Low Vegetation Density Research Articles

State-space modeling reveals habitat perception of a small terrestrial mammal in a fragmented landscape.

Habitat loss is a major cause of species loss and is expected to increase. Loss of habitat is often associated with fragmentation of remaining habitat. Whether species can persist in fragmented landscapes may depend on their movement behavior, which determines their capability to respond flexibility to changes in habitat structure and spatial distribution of patches.Movement is frequently generalized to describe a total area used, or segmented to highlight resource use, often overlooking finer‐scale individual behaviors. We applied hidden Markov models (HMM) to movement data from 26 eastern bettongs (Bettongia gaimardi) in fragmented landscapes. HMMs are able to identify distinct behavior states associated with different movement patterns and discover how these behaviors are associated with habitat features.Three distinct behavior states were identified and interpreted as denning, foraging, and fast‐traveling. The probability of occurrence of each state, and of transitions between them, was predicted by variation in tree‐canopy cover and understorey vegetation density. Denning was associated with woodland with low canopy cover but high vegetation density, foraging with high canopy cover but low vegetation density, and fast‐traveling with low canopy cover and low vegetation density.Bettongs did move outside woodland patches, often fast‐traveling through pasture and using smaller stands of trees as stepping stones between neighboring patches. Males were more likely to fast‐travel and venture outside woodlands patches, while females concentrated their movement within woodland patches. Synthesis and applications: Our work demonstrates the value of using animal movement to understand how animals respond to variation in habitat structure, including fragmentation. Analysis using HMMs was able to characterize distinct habitat types needed for foraging and denning, and identify landscape features that facilitate movement between patches. Future work should extend the use of individual movement analyses to guide management of fragmented habitat in ways that support persistence of species potentially threatened by habitat loss.

Mitigation & Identification for Local Aridity, Based of Vegetation Indices Combined with Spatial Statistics & Clustering K Means

This research aims to develop new methods of mapping aridity risk zones and their potential impacts on land fires using a combination of indices to identify land fires such as the CSI and NBR and the indices for NDVI and SAVI. The research location is in Gunung Merapi National Park (TNGM) and Gunung Merbabu National Park (TNGMb), in Central Java Province and Yogyakarta. The data used in this research is Landsat 8 OLI image year 2010-2018, DEM data from ASTER image in TNGM and TNGMb area using landsat 8 OLI image specification. The research was conducted in 3 stages such as pre-processing, image data extraction and post-processing. Global and Local Moran’s Analysis on NDVI, SAVI, CSI and NBR vegetation indices data can be used as an indicator of aridity and potential land fires. The experiments show that the average is in class 4 including the moderate greenish classification. Moderate greenish is interpreted that the study area is overgrown with meadows, shrubs, barren, sandy, rocky areas and a low population of vegetation canopy that shows that the area is on the surrounding mountain peaks. The results of the analysis shows Positive Spatial Autocorrelation, the phenomenon of aridity has spatial connectivity between observed regions. Analysis of K Means on the high vegetation density conditions shows that the weight of the distance between the vegetation data to the centroid is shorter, therefore the data is concentrated on a region. In low vegetation density conditions, the weight of the distance between the data to the centroid is increasingly wide, therefore the data looks more widely distributed.

Study on the flow structure around discontinued vertically layered vegetation in an open channel

The flow structure and geomorphology of rivers are significantly affected by vegetation patterns. In the present study, the effect of vegetation in the form of discontinuous and vertically double layered patches particularly on the resulting flow turbulence was examined computationally in an open channel. A k-ɛ model was implemented in this research work which was developed using 3-D numerical code FLUENT (ANSYS). After the validation process of numerical model, the impact of discontinuous layered vegetation patches on the flow turbulence was investigated against varying vegetation density and patch length. The mean stream-wise velocities at specified positions showed larger spatial fluctuations directly upstream and downstream of vegetation elements, whereas sharp inflections in the profiles were witnessed at the top of smaller submerged elements i.e. z/hs= 1 (where z is the flow depth and hs is the smaller vegetation height). The reduction in flow velocity due to tall vegetation structure was more as compared with that of short vegetation. The mean velocity in the patch regions was visibly higher than that in the gap regions. The profiles of turbulent flow properties showed more rise and fall within the patches with a high vegetation density i.e. Ss/d= 4; and St/d= 8 (where Ss/d and St/d are the smaller and taller vegetation spacing, while d is the vegetation diameter) as compared with low vegetation density i.e. Ss/d= 8; and St/d= 16. The turbulent flow structure in the large patch and gap regions was found to be more stable than that in the small patches and gaps; whereas, due to the variation in distribution form of the patch, turbulence is relatively unaffected, and the flow structure variation is low. Turbulence was observed to be large, followed by a saw-tooth distribution within the patches; whereas, low turbulence is observed in the non-vegetation regions. The turbulent intensity acquired maximum of 13% turbulence for dense vegetation arrangement as compared to that of sparse arrangement having maximum of 9% turbulent intensity. A noteworthy rise in turbulent kinetic energy and turbulent intensity was witnessed as the flow passed through the vegetated regions. Hence, a non-uniform flow was observed through discontinuous and double layered vegetation patches.

Potential of High-Resolution Pléiades Imagery to Monitor Salt Marsh Evolution After Spartina Invasion

An early assessment of biological invasions is important for initiating conservation strategies. Instrumental progress in high spatial resolution (HSR) multispectral satellite sensors greatly facilitates ecosystems’ monitoring capability at an increasingly smaller scale. However, species detection is still challenging in environments characterized by a high variability of vegetation mixing along with other elements, such as water, sediment, and biofilm. In this study, we explore the potential of Pléiades HSR multispectral images to detect and monitor changes in the salt marshes of the Bay of Arcachon (SW France), after the invasion of Spartina anglica. Due to the small size of Spartina patches, the spatial and temporal monitoring of Spartina species focuses on the analysis of five multispectral images at a spatial resolution of 2 m, acquired at the study site between 2013 and 2017. To distinguish between the different types of vegetation, various techniques for land use classification were evaluated. A description and interpretation of the results are based on a set of ground truth data, including field reflectance, a drone flight, historical aerial photographs, GNSS and photographic surveys. A preliminary qualitative analysis of NDVI maps showed that a multi-temporal approach, taking into account a delayed development of species, could be successfully used to discriminate Spartina species (sp.). Then, supervised and unsupervised classifications, used for the identification of Spartina sp., were evaluated. The performance of the species identification was highly dependent on the degree of environmental noise present in the image, which is season-dependent. The accurate identification of the native Spartina was higher than 75%, a result strongly affected by intra-patch variability and, specifically, by the presence of areas with a low vegetation density. Further, for the invasive Spartina anglica, when using a supervised classifier, rather than an unsupervised one, the accuracy of the classification increases from 10% to 90%. However, both algorithms highly overestimate the areas assigned to this species. Finally, the results highlight that the identification of the invasive species is highly dependent both on the seasonal presence of itinerant biological features and the size of vegetation patches. Further, we believe that the results could be strongly improved by a coupled approach, which combines spectral and spatial information, i.e., pattern-recognition techniques.

Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

Abstract Riparian and deltaic areas exhibit a high biodiversity and offer a number of ecosystem services but are often degraded by human activities. Dams, for example, alter the hydrologic and sediment regimes of rivers and can negatively affect riparian areas and deltas. In order to sustainably manage these ecosystems, it is, therefore, essential to assess and monitor the impacts of dams. To this end, site-assessments and in-situ measurements have commonly been used in the past, but these can be laborious, resource demanding and time consuming. Here, we investigated the impact of three dams on the riparian forest of the Nestos River Delta in Greece by employing multi-temporal satellite data. We assessed the evolution in the values of eight vegetation indices over 27 years, derived from 14 dates of Landsat data. We also employed a modelling approach, using a machine learning Random Forests model, to investigate potential linkages between the observed changes in the indices and a host of climatic and terrestrial predictor variables. Our results show that low density vegetation (0–25%) is more affected by the construction of the dams due to its proximity to anthropogenic influences and the effects of hydrologic regime alteration. In contrast, higher density vegetation cover (50–75%) appears to be largely unaffected, or even improving, due to its proximity to the river, while vegetation with intermediate coverage (25–49%) exhibits no clear trend in the Landsat-derived indices. The Random Forests model found that the most important parameters for the riparian vegetation (based on the Mean Decrease Gini and the Mean Decrease Accuracy) were the distance to the dams, the sea and the river. Our results suggest that management plans of riparian and deltaic areas need to incorporate and take into consideration new innovative management practices and monitoring studies that employ multi-temporal satellite data archives.

Comparisons suggest more efforts are required to parameterize wind flow around shrub vegetation elements for predicting aeolian flux

Upon interacting with the atmosphere, vegetation could alter the wind distribution and consequently the erodibility of nearby region. The parameterization of wind distribution around vegetation is crucial for the prediction of surface aeolian flux. This paper compared the performances of existing empirical distribution models in the estimation of aeolian flux for shrub vegetation, focusing on distribution pattern and vegetation porosity (main parameter of distribution function). Predicted dust fluxes directly entrained by air flow show weak sensitivity to both distribution pattern and porosity in the case of low vegetation density, which suggests some aspects in dust forecast models might be simplified. However, both distribution pattern and porosity show significant effect on sand saltation transport rate in the lee of vegetation element and, consequently, on the formation and evolution of surface aeolian landforms. The contribution of dust fluxes released in wind increase zone to the total emission by using current parameterizations increases with both the decrease of wind speed and the increase of vegetation density. Nevertheless, the parameterization of wind increase zone needs to be validated and improved by further experimental and numerical investigations.

Biotic forcing militates against river meandering in the modern Bonneville Basin of Utah

AbstractBiotic forcing on river meandering is a highly debated topic in sedimentology. Vegetation is assumed to hold a vital role on channel stability and sinuosity, for example through bank stabilization and pedogenic production of cohesive clays. However, statistically solid and causal relationships between vegetation density and river sinuosity remain largely untested in natural systems. This study investigates physical and biotic forcings on channel sinuosity in the Bonneville Basin of Utah (USA), an endorheic depression flanked by active fluvial networks (‘washes’) that display diverse vegetation density and channel‐planform style. By means of remote sensing and ground‐data collection, 58 washes are considered, 0·1 to 90 km2 in surface area and drained by trunk channels <45 m wide and <1·2 m deep. Each wash is composed of a catchment basin connected downstream to an aggradational and distributive channel network. Statistically solid regressions highlight the primary roles played by base level and catchment size on fluvial morphogenesis. In contrast, no correlation is found between vegetation density and other parameters such as trunk‐channel width or surface area of the largest meander in a wash. Similarly, no statistical correlation exists between vegetation density and meander size or sinuosity index. Rather, larger and more sinuous meanders are invariably associated with lower vegetation density. These results are corroborated by field evidence showing that sparse vegetation promotes flow disturbance, channel branching and bar braiding instead of stabilizing sediment surfaces. Thus, river meandering is attributed to cohesion offered by mud retention within the endorheic basin, as well as discharge and stream‐power modulation along bifurcating and low‐gradient channel reaches. Hence, this work demonstrates how meandering‐channel patterns may arise from entirely physical forcings in the absence of vegetation.

Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin

The estimation of fractional vegetation cover (FVC) by using remote sensing images has become feasible. Based on Landsat8-OLI images and field data obtained from an unmanned aerial vehicle, we established an empirical model (EM) and a pixel decomposition model (PDM) of FVC in the desert vegetation region, steppe vegetation region, meadow vegetation region and mixed vegetation region (the three vegetation region types) of the Qaidam Basin, and the inversion accuracies of the models were compared. The results show the following: (1) Vegetation classification inversion (VCI) provides a promising approach for FVC estimation. The accuracy of FVC by VCI was obviously better than that achieved using vegetation mixed inversion (VMI); (2) Differences were observed in the FVC estimation between VCI and VMI by the EM in areas with relatively high-density vegetation cover (FVC > 60%). The FVC in some parts of steppe region in the basin was slightly overestimated by VMI of the EM; 3) VCI estimated by the PDM resulted in lower inversion values for extremely low-density vegetation cover (FVC ≤ 10%) and higher inversion values for high-density vegetation cover (FVC > 80%). The FVC inversion was underestimated by the PDM in steppe and meadow regions with FVC > 15% in the basin. The application of VCI in different models can provide new ideas for the sustainable study of vegetation in arid regions.

Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco

The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 and 2017. To achieve this, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987; one Enhanced Thematic Mapper Plus (ETM+) scene from 2000; two Thematic Mapper (TM) scenes from 1995 and 2011; and one Landsat 8 Operational Land Imager (OLI) scene from 2017; were acquired and processed. Ground-based survey data and the normalized difference vegetation index (NDVI) were used to identify and to improve the discrimination between LULC categories. Then, the maximum likelihood (ML) classification method was applied was applied, in order to produce land cover maps for each year. Three classes were considered by the classification of NDVI value: low-density vegetation; moderate-density vegetation, and high-density vegetation. Our study achieved classification accuracies of 66.8% (1987), 99.9% (1995), 99.8% (2000), 99.9% (2011), and 99.9% (2017). The results from the Landsat-based image analysis show that the area of low-density vegetation was decreased from 27.4% to 2.1% over the past 30 years. While, in 2017, the class of high-density vegetation was increased to 64.6% of the total area of study area. The results of this study show that the total forest cover remained stable. The present study highlights the importance of the image classification algorithms combined with NDVI index for better understanding the changes that have occurred in this forest. Therefore, the findings of this study could assist planners and decision-makers to guide, in a good manner, the sustainable land development of areas with similar backgrounds.

Modelling above-ground biomass based on vegetation indexes: a modified approach for biomass estimation in semi-arid grasslands

ABSTRACTRemote sensing could be the most effective means for scaling up grassland above-ground biomass (AGB) from the sample scale to the regional scale. Remote sensing approaches using statistical models based on vegetation indexes (VIs) are frequently used because of their simplicity and reliability. And many researchers have already proven the method is scientific, feasible, and can bring relatively better effects in practice. However, the only deficiency of the method has been criticized because of the uncertainties introduced by saturation of spectral reflectance at high-density vegetation levels and the soil surface at low-density vegetation levels. Therefore, in this study, we aimed to improve grassland AGB estimates by using modified VIs (MVIs) to minimize the influence of the soil background. The field study was conducted in the Chen Barag Banner, the Ewenkizu Banner, and the Xin Barag Left Banner in the Hulun Buir Grassland, Inner Mongolia, northern China. Field plots were photographed and AGB samples were collected during field sampling. Remote sensing data were obtained from MOD09A1 (TERRA satellite). Four MVIs were first calculated based on the corresponding VI: the Ratio Vegetation Index (RVI), the Normalized Differential Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the Modified Soil-Adjusted Vegetation Index (MSAVI), by improving estimates of vegetation cover (VC). Then, MVIs, i.e., MRVI, MDVI, MNDVI, and MMSAVI, were regressed with the sample-scale AGB using an exponential function, a linear function, a logarithmic function, and a power function. When the accuracy of the models was tested by comparing root mean square error (RMSE), relative error (RE), and coefficient of determination (R2), the results demonstrated that MVI-AGB models performed better than the VI-AGB models. The logarithmic MNDVI-AGB model was the best of the regression functions. This model gave the best estimates of AGB from remote sensing data, compared with the values measured in field analyses. Our proposed method provides a new way to estimate regional grassland AGB and will be useful to analyze ecosystem responses under climate change.

HIGH-RESOLUTION ENHANCED PRODUCT BASED ON SMAP ACTIVE-PASSIVE APPROACH USING SENTINEL 1A AND 1B SAR DATA

Abstract. SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-1A and -1B Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program’s Sentinel-1A and -1B multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density (∼ < 3 kg m−2). The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

Description of a new species of the genus Agamura Blanford, 1874(Squamata: Gekkonidae) from southern Iran.

Herein, we describe a new species of spider gecko, Agamura kermanensis sp. nov., from south Kerman, Iran. The new species is distinguished from other species by having long forelimbs and hindlimbs and a high interorbital distance. Agamura kermanensis sp. nov. was discovered in the Faryab region of southern Kerman Province, which has a xeric climate and low vegetation density. The habitat of the species is similar to that of A. cruralis in southeast Iran. It can be assumed that the distribution of the species is more extensive than currently known. More investigation is required in Jazmourian and the surrounding areas. This is the third species of the genus Agamura discovered on the Iranian Plateau.

Efek Penggunaan Lahan Terhadap Degradasi Tanah Pada Kebun Campuran Di Kecamatan Kairatu Kabupaten Seram Bagian Barat Propinsi Maluku

Land use is the most influential factor in soil degradation due to erosion. The objectives of this research are to know the level of land degradation and the factors that influence the land use area, the upper vegetation density and the lower vegetation density. This research uses the method of measuring ground damage indicator in the field. The results showed that single, wide land use, upper vegetation density and lower vegetation density had an effect on soil degradation on mixed garden land use; while simultaneously, wide land use, upper vegetation density and lower vegetation density have an effect on soil degradation on mixed garden land use. The most influential factors for soil degradation in mixed gardens are the area of land use and lower vegetation density.Keyword: Degradation of soil, land, vegetation density, mixed gardens.

Study of the Recharge Area on Water Basin Soil with Remote Sensing Method Using Satellite Imagery Landsat 7 ETM + and Geographic Information Systems ( GIS ) (Case Study : Pasuruan District )

Water is natural resources that is very essential for living creatures and the environment. Along with the increase of population , will increase the water demand and reduce the area of free land / open green space for the formation of ground water. By considering soil water conservation aspects, it is necessary to conduct a study of the recharge area on water basin soil in Pasuruan. This study was to determine the condition of the recharge area in the Basin Groundwater Pasuruan in 2003 and 2014. So it will be k[1]nown the changes of the vegetations density and land cover in the recharge area that can affect infiltration process in order to conserve water resources in CAT Pasuruan. From these changes it would be able to make the prediction of future conditions in the recharge area in CAT Pasuruan. This study uses Remote Sensing using Satellite imagery Landsat and Geographic Information System (GIS). Studies to determine the recharge area is influenced by several parameters, that is slope of land, lithology and Rainfall. Processing results obtained vegetation density changes of the recharge area in 2003-2014, high vegetation density decreased by 4330,89 Ha, so that changes in the high vegetation density in 2025 is predicted to be 52305,39 Ha and become 52176,78 Ha in 2030. While land cover changes produce changes in forest land cover increased by 569,88 Ha , so that changes in forest land cover in 2025 is predicted to be 37563,88 Ha and became 38720,494 Ha in 2030. The relationship between vegetation density and land cover of the recharge area in 2003 - 2014 obtained high vegetation density that experienced an increase area, which is on garden land cover amounting to 1144,80 Ha, 943,02 Ha of forest, 947,79 Ha of settlements land cover and 262,53 Ha of vacant land. And that experienced an increase with low vegetation density are the fields amounting to 943,02 Ha and 277,47 Ha of vacant land. Land cover vegetations that dominates throughout the recharge area is a mahogany tree vegetation, and sengon.

Exploring the contributions of vegetation and dune size to early dune development using unmanned aerial vehicle (UAV) imaging

Abstract. Dune development along highly dynamic land–sea boundaries is the result of interaction between vegetation and dune size with sedimentation and erosion processes. Disentangling the contribution of vegetation characteristics from that of dune size would improve predictions of nebkha dune development under a changing climate, but has proven difficult due to the scarcity of spatially continuous monitoring data. This study explored the contributions of vegetation and dune size to dune development for locations differing in shelter from the sea. We monitored a natural nebkha dune field of 8 ha, along the coast of the island Texel, the Netherlands, for 1 year using an unmanned aerial vehicle (UAV) with camera. After constructing a digital surface model and orthomosaic we derived for each dune (1) vegetation characteristics (species composition, vegetation density, and maximum vegetation height), (2) dune size (dune volume, area, and maximum height), (3) degree of shelter (proximity to other nebkha dunes and the sheltering by the foredune). Changes in dune volume over summer and winter were related to vegetation, dune size and degree of shelter. We found that a positive change in dune volume (dune growth) was linearly related to initial dune volume over summer but not over winter. Big dunes accumulated more sand than small dunes due to their larger surface area. Exposed dunes increased more in volume (0.81 % per dune per week) than sheltered dunes (0.2 % per dune per week) over summer, while the opposite occurred over winter. Vegetation characteristics did not significantly affect dune growth in summer, but did significantly affect dune growth in winter. Over winter, dunes dominated by Ammophila arenaria, a grass species with high vegetation density throughout the year, increased more in volume than dunes dominated by Elytrigia juncea, a grass species with lower vegetation density (0.43 vs. 0.42 (m3 m−3) week−1). The effect of species was irrespective of dune size or distance to the sea. Our results show that dune growth in summer is mainly determined by dune size, whereas in winter dune growth was determined by vegetation type. In our study area the growth of exposed dunes was likely restricted by storm erosion, whereas growth of sheltered dunes was restricted by sand supply. Our results can be used to improve models predicting coastal dune development.

Short-term changes in spatial distribution pattern of an herbivore in response to accumulating snow

Deep snow can reduce accessibility to vegetation and cover by herbivores by blanketing understory cover, yet simultaneously increase access to foliage at higher levels. Thus, snow depth fluctuation should lead to spatiotemporal variation in herbivore habitat use. We measured shifts in habitat use by snowshoe hare (Lepus americanus Erxleben, 1777) as a function of snow depth in an eastern Canadian boreal forest where snow depth often exceeds 1 m. We hypothesized that as snow accumulates, snowshoe hares shift from locations with dense vegetation just above ground to locations with dense vegetation higher above ground. We surveyed 58 km of transects over three winters and found 1954 hare tracks. We analyzed track counts as a response to a density index of low vegetation (0–1.5 m above ground), high vegetation (2–4 m above ground), predator tracks, and snow depth. We found more hare tracks in sites with dense high vegetation when snow was deeper, and more hare tracks in sites with dense low vegetation when snow was shallower. Predator track presence did not influence responses to snow depth. Snow depth dynamics can drive hare distribution, and in turn, introduce uncertainty in spatial distribution models for the species and possibly its interactions with predators.

Evaluation of Typical Spectral Vegetation Indices for Drought Monitoring in Cropland of the North China Plain

Spectral vegetation index (SVI) is indispensable for comprehensively monitoring of agricultural drought based on remote sensing. Greenness sensitive (e.g., normalized difference vegetation index) and water sensitive SVIs (e.g., normalized difference infrared index) were mostly concerned in drought monitoring. Their sensitivity to drought conditions has been explored yet distinct results were found. This contradiction has not been fully understood in previous studies. In this study, the greenness and water sensitive SVIs were further evaluated in a cropland area of the North China Plain by using MODIS reflectance products from 2000 to 2015. Results indicate that fractional vegetation coverage (FVC) plays an important role in the sensitivity of SVIs to drought condition. During early months of the crop growth, when FVC is relatively low, greenness sensitive indices had closer correlation with in situ drought indices. On the contrary, water sensitive indices presented better correlation during the later months of the crop growth when FVC is high. This study further suggests that variations in vegetation water content are more dynamic than changes in vegetation greenness properties under the influence of drought. However, soil background effects will make greenness sensitive indices outperform water sensitive indices for mid and low density vegetation coverage.

Correlates of distribution and nesting success in a Welsh upland Eurasian Curlew Numenius arquata population

ABSTRACTCapsule: Habitat structure and composition explained spatial variation in breeding distribution and nesting success in a declining upland Eurasian Curlew Numenius arquata population in North Wales.Aims: To identify environmental correlates of Curlew breeding distribution, nesting success and change in distribution.Methods: Thirty random 1 km squares stratified by historical population trend were surveyed for Curlew density and nesting success, and habitat- and predation-related variables in a landscape containing agriculturally improved farmland, and moorland that was partly protected and subject to grazing reductions for nature conservation. Analyses tested for associations between Curlew measures and environmental variables.Results: Curlew breeding density declined by 29% between 1994 and 2008, and was highest in squares comprising a mixture of moorland and agriculturally improved farmland, and in squares with lower vegetation density and higher cover of Nardus stricta (characteristic of rough grazing). Nesting success was positively associated with cover of Trichophorum germanicum (characteristic of mire). Vegetation density was lower than average in squares with the highest Curlew densities, while in the protected area vegetation density was higher than average.Conclusion: Habitat and vegetation variables influenced Curlew distribution and nesting success in North Wales, largely in line with previous results but with no evidence for predator-related influences. Habitat condition for Curlew in the protected area could probably be increased through targeted increases in grazing alongside the protection of priority habitats.

ASSESSING TSUNAMI VULNERABILITY AREAS USING SATELLITE IMAGERY AND WEIGHTED CELL-BASED ANALYSIS

The application of multi-criteria analysis followed by weighted cell-based processing is one of the methods for tsunami vulnerability mapping. In this study, vulnerability due to tsunami disaster in coastal area of East Java Province Indonesia was carried out. Appropriate input criteria were derived from Digital Elevation Model data, satellite remote sensing and survey data. The criteria applied were elevation, slope, coastal proximity, river proximity, coastal type, and land use. Five classes of vulnerability were defined from low to high vulnerability. Digital Elevation Model from Aster GDEM was applied for creating elevation and slope map, while ALOS satellite image was used in land use mapping. Moreover, vector map of East Java coastal area was used for creating coastal type, coastal proximity, and river proximity map. Analytical hierarchy process was applied in the calculation of parameter’s weight, and it described that elevation was the highest weight. The area that identified as slightly high and high class of tsunami vulnerability spread in the coastal area which has a lower elevation and predict as inundated area. Most of the area was urban areas with low vegetation density. The high vulnerability areas were mostly found in the coastal area with the sloping coast type. The result presented here can aid as a basic data for city planning related to disaster mitigation and for the evacuation process and management strategy during disaster.

Seasonal and ageing effects on SFTW hydrodynamics study by full-scale tracer experiments and dynamic time warping algorithms

Surface flow treatment wetlands (SFTW) are considered as ecological engineering treatment wetlands used for wastewater and stormwater treatment. In France SFTW are commonly located between the wastewater treatment plant (WWTP) and the receiving aquatic environment. Furthermore, they are not considered as a regulatory treatment step but as a complementary element to WWTP. In this regard, there is currently no established design and sizing rule since (i) they are built on the remaining space after WWTP construction, (ii) it is a nature-based system, subject to high variability and complex interactions and (iii) feedback on ageing effects is lacking. This study permitted to qualify and to quantify seasonal and ageing effects on the SFTW hydraulic behavior which is a pond. The combination of fluorescent dye tracer application in a real system and data analysis by dynamic time warping (DTW) allowed to perform efficiency comparisons between all campaigns. Similar behaviors of dimensionless retention time distributions were observed for all campaigns. They were eventually linked with the SFTW shape. The analysis of these curves highlighted that the measured mean residence time was always lower than the nominal hydraulic residence time, due to preferential flow and dead-zones. A statistical approach suggested that the winter hydraulic behavior was characterized by the highest short-cutting index and strongest convective flow regime in comparison to others campaigns. A lower vegetation density, especially subaquatic plants, and presence of important preferential flow could explain this observation. DTW processing on RTD curves shed light on the ageing effect that has a much bigger impact than seasonal effect. This is mainly due to sediments accumulation, sludge and vegetation development.