpment type renosterveld showing strong karroid affinities. Fire plays an important role as a landscape scale disturbance that shapes plant communities in this vegetation type, however, post-fire succession has never before been documented for renosterveld vegetation. A study was therefore conducted in the northern Roggeveld to improve our understanding of the recovery of the vegetation following fire. The natural vegetation recovery was analysed using line transect data accumulated at five different sites over a ten year period. This paper reports on the post-fire vegetation trends with respect to changes in species composition, species richness, life form composition and life form richness. Vegetation cover began to re-establish within the first nine months following the fire, and remained at a high level from years 3 to 10. At the first survey the species richness varied from 13 to 17 species, with the highest species richness (14 to 31 species) generally encountered at each transect after three years. The highest Shannon index values were generally found within the first three years and the lowest Shannon index values were found in years 9 and 10. In all cases the Principal Co-ordinate Analysis ordinations of the species composition data indicated a clear separation in the species composition between the first two years (years 1 and 2) following the fire and the remaining years (year 3 to 10). This study also supports the ‘initial floristic composition’ model of Egler (1954) in that all or the majority of species encountered during the succession were already present at the beginning of the recovery phase and there was a rapid re-establishment of the initial plant community. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a fine-scale (30 meter resolution) regional land cover modeling system, based on the SLEUTH cellular automata model, that was developed for a 257000km2 area comprising the Chesapeake Bay drainage basin in the eastern United States. As part of this effort, we developed a new version of the SLEUTH model (SLEUTH-3r), which introduces new functionality and fit metrics that substantially increase the performance and applicability of the model. In addition, we developed methods that expand the capability of SLEUTH to incorporate economic, cultural and policy information, opening up new avenues for the integration of SLEUTH with other land-change models. SLEUTH-3r is also more computationally efficient (by a factor of 5) and uses less memory (reduced 65%) than the original software. With the new version of SLEUTH, we were able to achieve high accuracies at both the aggregate level of 15 sub-regional modeling units and at finer scales. We present forecasts to 2030 of urban development under a current trends scenario across the entire Chesapeake Bay drainage basin, and three alternative scenarios for a sub-region within the Chesapeake Bay watershed to illustrate the new ability of SLEUTH-3r to generate forecasts across a broad range of conditions. [Copyright &y& Elsevier]

Abstract: The surfaces of salt diapirs in the Zagros Mountains are mostly covered by surficial deposits, which significantly affect erosion rates, salt karst evolution, land use and the density of the vegetation cover. Eleven salt diapirs were selected for the study of surficial deposits in order to cover variability in the geology, morphology and climate in a majority of the diapirs in the Zagros Mountains and Persian Gulf Platform. The chemical and mineralogical compositions of 80 selected samples were studied mainly by X-ray powder diffraction and X-ray fluorescence. Changes in salinity along selected vertical profiles were studied together with the halite and gypsum distribution. The subaerial residuum formed from minerals and rock detritus released from the dissolved rock salt is by far the most abundant material on the diapirs. Fluvial sediments derived from this type of residuum are the second most common deposits found, while submarine residuum and marine sediments have only local importance. The mineralogical/chemical composition of surficial deposits varies amongst the three end members: evaporite minerals (gypsum/anhydrite and minor halite), carbonates (dolomite and calcite) and silicates-oxides (mainly quartz, phyllosilicates, and hematite). Based on infiltration tests on different types of surficial deposits, most of the rainwater will infiltrate, while overland flow predominates on rock salt exposures. Recharge concentration and thick accumulations of fine sediment support relatively rich vegetation cover in some places and even enable local agricultural activity. The source material, diapir relief, climatic conditions and vegetation cover were found to be the main factors affecting the development and erosion of surficial deposits. A difference was found in residuum type and landscape morphology between the relatively humid NW part of the studied area and the arid Persian Gulf coast: In the NW, the medium and thick residuum seems to be stable under current climatic conditions. Large sinkholes and blind valleys with sinking streams are common. On other diapirs, the original thick residuum is undergoing erosion and the new morphology is currently represented by salt exposures and badland-like landscapes or by fields of small sinkholes developed in the thin residuum. Models for evolution of the subaerial residuum and the diapir landscape/morphology are described in this paper. While the thick residuum with vegetation has very low erosion rates, the salt exposures and thin residuum are eroded rapidly. During wet periods (e.g. early Holocene), the diapirs rose and salt glaciers expanded as the influx of salt mass was much faster compared to erosion. After the onset of an arid climate, c. 6 ka BP, the rising of the some diapir surfaces decreased or even reversed due to acceleration of erosion thanks to vegetation degradation and changes in the residuum type and thickness. [Copyright &y& Elsevier]

This paper provides results of assessment of the tritium distribution in the vegetation cover in the areas of underground nuclear explosions at the Semipalatinsk Test Site (STS). The research was conducted at the former "Degelen" test site along the streams that are one of the main channels of tritium migration from underground nuclear explosions epicenters. The dominant plant species Carex supine and Achnatherum splendens that belong to different ecological groups in relation to humidity were selected as representatives of the vegetation cover. The TFWT (tissue free water tritium) and OBT (organically bound tritium) activity concentrations in the vegetation cover were measurement. TFWT activity concentration in the samples of both plant species had high values with an average of up to 30 kBq kg−1. The OBT activity concentration was 1–2 orders of magnitude lower than the TFWT in all plant samples. The TFWT and OBT activity concentrations in vegetation samples are closely correlated (r = 0.75, p < 0.05). No statistically significant difference was found between the content of tritium in the samples of Carex supine and those of Achnatherum splendens taken at the same locations. OBT/HTO ratios for vegetation samples of both species were close to equilibrium ratio used in environmental transfer models. In some cases, OBT/HTO ratios were significantly lower than one, which indicates that simple environmental transfer models may not accurately predict the behavior of HTO and OBT in different environmental compartments. The average OBT/HTO ratio for soil samples (0.6 ± 0.1) close to the equilibrium value indicates the equilibrium condition at the research area. The obtained [OBT] plant /[OBT] soil ratios indicate that soil organic matter accumulates tritium from year to year. However, in some locations with high tritium contamination ratios [OBT] plant /[OBT] soil were more than one due to OBT activity in soils is almost the same as OBT activity in plants. It was found that the nature of the spatial distribution of tritium in the vegetation cover in the areas of underground nuclear explosions is complex, and obviously depends on the location of the tunnels in which nuclear tests were conducted, as well as on the peculiarities of the hydrological regime of underground and surface waters, which are the main channels of tritium migration in the research area. Thus, the vegetation cover reflects the spatial distribution of tritium contamination in the sites of underground nuclear explosions and can be used as an indicator of the radiation situation when monitoring radiation-hazardous areas. • Study of 3H distribution in the plant cover in underground nuclear explosions area. • TFWT, OBT in the vegetation cover varies widely. • OBT/HTO ratios were close or lower than equilibrium value used in ecological transport models. • Location of tunnels affects 3H levels in plants. [ABSTRACT FROM AUTHOR]

A study to determine 137Cs, 90Sr, 241Am, 239+240Pu radionuclides in vegetation cover of the area of craters produced by underground nuclear explosions at the Semipalatinsk test site (STS) is summarised in this paper. Transfer factors (Tf) required for the quantitative description of the radionuclides transition from the soil to aboveground plant parts were found to be highest for sagebrush (Artemisia sublessingiana), Tf values for 90Sr were gradually decreasing with increasing distance from the dump zone. When arranging the radionuclide transferring factors in descending order, the following sequence was obtained: 90Sr Tf > 137Cs Tf > 241Am Tf > 239+240Pu Tf, which is consistent with International data. All Tf derived are much higher than those ones derived earlier for epicenters of aboveground nuclear tests and are closest to values for conventionally "background" areas at the Semipalatinsk test site. • Tf from at the area of PNEs are higher than for epicentres of aboveground nuclear tests. • Plant species determine differences in the radionuclides Tfs from 2 to 6 times. • Radionuclides on their uptake by plants: 90Sr > 137Cs > 241Am > 239+240Pu. [ABSTRACT FROM AUTHOR]

Coastal morphodynamics and environmental changes have been obviously altered by global warming, sea-level fluctuations, and human activities. Palynological analysis is one of the most important tools for providing information on vegetation, climate, and environmental change, which can be relatively sensitive indicators of human activity during the Holocene. However, Holocene coastal morphodynamics and palynological evidence from the Pearl River Delta and coastal areas has not been sufficiently studied. In this paper, we carry out analyses using detailed grain-size and palynological records of pollen, spores, freshwater algae, and marine dinoflagellate cysts from Borehole QZK13 in the Pearl River Estuary (PRE), southern China. The results show information relating coastal morphodynamics, vegetation evolution, climate changes, and anthropogenic activities during the Holocene based on radiocarbon chronology. In general, the PRE experienced marine transgressive processes; sea-level and the coastline tended to migrate landward between 10,700 and 4250 cal yr BP. Meanwhile, abundant arboreal pollen (mainly Castanopsis , Quercus -evergreen, Oleaceae, and Castanea) and small amounts of non-arboreal pollen (mainly Poaceae, Cyperaceae, and Artemisia) were observed in Borehole QZK13, which suggests that subtropical evergreen broadleaved forests were distributed throughout the PRE and adjacent areas, and relatively warm and moist climatic conditions were present during the early-middle Holocene. After that, there were relatively cool and wet climatic conditions, and marine regressive processes occurred in the PRE during the Late Holocene (ca. 4250–960 cal yr BP) indicated by an abrupt increase in aquatic herbs and Dicranopteris. Moreover, we hypothesize that the Neolithic "cultural fault" in the PRE is likely related to frequent storm surges induced by extreme typhoon events that occurred ca. 3000–2700 yr BP. • Grain size, pollen, spores, freshwater algae and dinoflagellate cysts from the Pearl River Estuary were investigated. • Three major phases shown by palynological results were corresponding to the changes in vegetation and climate. • We hypothesized that the Neolithic "cultural fault" is probably related to the storm surges induced by extreme typhoon events. • Natural vegetation cover has suffered large-scale destruction by ancient humans during the Late Holocene. [ABSTRACT FROM AUTHOR]

• Ecological land provides wind erosion prevention service (WEPS). • From 1990 to 2015, the area of ecological land decreased while non-ecological land increased. • The effects of five second-level types of ecological land changes on WEPS were discussed. • The transformation of desert to ecological landimproved WEPS. • The improvement of second-level ecological land types with low vegetation cover improved WEPS. As an important "buffer" to slow down the expansion of desertification, ecological land can provide wind erosion prevention service (WEPS), which is closely related to regional ecological security and human well-being. Previous studies have focused on the response of WEPS to land use change, to a certain extent, would easily cause uncertainties, where the same type can be influenced by different physical and anthropogenic factors and further alter the WEPS. To solve this problem, ecological land was defined from the main function of land in this paper as a new indicator, and the impact of ecological land change on WEPS was quantitatively measured. The results indicated the following: (1) During the period 1990–2015, the net area of ecological land reduced by 16,244 km2 or 17.74%. All second-level types of ecological land experienced a decreasing trend. (2) The annual average WEPS rate in the Tarim Basin was 34.82%, which increased by 1.42% across the 25 year period. (3) The increase of WEPS was mainly attributed to the expansion of oasis and the transformation of second-level ecological land types from low vegetation coverage to high vegetation coverage. The transformation of ecological land to desert was the main reason for the deterioration in WEPS. In the period 1990–2015, the protection and effective management of ecological land improved the ecological environment and increased the surface shelter in some regions, which was conducive to the improvement in WEPS. [ABSTRACT FROM AUTHOR]

The stability of a dike is influenced strongly by its water content, via both changing its weight and strength. While safety calculations using both analytical and numerical methods are well studied, the impact of surface boundaries exposed to natural conditions is rarely considered, nor is the fact that this surface is covered in vegetation and is susceptible to cracking. This paper presents a numerical study of stability of dikes covered with grass, subject to meteorological forcing and crack formation due to drying conditions. Building on a previous study and adding the impact of cracking, a crop model and a Finite Element Method (FEM) model are integrated together using an optimisation method to ensure mass balance and consistency. The crop model, used to simulate vegetation growth and infiltration/evaporation in response to meteorological forcing, is modified to consider preferential flow due to cracking. The FEM model, used to simulate the dike stability and hydro-mechanical behaviour, has the material properties modified to simulate the impact of cracks. Results simulating a ten-year period indicate a strong impact of cracking on the factor of safety. The vegetation was found to be responsive to both crack presence and an increase in the amount of cracks, which suggests that monitoring vegetation could be a useful tool to identify cracked (vulnerable to cracking) locations along dikes. [ABSTRACT FROM AUTHOR]

• The changes of NDVI in urbanized areas of most cities in China exhibited 'U' shapes. • More vegetation degradation was found in urbanized areas than in outer buffers. • Overall, urbanization has negative effects on vegetation for most cities of China. • The adverse effects of urbanization on vegetation gradually diminished over time. • The valid influence distance of urbanization on vegetation may be about 30–40 km. A better understanding the effects of urbanization on vegetation is vital to promoting the sustainable development of cities in China, which has also experienced the world's most remarkable urbanization during the past decades. This paper used satellite-derived night light, vegetation index, and socio-economic data to analyze spatio-temporal patterns of vegetation changes and evaluate the influence of urbanization on vegetation cover in 46 Chinese metropolises over the last four decades. To examine the effects of urbanization on vegetation and its valid distance, we proposed a new method of comparing three indicators, namely, annual average normalized difference vegetation index (NDVI), long-term trend of NDVI change and the proportion of pixels characterized by significant change in NDVI between built-up areas and its multiple buffer zones. The results showed that the effects of urbanization on vegetation have obvious spatial differences. Urbanization generally resulted in the decrease of vegetation coverage in built-up areas for most of China's central and eastern metropolises, while in Western China, usually restricted by natural conditions, the urbanization usually improved vegetation coverage. The rates of increase in NDVI were significant lower than that of the buffer zones for most cities in central and eastern China. Urbanized areas of most cities located in central and eastern China had a higher percent of vegetation pixels, representing degradation than that in the buffer zones. The opposite was true for pixels representing vegetation restoration. The changes of NDVI in the cities in Western China were basically contrary to those in central and eastern China. The impacts of urbanization on vegetation are stage-specific. With the continuous progress of urbanization level, the adverse effect of urbanization on vegetation were gradually diminished or even disappeared. Cities with higher nighttime light levels appeared to have less vegetation coverage in the early years, and negative correlations between mean NDVI and the mean digital number value have become weak in more recent years. The changes in NDVI in urbanized areas of most cities significantly followed a 'U' shape. This phenomenon may further suggest that positive effects of urbanization become more obvious over time during advanced urbanization. The spatial scope of the impacts of urbanization on vegetation was beyond the urban built-up areas, and the effect intensity appeared to attenuate as the buffer distance became further away from the urbanized area. The valid distance of impacts of urbanization on vegetation is generally about 30–40 km. [ABSTRACT FROM AUTHOR]

This paper presents GIS-based mapping of snow avalanche release areas using probabilistic occurrence ratio (POR) model. The proposed method utilized the topographical and avalanche occurrence dataset. The avalanche occurrence topographical factors such as slope, aspect, curvature and elevation generated from ASTER-GDEM V2 whereas vegetation cover from Landsat 8 OLI were considered in the proposed model. An avalanche inventory map of avalanche occurrence locations was prepared for training and validation of the model. From avalanche inventory map, 80% of avalanche occurrence data for training and remaining 20% was used for validation of the model. Finally, avalanche release area map was generated using the POR model and classified into five categories based on the Jenks natural break method. The R A -index and ROC-AUC methods were used for validation of the avalanche release area map. The validation accuracy of the model is confirmed as 92.41% and 89.13% as per success and prediction rates. • The first initiative to use GIS-based probabilistic occurrence ratio (POR) model for release area mapping of snow avalanches. • 233 avalanche locations for training and 59 avalanche locations were used for validation of the POR model. • Five terrain based avalanche occurrence factors were taken into the model. • The RA-index and ROC-AUC methods were used to assess the avalanche susceptibility map. • Validation of result using the ROC-AUC method was observed as 92.41% accuracy. [ABSTRACT FROM AUTHOR]