Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R² = 0.99 for streambed width, R² = 0.82 for riparian zone width, R² = 0.89 for PPC, R² = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia.     

Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake,China

Knowledge of the composition and areal distribution of aquatic vegetation types, as well as their seasonal and interannual variations, is crucial for managing and maintaining the balance of lake ecosystems. In this study, a series of remotely sensed images with a resolution of 30 m (HJ-CCD and Landsat TM) were collected and used to map the distribution of aquatic vegetation types in Taihu Lake, China. Seasonal and interannual dynamics of aquatic vegetation types were explored and analyzed. The distribution areas of Type I (emergent, floating-leaved and floating vegetation) and Type II (submerged vegetation) were used to model their growing season phenology by double logistic functions. The resulting double logistic models showed, the area of Type I reached its peak in mid-August, and the maximum area for Type II occurred in mid-September. From 1984 to 2013, Type I area increased continuously from 59.75 km² to 148.00 km² (R² = 0.84), whereas the area covered by Type II first increased and then decreased, with a trend conforming to a significant quadratic curve (R² = 0.83). The eutrophication and stable state of Taihu Lake was assessed using a simple indicator which was expressed as a ratio of Type II area to Type I area. The results showed that the eutrophication in the lake might have been increasing in the area studied since 2000. Additionally, the results showed that air temperature had likely a direct effect on the growth of Type I (R² = 0.66) and a significant, but delayed, effect on the growth of Type II.     

Bio-thermal effects of open dumps on surroundings detected by remote sensing—Influence of geographical conditions

This study has used remotely sensed data of Landsat-8 for monitoring open dumps of Municipal Solid Waste (MSW) using vegetation health as a bio-indicator and thermal emissions from it. Open dump of Mahmood Booti has been found to affect the surrounding vegetation up to 800 m in dry summers and reducing to 400 m in winters, while averaging to a distance of about 650 m. Average thermal influence zone has been observed to have same radial extent of about 650 m varying between the minimum of 350 m in dry summer and maximum of 1000 m in winter. All the corresponding details of bio-indicators and temperature variations have also been discussed. In addition to this, the results and methodology of spatial analysis for Mahmood Booti dump of Lahore, Pakistan, surrounded by a heterogeneous land cover, have been compared with the main dumping facility of Faisalabad, Pakistan, which is surrounded by a homogeneous vegetation cover all around. This comparison yielded two main conclusions, first, the surrounding geography of an open MSW dump affects the severity of bio-thermal effects, in addition to waste age, characterization, pile etc. Second, GIS analysis for studying bio-thermal effects requires modification that varies for prevailing neighborhood land cover conditions of MSW open dumps. Use of remotely sensed data for monitoring dumped MSW is a good alternative but selection of proper GIS methodology, representing natural setting of phenomena is equally important as that of the accuracy of the remotely sensed data.     

The seed bank in a subtropical freshwater marsh: implications for wetland restoration

Seed bank samples were collected from Huli Marsh, a subtropical shallow water mountainous marsh in Hunan Province, South China. Core samples were divided into upper and lower layers (each 5 cm in depth) and allowed to germinate in three water levels (0, 5 and 10 cm) over a 4-month period. A total of 51 species germinated and the mean density was 9211 ± 7188 seedlings m⁻². In the top 5 cm 41 species and 5747 ± 5111 seedlings m⁻² germinated, whereas 40 species and 3464 ± 3363 seedlings m⁻² did so from 5–10 cm. Germinated seedling density was significantly higher in the upper layer, largely due to differences in eight species. With increasing experimental water depth, less seedlings germinated: respectively, 9788 ± 7157 m⁻², 2050 ± 2412 m⁻² and 1978 ± 2616 m⁻², of 44, 21 and 19 species, submerged under 0, 5 or 10 cm. Seven species could emerge only in 0 water level. Vallisneria natans occurred only in 5 cm water, whereas Ottelia alismoides occurred in 10 cm water. In the vegetation survey of the marsh, 25 species were recorded, which was less than half of the species recorded in the seed bank. The top 10 dominants in the standing vegetation, accounting for 89% of vegetation abundance, represented only 10% in the seed bank. Twenty germinated species that also occurred in the standing vegetation accounted for 56% of the total seed bank. Our observed number of species germinating from a Chinese wetland seed bank is within the range observed elsewhere in the northern hemisphere (15–113 species).     

Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa

Texture information from passive remote sensing images provides surrogates for habitat structure, which is relevant for modeling biodiversity across space and time and for developing effective ecological indicators. However, the applicability of this information might differ among taxa and diversity measures. We compared the ability of indicators developed from texture analysis of remotely sensed images to predict species richness and species turnover of six taxa (trees, pyraloid moths, geometrid moths, arctiinae moths, ants, and birds) in a megadiverse Andean mountain rainforest ecosystem. Partial least-squares regression models were fitted using 12 predictors that characterize the habitat and included three topographical metrics derived from a high-resolution digital elevation model and nine texture metrics derived from very high-resolution multi-spectral orthophotos. We calculated image textures derived from mean, correlation, and entropy statistics within a relatively broad moving window (102 m × 102 m) of the near infra-red band and two vegetation indices. The model performances of species richness were taxon dependent, with the lowest predictive power for arctiinae moths (4%) and the highest for ants (78%). Topographical metrics sufficiently modeled species richness of pyraloid moths and ants, while models for species richness of trees, geometrid moths, and birds benefited from texture metrics. When more complexity was added to the model such as additional texture statistics calculated from a smaller moving window (18 m × 18 m), the predictive power for trees and birds increased significantly from 12% to 22% and 13% to 27%, respectively. Gradients of species turnover, assessed by non-metric two-dimensional scaling (NMDS) of Bray-Curtis dissimilarities, allowed the construction of models with far higher predictability than species richness across all taxonomic groups, with predictability for the first response variable of species turnover ranging from 64% (birds) to 98% (trees) of the explained change in species composition, and predictability for the second response variable of species turnover ranging from 33% (trees) to 74% (pyraloid moths). The two NMDS axes effectively separated compositional change along the elevational gradient, explained by a combination of elevation and texture metrics, from more subtle, local changes in habitat structure surrogated by varying combinations of texture metrics. The application of indicators arising from texture analysis of remote sensing images differed among taxa and diversity measures. However, these habitat indicators improved predictions of species diversity measures of most taxa, and therefore, we highly recommend their use in biodiversity research.     

Characterization of spatial scaling relationships between vegetation pattern and topography at different directions in Gurbantunggut desert,China

Vegetation striped pattern is a common feature in semiarid and arid landscapes, which is seen as mosaics including vegetated and non-vegetated patches. Identifying scales of pattern in ecological systems and referring patterns to multi-scaled processes that create them are ongoing challenges. The aim of this paper is to study the vegetation patterns and their across-scale relationships between the vegetation and anisotropic topography (W–E and N–S) in 12 transects at Gurbantunggut desert. We used wavelet-based across-scale analysis for extracting information on scales of pattern for those transect data, evaluating their inherent structure, and inferring characteristics of the processes that imposed those patterns at across scales. The results show that, in W–E direction, the scales of vegetation pattern (C. ewersmanniana is at the scale 40 m, H. ammodendron, at 35 m) correspond to the dune ridge/dune valley sequences (appearing at distance of 40 m), and vegetation on mesoscale and large scale are significant cross-scale correlation with topography on mesoscale and large scale in all W–E transects. In N–S direction, there is an irregular pattern of vegetation along the N–S irregular topography, and no unified cross-scale relationships between topography and vegetation on different scales in different transects. Moreover, cross-scale correlation analysis between topography and vegetation provides further detail on hierarchical structure and specific scales in space that strongly influenced the larger patterns. Knowledge of the cross-scale relationships between topography and vegetation could lead to better understanding and management of biological resources in that region.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

The morpho-agronomic characterization study of Lens culinaris germplasm under salt marsh habitat in Swat,Pakistan

The present research study evaluate and identify the most suitable and high yielding genotypes of Lens culinaris for the salt marsh habitat of Swat in moist temperate sort of agro climatic environment of Pakistan. A total of fourteen genotypes were cultivated and analyzed through Randomized Complete Block Design (RCBD). These genotypes were AZRC-4, NL-2, NL4, NL-5, NL-6, NARC-11-1, NARC-11-2, NARC-11-3, NARC-11-4, 09503, 09505, 09506, P.Masoor-09 and Markaz-09. Different parameters i.e., germination rate, flowering, physiological maturity, plant height, biological grain yield, seed weight, pods formation and its height, pods per plants and protein content were focused specially throughout the study. Preliminary the Lentil genotypes have significant variability in all the major morpho-agronomic traits. The days to germination, 50% flowering and 100 seed weight ranged from 7 to 9, 110 to 116 days, and from 5.4 to 7.3 gm respectively. Biological yield and grain yield ranged from 5333 to 9777 kg ha⁻¹ and 1933 to 3655 kg ha⁻¹ respectively. Whereas, protein contents ranged from 23.21% to 28.45%. It was concluded that the genotype AZRC-4 is better varity in terms of grain yield plus in 100 seed weight and moreover, 09506 genotype was significant under salt marsh habitat in early maturing for the Swat Valley, Pakistan.     

Mayfly bioindicator thresholds for several anthropogenic disturbances in neotropical savanna streams

Anthropogenic disturbances are widely recognized as major threats to terrestrial and aquatic biodiversity worldwide, including areas located in non-forest ecosystems. Headwater streams in the neotropical savanna are severely threatened by large-scale landscape changes that degrade local habitat characteristics and lead to biodiversity loss. The objective of our study was to evaluate Ephemeroptera assemblages as bioindicators of catchment land use and cover, local streambed and riparian vegetation conditions, and instream water quality. To do so, we sampled mayfly nymphs in 184 stream sites across a broad disturbance gradient in four hydrologic units of the Brazilian neotropical savanna. We selected seven metrics without significant co-variation with natural variability: % catchment urban, riparian vegetation condition index (RCOND), human disturbances of the stream channel and riparian zone (W1_HALL), substrate mean embeddedness (XEMBED), dissolved oxygen (mg L⁻¹), pH, and total phosphorus (mg L⁻¹). We ran threshold indicator taxa analysis (TITAN) for each disturbance metric to detect change points in mayfly genera responses (whether sensitive or tolerant) and assemblage turnover pattern. TITAN showed that 20 of the 39 genera found were robust bioindicators (based on purity and reliability values >0.95), sixteen of them being sensitive to increased disturbance. The most sensitive genera were Tricorythopsis (Leptohyphidae) and Camelobaetidius (Baetidae), showing decreased abundance to most disturbance metrics. We found a turnover pattern of mayfly genera in response to W1_HALL in a narrow variation range. For total phosphorus, the benchmark value defined in Brazilian Federal Legislation is higher than the turnover threshold of several mayfly genera. This indicates that we will lose many sensitive genera even within the limits imposed by national environmental legislation. The indicator taxa approach, based on multiple taxa rather than univariate metrics or single indicator species, demonstrates the value of quantitative ecological information for conserving and managing freshwater ecosystems globally.     

Phragmites australis and silica cycling in tidal wetlands

Tidal marshes have recently been shown to be important biogenic Si recycling surfaces at the land–sea interface. The role of vegetation in this recycling process has not yet been quantified. In situ and ex situ decomposition experiments were conducted with Phragmites australis stems. In a freshwater tidal marsh, litterbags were incubated at different elevations and during both winter and summer. Biogenic Si (BSi) dissolution followed a double exponential decay model in the litterbags (from ca. 60 to 15 mg g⁻¹ after 133 days), irrespective of season. Si was removed much faster from the incubated plant material compared to N and C, resulting in steadily decreasing Si/N and Si/C ratios. Ex situ, decomposition experiments were conducted in estuarine water, treated with a broad-spectrum antibiotic, and compared to results from untreated incubations. The bacterial influence on the dissolution of dissolved Si (DSi) from P. australis stems was negligible. Although the rate constant for dissolved Si dissolution decreased from 0.004 to 0.003 h⁻¹, the eventual amount of BSi dissolved and saturation concentration in the incubation environment were similar in both treatments. P. australis contributes to and enhances dissolved Si recycling capacity of tidal marshes: in a reed-dominated small freshwater tidal marsh, more than 40% of DSi export was attributable to reed decomposition. As the relation between tidal marsh surface and secondary production in estuaries has been linked to marsh Si cycling capacity, this provides new insight in the ecological value of the common reed.     

Ecological re-engineering of a freshwater impoundment for salt marsh restoration in a hypertidal system

The purpose of this paper was to examine the vegetative, sedimentary, nekton and hydrologic response to ecological re-engineering of a freshwater impoundment in the Upper Bay of Fundy. The dyke was breached in five locations and one channel initiated to connect the river to the borrow pit behind the dyke. This triggered significant self-organization within the restoration site. Existing channels (e.g. borrow pit) were incorporated within the newly excavated and developing creek system, increasing the hydraulic connectivity within the marsh and increasing fish habitat. Vegetation colonization, primarily by Spartina alterniflora, was rapid with almost 100% coverage by the end of the third year. Species associated with high marsh communities such as Juncus gerardii, Scirpus robustus, Ranunculus cymbalaria and Puccinellia maritima were present in increasing abundance post-restoration at the restoration site. The constructed channel eroded downward 2.3 m and the head of the channel retreated 35 m in response to the increased tidal prism. In the year immediately following the breach (2006), the surface of the marsh was unconsolidated and rates of change in surface elevation measured at RSET stations ranged from ?0.7 (±0.1) to 1.7 (±0.2) cm yr^?1 (±1SE). Measurements between years are highly variable. By year 3 the rate of surface elevation change decreased to a more moderate but variable mean of 0.3 (±0.6) cm yr^?1 with the marker horizons recording mean accretion rates of 0.7 cm yr^?1. This implies subsurface consolidation as the sediments dewatered or organic matter decomposed and vegetation became more established. Fish density decreased after restoration, however, remained higher than at the reference site for most years.     

Micro-topography driven vegetation patterns in open mosaic landscapes

Elevation models based on remotely sensed data, especially high-resolution Digital Terrain Models (DTMs) generated using airborne laser scanner (ALS) data, are increasingly being used for the analysis of plant diversity patterns in open landscapes. The vegetation pattern of alkali landscapes shows a high correlation with the position of water table and salt accumulation, which are strongly correlated with topographic variations occurring at a small spatial scale of a few decimetres (micro-topography). In this study we classified eight grassland associations in an alkali landscape based on a DTM generated from ALS data at a pixel size of 0.25 m, and 30 variables derived from the DTM, using an ensemble learning method (Random Forest). Our aim was to identify the micro-topographic variables which could be indicators of vegetation pattern in alkali landscapes. The associations range from Cynodon pastures (short dry grasslands on soil with low salt content) occupying the highest elevations to Beckmannia meadows (wet grasslands on soils with moderate salt content composed of tall grass species) at the lowest elevations, with an elevation difference of approximately 1.2 m between the two. Apart from slope, aspect and curvature, we used Topographic Wetness Index (TWI), and Topographic Position Indices (TPI) at various kernel sizes ranging from 50 cm to 500 m for the classification. The eight associations were also grouped into four aggregated categories — loess grasslands, alkali steppes, open alkali swards and alkali meadows — for further analysis. Vegetation of the studied alkali landscape could be classified into the eight associations with an accuracy of κ: 0.56, and into the four aggregated categories with an accuracy of κ: 0.77 using all the variables. Sequential backward and forward selections of variables were implemented to reduce the number of variables while maximising the accuracies, resulting in increased accuracies of κ: 0.72 and κ: 0.83 for the associations and aggregated categories using six and three variables respectively. TPI at different kernel sizes, previously used to explain vegetation distribution in mountainous areas, was found to be a better indicator of vegetation types than absolute elevations in lowlands where the elevation differences are more subtle. Two characteristic features of the study area — erosional channels and alkali steps — could also be delineated using micro-topographic variables. The results point to the possibility of large-area mapping and monitoring of grasslands where micro-topography is an indicator of vegetation, using only the elevation data from ALS.     

Spatial pattern model of herbaceous plant mass at species level

Individual plant species distribute according to their own spatial pattern in a community. In this study, we proposed an index for measuring the spatial heterogeneity in mass (dry weight) of individual plant species. First, we showed that the frequency distributions for mass of individual plant species per quadrat in a plant community are expressed using the gamma distribution with two parameters of λ (mean) and p. The parameter p is a measure indicating the level of spatial heterogeneity of plant mass as follows: (1) when p = 1, the plant mass per quadrat has a random pattern; (2) when p > 1, the plant mass has a spatial pattern with a lower heterogeneity than would be expected in the random pattern; and (3) when p < 1, the plant mass has a spatial pattern with a higher heterogeneity than would be expected in the random pattern. The p value for a given species can easily be calculated by the following equation if we use the moment method: (mean plant mass among quadrats)² / (variance of plant mass among quadrats). The scatter diagram of (λ, p) for individual plant species, exhibits the spatial characteristics of each species in the community. We illustrated two examples of the (λ, p) diagram from data for individual species composing actual communities in a semi-natural grassland and a weedy grassland. Frequency distributions for the plant mass of individual species per quadrat followed the gamma distribution, and indi vidual species exhibited an inherent level of spatial heterogeneity.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay

The aim of this study was to determine the effects of catchment and riparian stream buffer-wide urban and non-urban land cover/land use (LC/LU) on total nitrogen (TN) and total phosphorus (TP) runoff to the Chesapeake Bay. The effects of the composition and configuration of LC/LU patches were explored in particular. A hybrid-statistical-process model, the SPAtially Referenced Regression On Watershed attributes (SPARROW), was calibrated with year 1997 watershed-wide, average annual TN and TP discharges to Chesapeake Bay. Two variables were predicted: (1) yield per unit watershed area and (2) mass delivered to the upper estuary. The 166,534 km² watershed was divided into 2339 catchments averaging 71 km². LC/LU was described using 16 classes applied to both the catchments and also to riparian stream buffers alone. Seven distinct landscape metrics were evaluated. In all, 167 (TN) and 168 (TP) LC/LU class metric combinations were tested in each model calibration run. Runs were made with LC/LU in six fixed riparian buffer widths (31, 62, 125, 250, 500, and 1000 meters (m)) and entire catchments. The significance of the non-point source type (land cover, manure and fertilizer application, and atmospheric deposition) and factors affecting land-to-water delivery (physiographic province and natural or artificial land surfaces) was assessed. The model with a 31 m riparian stream buffer width accounted for the highest variance of mean annual TN (r² = 0.9366) and TP (r² = 0.7503) yield (mass for a specified time normalized by drainage area). TN and TP loadings (mass for a specified time) entering the Chesapeake Bay were estimated to be 1.449 × 10⁸ and 5.367 × 10⁶ kg/yr, respectively. Five of the 167 TN and three of the 168 TP landscape metrics were shown to be significant (p-value ≤ 0.05) either for non-point sources or land-to-water delivery variables. This is the first demonstration of the significance of riparian LC/LU and landscape metrics on water quality simulation in a watershed as large as the Chesapeake Bay. Land cover metrics can therefore be expected to improve the precision of estimated TN and TP annual loadings to the Chesapeake Bay and may also suggest changes in land management that may be beneficial in control of nutrient runoff to the Chesapeake Bay and similar watersheds elsewhere.     

Variation of soil nutrients and particle size under different vegetation types in the Yellow River Delta

The Yellow River Delta wetland, located at the southern coast of Bohai Gulf, provides important ecosystem services such as flood control, water purification, biodiversity conservation, nutrient removal and carbon sequestration, shoreline stabilization, tourism attraction and wetland products maintains in the Yellow River Delta. This study assessed how agricultural activities in a reclamation wetland changed soil pH, soil electric conductivity, soil nutrient and soil particle size as compared to natural vegetation by using a combination of field experiments and lab analysis. The vegetation type included adjacent alfalfa field (Medicago sativa), cotton field (Gossypium spp.), Chinese tamarisk shrub (Tamarix chinensis), and reed marsh (Phragmites sage). The results indicated that the soil pH was higher (pH > 8) in alfalfa field and cotton field, and alfalfa field and reed marsh had significant function in reducing soil salt content, soil electric conductivity of alfalfa field at 0–30 cm were 140.38 ± 14.36, 114.48 ± 14.36, 125.30 ± 11.37 μs/cm. The effect of different vegetation types on soil nutrient was significant (P < 0.05). Soil organic matter at 0–10 cm in Chinese tamarisk shrub and reed marsh was 21.66 ± 3.82 g/kg and 16.51 ± 4.60 g/kg, which was higher than that of alfalfa field (10.47 ± 2.36 g/kg) and cotton field (9.82 ± 1.27 g/kg), but soil total nitrogen content in alfalfa field was the highest, which is significantly higher than that of cotton field, Chinese tamarisk shrub and reed marsh(P < 0.05), the content of soil total nitrogen at 0–10 cm and 10–20 cm was 7.67 ± 0.38 g/kg and 5.97 ± 0.51 g/kg, respectively, while the content of available P and available K was reversed. The difference of soil particle size between layers was not significant (P > 0.05), the sand content of Chinese tamarisk shrub soils in 0–10 cm was the highest, the next was alfalfa field and cotton field, and the content of silt and clay in reed marsh was higher than the others. The correlation and significant degree between soil particle size and soil nutrient was related with vegetation types, and soil organic matter was significantly positively correlated with soil silt and clay content on the alfalfa field. The results demonstrated that wetland cultivation was one of the most important factors influencing on the nutrient fate and reserves in soil, which could lead to rapid nutrient release and slow restoration through abandon cultivation. Consequently, compared with cotton field, alfalfa field is more favorable to sustainable management of wetland cultivation in the Yellow River Delta. It should be considered in wetland restoration projects planning.     

Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains,USA

There is uncertainty about the extent and distribution of grasslands following the C3 and C4 photosynthetic pathways. Since these grasses have an asynchronous seasonal profile it should be possible to estimate and map the C3–C4 composition of grasslands from multi-temporal remote sensing imagery. This potential was evaluated using 30 weekly composite MERIS MTCI images for South Dakota, USA. Derived relationships between the remotely sensed response and composition of grasslands were significant, with R² ∼ 0.6. It also appears possible to map broad classes of grassland composition, with a three class (high, medium and low C3 cover) classification having an accuracy of 77.8%.     

Spatial metrics for detecting ecosystem degradation in the ridge-slough patterned landscape

Indicators of landscape condition should be selected based on their sensitivity to environmental changes and their capacity to provide early warning detection of those changes. We assessed the performance of a suite of spatial-pattern metrics selected to quantify the condition of the ridge-slough landscape in the Everglades (South Florida, USA). Spatial pattern metrics (n = 14) that describe landscape composition, geometry and hydrologic connectivity were enumerated from vegetation maps of twenty-five 2 × 2 km primary sampling units (PSUs) that span a gradient of hydrologic and ecological condition across the greater Everglades ecosystem. Metrics were assessed in comparison with field measurements from each PSU of landscape condition obtained from regional surveys of soil elevation, which have previously been shown to capture dramatic differences between conserved and degraded locations. Elevation-based measures of landscape condition included soil elevation bi-modality (BI_SE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SD_SE), a continuous measure of condition. Metric performance was assessed based on the strength (sensitivity) and shape (leading vs. lagging) of the relationship between spatial pattern metrics and these elevation-based measures. We observed significant logistic regression slopes with BI_SE for only 4 metrics (slough width, ridge density, directional connectivity index – DCI, and least flow cost – LFC). More significant relationships (n = 8 metrics) were observed with SD_SE, with the strongest associations for slough density, mean ridge width, and the average length of straight flow, as well as for a suite of hydrologic connectivity metrics (DCI, LFC and landscape discharge competence – LDC). Leading vs. lagging performance, inferred from the curvature of the association obtained from the exponent of fitted power functions, suggest that only DCI was a leading metric of the loss of soil elevation variation; most metrics were indeterminate, though some were clearly lagging. Our findings support the contention that soil elevation changes from altered peat accretion dynamics precede changes in landscape pattern, and offer insights that will enable efficient monitoring of the ridge-slough landscape as part of the ongoing Everglades restoration effort.     

How does dune morphology shape coastal EC habitats occurrence? A remote sensing approach using airborne LiDAR on the Mediterranean coast

We examined the relationship between coastal habitats (sensu European Union Habitats Directive) and local dune morphology along a Mediterranean coastal dune system by integrating field collected vegetation data and remotely sensed imagery. Specifically, we described the morphological profile of each EC habitat based on the morphological variables that are most likely to affect their occurrence, including elevation, slope, curvature, northness, eastness and sea distance. In addition, we assessed the role and strength of each morphological variable in determining the occurrence of EC habitats.We used 394 random vegetation plots representative of six EC habitats (Habitat 1210: “Annual vegetation of drift lines”; Habitat 2110: “Embryonic shifting dunes”; Habitat 2120: “Shifting dunes along the shoreline with Ammophila arenaria”; Habitat 2210 and 2230: “Crucianellion maritimae fixed beach dunes” and “Malcolmietalia dune grasslands”; Habitat 2250: “Coastal dunes with Juniperus spp.”; Habitat 2260: “Cisto-Lavanduletalia dune sclerophyllous scrubs”) found along the Tyrrhenian coast of central Italy. We derived each morphological variable from a DTM (Digital Terrain Model) obtained from 2-m resolution LiDAR (Light Detection And Range) images. The mean value of each variable was calculated at different spatial scales using buffer areas of increasing radius (2 m, 4 m, 8 m) around each vegetation plot. Mean morphological values for each EC habitat were compared using Kruskal-Wallis rank test. The role and strength of the relationship between habitat type and the morphological variables were assessed using Generalized Linear Models.EC habitats occur differentially across dune morphology, and the role and strength of each morphological variable define habitat specificity. Dune elevation and sea distance were determined to be the key factors in shaping EC habitat occurrence along this section of the Mediterranean coast. Identification of the close relationship between habitat type and morphological variables deriving from airborne LiDAR imagery points to the high potential of such remote sensing tool for analyzing and monitoring the integrity of coastal dune ecosystems. As airborne LiDAR enables the rapid collection of extremely accurate topographic data over large areas, it also offers useful information for the management of these threatened and fragile ecosystems.