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.     

The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method

Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low-relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0–40 mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude (Pearson’s r between root-mean squared error of prediction and rainfall magnitude = −0.943 for percentage of sand and −0.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 20 mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying.     

Effects of yak dung patch dropped in cold season on soil and pasture on the Qinghai-Tibetan Plateau

To clarify how dung patches from grazing yaks affect soil and pasture in the alpine meadow of Qinghai-Tibetan Plateau, yak dung was collected, mixed and redistributed in a cold grazing season. The soil physical and chemical properties and forage growth were then monitored under the yak dung patch, and 10 cm and 50 cm from the edge of yak dung patches. The result has shown that yak dung significantly improved soil moisture, total organic matter, and soil available N and P under or close to the dung patches. The forage production at 10 cm from the dung patch (303 g/m²) was significantly higher than that at 50 cm from the dung patch (control) (284 g/m²) in the second year, while the production was similar to the control in the first and the third year. The process of yak dung decomposition was slow and yak dung remains were observed 3 years after the drop. The dung patches also formed a strong ‘shell’, very difficult for plant underneath to penetrate and grow. Therefore, almost all plants under yak dung patches died, leading to decline in forage yield in the first, second, and the third year. In practice in the Qinghai-Tibetan Plateau regions, yak dung is often collected as fuel by the local farmers. Removing yak dung from alpine meadow may on one hand lead to losses in soil nutrients, but on the other hand reduces some of the negative effects, e.g. the reduction of forage yield under yak dung patches.     

New judgement on the source of soil water in extremely dry zone

There is always a relatively high moisture content in the shallow soil layer in Gobi area on the top of the Mogao Grottoes in the extremely dry zone. This paper gives out a new judgement on soil water source through field experiments using the isolated water method, simulated rainfall method and greenhouse method. Under the condition of typical hyper-arid climate far away from rainfall effect and after cutting off the connection of soil and groundwater the simulated rainfall experiment was conducted, through putting up a plastic greenhouse to obtain a relatively closed space and extracting the condensed water on the film surface, the soil water output from the greenhouse was monitored. The experimental results show that generally the rainfall can be completely evaporated in about 20 days under the isolated condition. In the relatively closed space sheltered by arched greenhouse in the Gobi area water can be continuously transferred outward from subsoil and condensed on the roof film, with a daily output quantity of 3–5 g/m²d. After prolonged output soil water content remains significantly higher than that of the control and before covering by arched shed. Comprehensively judging from this, groundwater is an important source of soil water in addition to precipitation. The new judgement has a very important practical significance to the water research of the groundwater–soil–plant-atmosphere circle (GSPAC) and the recovery of the desertified environment.     

Capacities of soil water reservoirs and their better regression models by combining “merged groups PCA” in Chongqing,China

Soil water resource, together with the surface and sub-surface water resource, is essential to the regional water balance and world water cycle. A total of 90 soil samples were collected from 30 different soil profiles of dry fields throughout Chongqing, China randomly to show how soil could be a crucial part of water resources by discussing their five types of calculated soil water reservoir capacities, namely the total soil water reservoir capacity (mm) (TC), soil water storage capacity (mm) (SC), unavailable soil water reservoir capacity (mm) (UC), available soil water reservoir capacity (mm) (AC), and soil dead water storage capacity (mm) (DC) in certain layer, respectively. Overall, the total soil water reservoir capacity in 0–40 cm was about 209 mm, of which 70 mm belonged to available soil water reservoir capacity. Not all the five types of soil water reservoir capacities had significant correlations between each other. Soil structure, especially the size and quantity of soil pore was mainly determined by soil particle composition (clay, silt, and sand content). The more sand and less clay led to the more soil macropores, which provided room for soil water. Thus, clay, silt, and sand content jointly produced profound influence on soil water reservoir capacities. Nevertheless, specific water capacity and topographic factors displayed weak correlations to soil water reservoir capacities, which required further research works. Ultimately, the better regression models were achieved by multiple regression analysis coupled with “merged groups PCA” than by multiple regression analysis with “all variables PCA”. UC, SC, TC and DC could be well simulated (mostly R² > 0.70; P < 0.05) through normal multiple regression analysis using original variables as well as multiple regression analysis with “merged groups PCA”. Only regression models of TC and DC were highly significant (mostly R² > 0.70; P < 0.05) through “all variables PCA” method. And there were poor coefficients of determination (R²) for AC (mostly R² < 0.40; P < 0.05) by all the three regression methods.     

Seed rain and seed bank of Chinese yew (Taxus chinensis var. mairei) population in Tianmu Mountain☆

Seed rain and seed bank of a Chinese yew (Taxus chinensis var. mairei) population in Tianmu Mountain were researched in 2008 and 2009. The seed rain lasted from 16th–23th of October to 5th–14th of December, and the heaviest seed falling period was from 2nd to18th of November. The intensity of seed rain showed a great inter-annual variation, with a good harvest in 2008. The fallen seeds were composed of 49.9% proportion of immature seed, 33.8% proportion of chewed seed and 16.3% proportion of mature seed. The analysis on the soil seed bank under mother forest showed that the number of intact seeds was 122.75 ± 108.08 grain/m² in October, 279.25 ± 210.73 grain/m² in December 2008, and 166.5 ± 165.34 grain/m² in October, 322.5 ± 275.73 grain/m² in December 2009. The increased number of seed was 156.5 ± 222.723 grain/m² in 2008 and 156 ± 275grain/m² in 2009, which showed a significant variation. Large number of intact seeds added into soil seed bank after seed rain each year. The number of intact seeds in soil seed bank decreased 112.75 ± 47.74 grain/m² from December 2008 to October 2009. Large number of intact seeds lost from seed rot and seed predation by animals. The number of seeds in soil bank under bamboo forest was much lower than that of mother tree forest, and the increased number of seeds was 0.63 ± 1.60 grain/m² in 2008 and 2.88 ± 1.86 grain/m² in 2009. The number of seedling was 0.73 ± 1.10 trees/m² in mother tree forest and 0.09 ± 0.35 trees/m² in bamboo forest. Seedling survival ratio was 0.37% in mother tree forest and 10.23% in bamboo forest. The micro-habitat in bamboo forest was fit for seed germination. Birds transported seeds to bamboo forest, and had an important effect on the regeneration of Chinese yew.     

Mapping changes to vegetation pattern in a restoring wetland: Finding pattern metrics that are consistent across spatial scale and time

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process. Pattern metrics, derived from classified remotely sensed imagery, have been used to measure composition and configuration of patches and landscapes, but they can be unpredictable across scales, and inconsistent across time. We sought to identify pattern metrics that are consistent across spatial scale and time – and thus robust measures of vegetation and habitat configuration – for a restored tidal marsh in the San Francisco Bay, CA, USA. We used high-resolution (20 cm) remotely sensed color infrared imagery to map vegetation pattern over 2 years, and performed a multi-scale analysis of derived vegetation pattern metrics. We looked at the influence on metrics of changes in grain size through resampling and changes in minimum mapping unit (MMU) through smoothing. We examined composition, complexity, connectivity and heterogeneity metrics, focusing on perennial pickleweed (Sarcocornia pacifica), a dominant marsh plant. At our site, pickleweed patches grew larger, more irregularly shaped, and closely spaced over time, while the overall landscape became more diverse. Of the two scale factors examined, grain size was more consistent than MMU in terms of identifying relative change in composition and configuration of wetland marsh vegetation over time. Most metrics exhibited unstable behavior with larger MMUs. With small MMUs, most metrics were consistent across grain sizes, from fine (e.g. 0.16 m²) to relatively large (e.g. 16 m²) pixel sizes. Scale relationships were more variable at the landcover class level than at the landscape level (across all classes). This information may be useful to applied restoration practitioners, and adds to our general understanding of vegetation change in a restoring marsh.     

Distance models in ecological network management: A case study of patch connectivity in a grassland network

Landscape connectivity is a key issue of nature conservation and distance parameters are essential for the calculation of patch level metrics. For such calculations the so-called Euclidean and the least cost distance are the most widespread models. In the present work we tested both distance models for landscape connectivity, using connectivity metrics in the case of a grassland mosaic, and the ground beetle Pterostichus melas as a focal species. Our goal was to explore the dissimilarity between the two distance models and the consequent divergence from the calculated values of patch relevance in connectivity. We found that the two distance models calculated the distances similarly, but their estimations were more reliable over short distances (circa 500 m), than long distances (circa 3000 m). The variability in the importance of habitat patches (i.e. patch connectivity indices) was estimated by the difference between the two distance models (Euclidean vs. least cost) according to the patch size. The location of the habitat patches in the matrix seemed to be a more important factor than the habitat size in the estimation of connectivity. The uncertainty of three patch connectivity indices (Integral Index of Connectivity, Probability of Connectance and Flux) became high above a habitat size of 5 ha. Relevance of patches in maintaining connectivity varied even within small ranges depending on the estimator of distance, revealing the careful consideration of these methods in conservation planning.     

Impact of landscape pattern at multiple spatial scales on water quality: A case study in a typical urbanised watershed in China

Buffer zones along rivers and streams can provide water quality services by filtering nutrients, sediment and other contaminants from the surface. Redundancy analysis was used to determine the influence of the landscape pattern at the entire catchment scale and at multiple buffer zone scales (100 m, 300 m, 500 m, 1000 m and 1500 m) on the water quality in a highly urbanised watershed. Change-point analysis was further applied to estimate the specific locations along a gradient of landscape metric that result in a sudden change in the water quality variable. The landscape characteristics for 100 m buffer zones appeared to have a slightly greater influence on the water quality than the entire catchment. The patch density of urban land and the large patch index of water were recognised as the dominant variables influencing the water quality for a 100 m buffer zone. The result of change-point analysis indicated key interval values of the two landscape metrics within the 100 m buffer zone. When the patch density of urban land was >30–40 n/100 ha and the largest patch index of water was >2.5–3.5%, the watershed water quality appeared to be better protected.     

Integrating geo-biodiversity features in the analysis of landscape patterns

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how specific physical and biological factors, namely geo- and biodiversity features influence landscape patterns. The focus was on microscale relationships and we chose as our focus area a small scale study site covering 3091 ha characterized by vegetation mosaics with multiple patterns. We considered geology, soil and altitude (for geodiversity) and land cover classes (for biodiversity) as superposed layers and we aggregated their elements into a new combined mosaic. Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses. We determined the most important parameters by Principal Component Analysis. The first component was mainly linked to metrics related to size variability, while the second one was related to border complexity. In the reduced space, we delineated three clusters of objects that were characterized by different landscape patterns. We analyzed the underlying geology, soil structure and occurring land cover classes for each cluster. We then performed Redundancy Analysis using geo- and biodiversity features as predictor variables and metrics as response variables. While the land cover acted as explanatory variable for the first principal axis of variation, the geodiversity features (geology and soil) were related to the second one. Specifically, the occurrence of limestone yields more complex borders of patches; some phenomena are visible in situ, such as limestone appearing at the surface as outcrops (lapis) that induce irregular shapes of the patches. Overall, the analyses hinted that, besides the land cover class, the underlying geology plays an important role in defining landscape patterns, and this relationship can be revealed through the use of appropriate statistical tools. On the other hand, the study area is an agro-silvopastoral landscape, where local traditional management practices are also an important driver for the occurrence of specific patterns. Therefore, understanding the links between geo- and biodiversity characteristics and landscape features can contribute to developing appropriate management and planning strategies.     

Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set

Little information is available to assess the dynamic changes in wetland soil quality in coastal regions, though it is essential for wetland conservation and management. Soil samples were collected in Suaeda salsa wetlands (SWs), Tamarix chinensis wetlands (TWs), Suaeda salsa–Tamarix chinensis wetlands (STWs), freshwater Phragmites australis wetlands (FPWs) and saltwater Phragmites australis wetlands (SPWs) in three sampling periods (i.e., summer and autumn of 2007 and spring of 2008). According to the flooding characteristics of these wetlands, the study area could be grouped into three sub-regions: short-term flooding region (STFR), seasonal flooding region (SFR) and tidal flooding region (TFR). Soil quality was evaluated using the soil quality index (SQI), which was calculated using the selected minimum data set (MDS) based on principal components analysis (PCA). Our results showed that soil salt content (SSC), total carbon (TC), magnesium (Mg), nitrate nitrogen (NO₃⁻-N) and total sulfur (TS) consisted of a MDS among 13 soil properties. The SQI values varied from 0.18 to 0.66 for all soil samples, of which the highest and lowest SQI values were observed in TFR. The average SQI values were significantly higher in summer (0.50 ± 0.13) than in spring (0.37 ± 0.13) and autumn (0.36 ± 0.11) in the whole study area (p < 0.05). The average SQI values followed the order STFR (0.44 ± 0.12) > TFR (0.41 ± 0.15) > SFR (0.35 ± 0.09) although no significant differences were observed among the three regions (p > 0.05). SPWs and SWs soils showed higher SQI values (0.50 ± 0.10 and 0.47 ± 0.15, respectively) than TWs (0.30 ± 0.08) soils (p < 0.05). The SSC was the dominant factor of soil quality with its proportion of 34.1% contributing to the SQI values, followed by TC (24.5%) and Mg (24.1%). Correlation analysis also showed that SQI values were significantly negatively correlated with SSC. SSC might be a characteristic indicator of wetland soil quality assessment in coastal regions. The findings of this study showed that the SQI based on MDS is a powerful tool for wetland soil quality assessment.     

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.     

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.     

A review of indicators of estuarine tidal wetland condition

This review critically evaluates indicators of tidal wetland condition based on 36 indicator development studies and indicators developed as part of U.S. state tidal wetland monitoring programs. Individual metrics were evaluated based on relative scores on two sets of evaluation factors. A rigor score evaluated metric development based on conceptual relevance, indicator development method, degree of independent validation, and temporal and spatial extent tested. An applicability score evaluated metrics based on cost of data collection, probable spatial extent of applicability, technical complexity, and indicator responsiveness. The majority of indicators could be classified as biotic condition indicators (81%), with vegetation (37%) and macroinvertebrate (28%) metrics composing the largest proportion. Most metrics provided a conceptual model or scientific justification (97%), were developed by correlation to environmental gradients (46%), were tested over multiple seasons or years (49%) and at multiple sites (88%). Few were independently validated (18%). Average rigor score was 10 (on a scale of 0–25) and ranged between 1 and 21. Highest rigor scores were for trematode community metrics (community similarity index, species richness) and metrics of grass shrimp (Palaemonetes pugio) individuals (gene expression, relative fecundity, embryo hatching success, larval survival). Most metrics had a high cost of data collection (63%), required field and laboratory processing (84%), would be applicable across the U.S. (72%), and were responsive to the variable of interest (44%). Mean applicability score was 4.9 (range: 2–8). Highest scores were found for metrics that only required field collection of data using simple or no instrumentation. Lowest scoring metrics required expensive equipment, specialized taxonomic knowledge, complex laboratory analysis, and/or culturing of organisms. Scores for individual metrics were grouped by indicator, then averaged and rescaled between 0 and 100 to provide a composite evaluation of the indicator they measured. Among major indicator types, biotic indicators had the highest rigor scores (mean = 44, range 20–79), followed by indicators of chemical/physical characteristics (mean = 36, range 16–56), landscape condition (mean = 31, range 24–37), and hydrology/geomorphology indicators (mean = 21, range 4–52). In contrast, biotic indicators scored lowest for applicability (mean = 58, range 25–100) and indicators of landscape condition scored highest. The results of this review suggest that the development and selection of tidal wetland indicators could be vastly improved by employing a standardized development methodology that provides uniform information about each indicator. In addition, tidal wetland indicator research should focus on the development of indicators of ecological processes and disturbance regimes.     

Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity

Characterization of soil properties is a key step in understanding the source of spatial variability in the productivity across agricultural fields. A study on a 16 ha field located in the eastern region of Saudi Arabia was undertaken to investigate the spatial variability of selected soil properties, such as soil compaction ‘SC’, electrical conductivity ‘EC’, pH (acidity or alkalinity of soil) and soil texture and its impact on the productivity of Rhodes grass (Chloris gayana L.). The productivity of Rhodes grass was investigated using the Cumulative Normalized Difference Vegetation Index (CNDVI), which was determined from Landsat-8 (OLI) images. The statistical analysis showed high spatial variability across the experimental field based on SC, clay and silt; indicated by values of the coefficient of variation (CV) of 22.08%, 21.89% and 21.02%, respectively. However, low to very low variability was observed for soil EC, sand and pH; with CV values of 13.94%, 7.20% and 0.53%, respectively. Results of the CNDVI of two successive harvests showed a relatively similar trend of Rhodes grass productivity across the experimental area (r = 0.74, p = 0.0001). Soil physicochemical layers of a considerable spatial variability (SC, clay, silt and EC) were utilized to delineate the experimental field into three management zones (MZ-1, MZ-2 and MZ-3); which covered 30.23%, 33.85% and 35.92% of the total area, respectively. The results of CNDVI indicated that the MZ-1 was the most productive zone, as its major areas of 50.28% and 45.09% were occupied by the highest CNDVI classes of 0.97–1.08 and 4.26–4.72, for the first and second harvests, respectively.     

Microhabitat variations and seed bank-vegetation relationships in a desert wadi ecosystem

Due to extreme variability in patterns of rainfall, plant seed banks are an important component of desert habitats. Here I report on effects of standing vegetation and three different microhabitats (channel, bank and terrace) on the soil seed bank of a desert wadi ecosystem in the Eastern Desert of Egypt. A total of 450 soil samples at 45 stands were collected to represent the different wadi microhabitats. The germinable seed bank was estimated by controlled counts of seedling emergence. The floristic composition, functional properties and diversity of the soil seed bank, as well as its similarity with the standing vegetation varied among wadi microhabitats. Such variation could be attributed to differences in disturbance intensity among microhabitats (terrace < bank < channel) and variation of soil factors along the microtopographic gradient. Channel showed the highest species richness and size of soil seed bank, followed by bank and then terrace. Moreover the Shannon index of diversity of the seed bank and its similarity with standing vegetation were significantly greater in both channel and bank microhabitats than in terrace. At the level of plant functional groups, number of seeds of annuals was higher in both channel and bank than in terrace. Shrubs were more abundant in seed banks of channel compared to terrace. The size and species richness of seed bank were increased with the total plant cover, annual/perennial ratio and species richness of the standing vegetation.     

Environmental indicators for estimating the potential soil respiration rate in alpine zone

Soil respiration is the main form of carbon flux from soil to atmosphere in the global carbon cycle. The effect of temperature on soil respiration rate is important in evaluating the potential feedback of soil organic carbon to global warming. We incubated soils from the alpine meadow zone and upper rocky zone along an altitudinal gradient (4400–5500 m a.s.l.) on the Tibetan Plateau under various temperature and soil moisture conditions. We evaluated the potential effects of temperature and soil moisture on soil respiration and its variation across altitudes. Soil respiration rates increased as the temperature increased. At 60% of soil water content, they averaged 0.21–5.33 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.11–0.50 μmol g soil⁻¹ day⁻¹ in the rocky zone over the experimental temperature range. Soil respiration rates in the rocky zone did not increase between 25 and 35 °C, probably because of heat stress. Rates of decomposition of organic matter were high in the rocky zone, where the CN ratio was smaller than in the middle altitudes. Soil respiration rates also increased with increasing soil water content from 10% to 80% at 15 °C, averaging 0.04–2.00 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.03–0.35 μmol g soil⁻¹ day⁻¹ in the rocky zone. Maximum respiration rates were obtained in the middle part of the alpine slope in any case of experimental temperature and soil moisture. The change patterns in soil respiration rate along altitude showed similar change pattern in soil carbon content. Although the altitude is a variable including various environmental factors, it might be used as a surrogate parameter of soil carbon content in alpine zone. Results suggest that temperature, soil moisture and altitude are used as appropriate environmental indicators for estimating the spatial distribution of potential soil respiration in alpine zone.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

AnimalFinder: A semi-automated system for animal detection in time-lapse camera trap images

Although the use of camera traps in wildlife management is well established, technologies to automate image processing have been much slower in development, despite their potential to drastically reduce personnel time and cost required to review photos. We developed AnimalFinder in MATLAB® to identify animal presence in time-lapse camera trap images by comparing individual photos to all images contained within the subset of images (i.e. photos from the same survey and site), with some manual processing required to remove false positives and collect other relevant data (species, sex, etc.). We tested AnimalFinder on a set of camera trap images and compared the presence/absence results with manual-only review with white-tailed deer (Odocoileus virginianus), wild pigs (Sus scrofa), and raccoons (Procyon lotor). We compared abundance estimates, model rankings, and coefficient estimates of detection and abundance for white-tailed deer using N-mixture models. AnimalFinder performance varied depending on a threshold value that affects program sensitivity to frequently occurring pixels in a series of images. Higher threshold values led to fewer false negatives (missed deer images) but increased manual processing time, but even at the highest threshold value, the program reduced the images requiring manual review by ~ 40% and correctly identified > 90% of deer, raccoon, and wild pig images. Estimates of white-tailed deer were similar between AnimalFinder and the manual-only method (~ 1–2 deer difference, depending on the model), as were model rankings and coefficient estimates. Our results show that the program significantly reduced data processing time and may increase efficiency of camera trapping surveys.