Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Toxicity of sediment pore water in Puget Sound (Washington, USA): a review of spatial status and temporal trends

Data from toxicity tests of the pore water extracted from Puget Sound sediments were compiled from surveys conducted from 1997 to 2009. Tests were performed on 664 samples collected throughout all of the eight monitoring regions in the Sound, an area encompassing 2,294.1 km². Tests were performed with the gametes of the Pacific purple sea urchin, Strongylocentrotus purpuratus, to measure percent fertilization success as an indicator of relative sediment quality. Data were evaluated to determine the incidence, degree of response, geographic patterns, spatial extent, and temporal changes in toxicity. This is the first survey of this kind and magnitude in Puget Sound. In the initial round of surveys of the eight regions, 40 of 381 samples were toxic for an incidence of 10.5 %. Stations classified as toxic represented an estimated total of 107.1 km², equivalent to 4.7 % of the total area. Percent sea urchin fertilization ranged from >100 % of the nontoxic, negative controls to 0 %. Toxicity was most prevalent and pervasive in the industrialized harbors and lowest in the deep basins. Conditions were intermediate in deep-water passages, urban bays, and rural bays. A second round of testing in four regions and three selected urban bays was completed 5–10 years following the first round. The incidence and spatial extent of toxicity decreased in two of the regions and two of the bays and increased in the other two regions and the third bay; however, only the latter change was statistically significant. Both the incidence and spatial extent of toxicity were lower in the Sound than in most other US estuaries and marine bays.     

Estimation of late twentieth century land-cover change in California

We present the first comprehensive multi-temporal analysis of land-cover change for California across its major ecological regions and primary land-cover types. Recently completed satellite-based estimates of land-cover and land-use change information for large portions of the United States allow for consistent measurement and comparison across heterogeneous landscapes. Landsat data were employed within a pure-panel stratified one-stage cluster sample to estimate and characterize land-cover change for 1973?C2000. Results indicate anthropogenic and natural disturbances, such as forest cutting and fire, were the dominant changes, followed by large fluctuations between agriculture and rangelands. Contrary to common perception, agriculture remained relatively stable over the 27-year period with an estimated loss of 1.0% of agricultural land. The largest net declines occurred in the grasslands/shrubs class at 5,131 km² and forest class at 4,722 km². Developed lands increased by 37.6%, composing an estimated 4.2% of the state??s land cover by 2000.     

Environmental change monitoring in the arid and semi-arid regions: a case study Al-Basrah Province, Iraq

In recent years, land use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. This research utilizes the integrated remote sensing and geographic information systems (GIS) in the southern part of Iraq (Basrah Province was taken as a case) to monitor, map, and quantify the environmental change using a 1:250,000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation land, sand land, urban area, unused land, and water bodies. Supervised classification and normalized difference buildup index, normalized difference vegetation index, normalized difference bare land index, the normalized differential water index, crust index (CI) algorithms, and change detection techniques were adopted in this research and used, respectively, to retrieve its class boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. The results showed that the urban area, sand lands, and bare lands had increased by the rate of 1.2%, 0.8%, and 0.4% per year, with area expansion from 3,299.1, 4,119.1 km², and 3,201.9 km² in 1990 to 3,794.9, 4,557.7, and 3,351.7 km² in 2003, respectively. While the vegetation cover and water body classes were about 43.5% in 1990, the percentage decreased to about 39.6% in 2003. This study demonstrates the effectiveness of the remote sensing and GIS technologies in detecting, assessing, mapping, and monitoring the environmental changes.     

Monitoring an ecosystem at risk: What is the degree of grassland fragmentation in the Canadian Prairies?

Increasing fragmentation of grassland habitats by human activities is a major threat to biodiversity and landscape quality. Monitoring their degree of fragmentation has been identified as an urgent need. This study quantifies for the first time the current degree of grassland fragmentation in the Canadian Prairies using four fragmentation geometries (FGs) of increasing specificity (i.e. more restrictive grassland classification) and five types of reporting units (7 ecoregions, 50 census divisions, 1,166 municipalities, 17 sub-basins, and 108 watersheds). We evaluated the suitability of 11 datasets based on 8 suitability criteria and applied the effective mesh size (m _eff) method to quantify fragmentation. We recommend the combination of the Crop Inventory Mapping of the Prairies and the CanVec datasets as the most suitable for monitoring grassland fragmentation. The grassland area remaining amounts to 87,570.45 km² in FG4 (strict grassland definition) and 183,242.042 km² in FG1 (broad grassland definition), out of 461,503.97 km² (entire Prairie Ecozone area). The very low values of m _eff of 14.23 km² in FG4 and 25.44 km² in FG1 indicate an extremely high level of grassland fragmentation. The m _eff method is supported in this study as highly suitable and recommended for long-term monitoring of grasslands in the Canadian Prairies; it can help set measurable targets and/or limits for regions to guide management efforts and as a tool for performance review of protection efforts, for increasing awareness, and for guiding efforts to minimize grassland fragmentation. This approach can also be applied in other parts of the world and to other ecosystems.     

An assessment of the optimal scale for monitoring of MODIS and FIA NPP across the eastern USA

Robust monitoring of carbon sequestration by forests requires the use of multiple data sources analyzed at a common scale. To that end, model-based Moderate Resolution Imaging Spectroradiometer (MODIS) and field-based Forest Inventory and Analysis (FIA) data of net primary productivity (NPP) were compared at increasing levels of spatial aggregation across the eastern USA. A total of 52,167 FIA plots and colocated MODIS forest cover NPP pixels were analyzed using a hexagonal tiling system. A protocol was developed to assess the optimal scale as an optimal size of landscape patches at which to map spatially explicit estimates of MODIS and FIA NPP. The optimal mapping resolution (hereafter referred to as optimal scale) is determined using spatially scaled z-statistics as the tradeoff between increased spatial agreement as measured by Pearson’s correlation coefficient and decreased details of coverage as measured by the number of hexagons. Spatial sensitivity was also assessed using land cover assessment and forest homogeneity using spatially scaled z-statistics. Pearson correlations indicate that MODIS and FIA NPP are most highly correlated when using large hexagons, while z-statistics indicate an optimal scale at an intermediate hexagon size of 390 km². This optimal scale had more spatial detail than was obtained for larger hexagons and greater spatial agreement than was obtained for smaller hexagons. The z-statistics for land cover assessment and forest homogeneity also indicated an optimal scale of 390 km².     

Long-term chemical and biological improvement in an acid mine drainage-impacted watershed

Acid mine drainage (AMD) is a common result of coal and metal mining worldwide caused by weathering of metal sulfides exposed during mining. AMD typically results in low-pH, high-metal, high-conductivity water that does not support aquatic life. Chemical water quality improvement does not necessarily lead to rapid biological recovery. Little Raccoon Creek, a major tributary to Raccoon Creek in the Western Allegheny Plateau of Ohio, drains 401 km², has a legacy of AMD that stems from mining activities over more than a century. Since 1999, seven major passive treatments systems have been installed in the watershed to a total of over $6.5 million. This study analyzes the hourly water quality data collected at a United States Geological Survey gage station alongside trends in fish and macroinvertebrate communities. Both fish and macroinvertebrate communities have shown a statistically significant improvement in the lower reaches of Little Raccoon Creek since treatment began. Long-term chemical monitoring shows a significant increase in pH, but no significant change in conductivity. The conductivity data is well correlated with sulfate concentrations and discharge, while the pH is well correlated with net  alkalinity data, but not with discharge. Significant investment in passive treatment systems and land reclamation has decreased the percent occurrence of pH measurements below the target of 6.5 and has led to recovery of both fish and macroinvertebrate communities in the downstream reaches of Little Raccoon Creek. Long-term monitoring has proven to be a valuable tool to assess success of a high-cost remediation program.     

Habitat monitoring and conservation prioritisation of protected areas in Western Ghats,Kerala, India

Spatially explicit approach is essential to prioritise the ecosystems for biodiversity conservation. In the present study, the conservation status of 20 protected areas of the Western Ghats of Kerala, India, was analysed based on long-term changes in forests (1975–1985–1995–2005–2013), landscape level changes in fragmentation and forest fires (2005–2015). This study has shown that a significant forest loss occurred in protected areas before declaration. Idukki is one of the major protected areas which showed a drastic reduction (18.83%) in its forest cover. During 1985–1995, Periyar tiger reserve had lost 24.19 km² core 3 forest area followed by Peppara (18.54 km²), Parambikulam (17.93 km²), Chimmony (17.71 km²), Peechi-Vazhani (12.31 km²) and Neyyar (11.67 km²). An area of 71.33 km² of the protected area was affected by fires in 2014. Overall protected area-wise decadal analysis indicates Periyar has the highest number of fire incidences followed by Wayanad, Kurinjimala, Silent Valley and Eravikulam. Disturbances in the form of fires and fragmentation still exist and may have significant conservation threat to flora and fauna. Among protected areas, many are having a probability to go under threat or dynamic stage. Chinnar, Thattekkad and Kurinjimala sanctuaries are representing high levels of vulnerability, or they are near to decline stage. Habitat level monitoring of the anthropogenic disturbances can be efficiently useful for the strategic conservation planning. The present study has provided geospatial database on spatial patterns of deforestation, fragmentation and forest fires in protected areas of Kerala. Conservation prioritization approach based on these parameters will be useful for the strategic planning in the state of Kerala.     

Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve,India using remote sensing and GIS

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest resource management. There is no comprehensive data existing on forest fires on a regular basis in Biosphere Reserves of India. The present work have been carried out to locate and estimate the spatial extent of forest burnt areas using Resourcesat-1 data and fire frequency covering decadal fire events (2004–2013) in Similipal Biosphere Reserve. The anomalous quantity of forest burnt area was recorded during 2009 as 1,014.7 km². There was inconsistency in the fire susceptibility across the different vegetation types. The spatial analysis of burnt area shows that an area of 34.2 % of dry deciduous forests, followed by tree savannah, shrub savannah, and grasslands affected by fires in 2013. The analysis based on decadal time scale satellite data reveals that an area of 2,175.9 km² (59.6 % of total vegetation cover) has been affected by varied rate of frequency of forest fires. Fire density pattern indicates low count of burnt area patches in 2013 estimated at 1,017 and high count at 1,916 in 2004. An estimate of fire risk area over a decade identifies 12.2 km² is experiencing an annual fire damage. Summing the fire frequency data across the grids (each 1 km²) indicates 1,211 (26 %) grids are having very high disturbance regimes due to repeated fires in all the 10 years, followed by 711 grids in 9 years and 418 in 8 years and 382 in 7 years. The spatial database offers excellent opportunities to understand the ecological impact of fires on biodiversity and is helpful in formulating conservation action plans.     

Ecosystem evaluation (1989–2012) of Ramsar wetland Deepor Beel using satellite-derived indices

The unprecedented urban growth especially in developing countries has laid immense pressure on wetlands, finally threatening their existence altogether. A long-term monitoring of wetland ecosystems is the basis of planning conservation measures for a sustainable development. Deepor Beel, a Ramsar wetland and major storm water basin of the River Brahmaputra in the northeastern region of India, needs particular attention due to its constant degradation over the past decades. A rule-based classification algorithm was developed using Landsat (2011)-derived indices, namely Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Normalised Difference Pond Index (NDPI), Normalised Difference Vegetation Index (NDVI) and field data as ancillary information. Field data, ALOS AVNIR and Google Earth images were used for accuracy assessment. A fuzzy accuracy assessment of the classified data sets showed an overall accuracy of 82 % for MAX criteria and 90 % for RIGHT criteria. The rules were used to classify major wetland cover types during low water season (January) in 1989, 2001 and 2012. The statistical analysis of the classified wetland showed heavy manifestation in aquatic vegetation and other features indicating severe eutrophication over the past 23 years. This degradation was closely related to major contributing anthropogenic factors, such as a railway line construction, growing croplands, waste disposal and illegal human settlements in the wetland catchment. In addition, the landscape development index (LDI) indicated a rapid increase in the impact of the surrounding land use on the wetland from 1989 to 2012. The techniques and results from this study may prove useful for top-down landscape analyses of this and other freshwater wetlands.     

Sampling for landscape elements—a case study from Lower Saxony,Germany

The estimation of coverage, i.e., the proportion of the total area in a study region covered by a given target class, is essential to many aspects of environmental monitoring. We analyze and compare the efficiency of different sample-based approaches for the estimation of coverage of different land cover classes from aerial imagery in a case study in Lower Saxony, Germany on the basis of the estimated standard errors. A complete delineation of vegetation classes in n?=?279 aerial photo plots of 400 × 400 m thrown onto the study region of 1,117.7 km² in accordance with a systematic grid is compared to different configurations of line intercept sampling and clusters of points. The observation designs under study are characterized by different complexity and total size of the observation units and therefore also to the efforts related to yield a single observation. Especially for those classes that cover a relatively large proportion of the sampling frame, our results show that difference in performance between the different designs are negligible. A cluster of four transects of 200 m each allows estimating the area of land cover classes with high coverage with nearly similar precision as a complete mapping of fixed area plots of 16 ha each. Clusters of points show unexpected high precision for the estimated coverage of land cover classes with relatively high coverage.     

Enhanced semipermanent dialysis samplers for long-term environmental monitoring in saturated sediments

The ability to sample in situ natural environmental processes has proven to be challenging when working with redox-sensitive contaminates in saturated sediments in wetland systems, especially within the rhizosphere, where sharp redox gradients are common. Many traditional approaches are invasive and disturb natural sediment chemistry. Through laboratory and field studies, the work presented in this study demonstrates a novel semipermanent dialysis sampler that allows for long-term, anaerobic monitoring of shallow sediments. Dialysis samplers were deployed and tested for over 1 year while being exposed to extremes in climate. These newly designed devices produce statistically reproducible data and capture sensitive redox trends. Results from the newly designed samplers were compared to conventional samplers. Initially, both the new and old designs yielded statistically similar data, but these data diverged over a period of months. The new devices are less invasive, so data gathered from these devices are more likely to be a closer representation of true conditions in the subsurface. By giving reliable data from a consistent location in space, these new samplers represent a significant step forward in capturing spatial and temporal variability in wetland redox chemistry during long-term monitoring.     

Mapping Invasive Aquatic Vegetation in the Sacramento-San Joaquin Delta using Hyperspectral Imagery

The ecological and economic impacts associated with invasive species are of critical concern to land managers. The ability to map the extent and severity of invasions would be a valuable contribution to management decisions relating to control and monitoring efforts. We investigated the use of hyperspectral imagery for mapping invasive aquatic plant species in the Sacramento-San Joaquin Delta in the Central Valley of California, at two spatial scales. Sixty-four flightlines of HyMap hyperspectral imagery were acquired over the study region covering an area of 2,139 km² and field work was conducted to acquire GPS locations of target invasive species. We used spectral mixture analysis to classify two target invasive species; Brazilian waterweed (Egeria densa), a submerged invasive, and water hyacinth (Eichhornia crassipes), a floating emergent invasive. At the relatively fine spatial scale for five sites within the Delta (average size 51 ha) average classification accuracies were 93% for Brazilian waterweed and 73% for water hyacinth. However, at the coarser, Delta-wide scale (177,000 ha) these accuracy results were 29% for Brazilian waterweed and 65% for water hyacinth. The difference in accuracy is likely accounted for by the broad range in water turbidity and tide heights encountered across the Delta. These findings illustrate that hyperspectral imagery is a promising tool for discriminating target invasive species within the Sacramento-San Joaquin Delta waterways although more work is needed to develop classification tools that function under changing environmental conditions.     

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches—object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)—for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25?×?25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50?×?50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.     

Long-term spatiotemporal patterns of CH4 and N2O emissions from livestock and poultry production in Turkey

This study quantified spatiotemporal patterns of CH₄ and N₂O emissions from livestock and poultry production in Turkey between 1961 and 2007. CH_4(enteric) (from enteric fermentation), CH_4(manure) (from manure management), and N₂O_(AWM) (from animal waste management) emissions in Turkey were estimated at 1,164, 216, and 55 Gg in 1961 and decreased to 844, 187, and 39 Gg in 2007, contributing a share of roughly 2% to the global livestock-related CH₄ emissions and %1.5 to the global N₂O_(AWM) emissions, respectively. Total CO₂-eq emissions were estimated at 50.7 Tg in 1961 and declined from a maximum value of 60.7 Tg in 1982 to a minimum value of 34.5 Tg in 2003, with a mean emission rate of 48 Tg year^???1 due to a significant reduction in the number of ruminant livestock. The highest mean share of emissions belonged to West Black Sea (14% and 16%) for CH_4(enteric) and CH_4(manure) and to North East Anatolia (12% and %13) for N₂O_(AWM) and total CO₂-eq emissions, respectively. The highest emission density was 1.7 Mg km^???2 year^???1 for CH_4(enteric), 0.3 Mg km^???2 year^???1 for CH_4(manure), and 0.07 Mg km^???2 year^???1 for the total CO₂-eq emissions in the West and North East Anatolia regions and 0.09 Mg km^???2 year^???1 for N₂O_(AWM) in the East Marmara region. Temporal and spatial variations in CH_4(enteric), CH_4(manure), and N₂O_(AWM) emissions in Turkey were estimated using regression models and ordinary kriging at a 500-m resolution, respectively.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Compositing climate change vulnerability of a Mediterranean region using spatiotemporally dynamic proxies for ecological and socioeconomic impacts and stabilities

The study presents a new methodology to quantify spatiotemporal dynamics of climate change vulnerability at a regional scale adopting a new conceptual model of vulnerability as a function of climate change impacts, ecological stability, and socioeconomic stability. Spatiotemporal trends of equally weighted proxy variables for the three vulnerability components were generated to develop a composite climate change vulnerability index (CCVI) for a Mediterranean region of Turkey combining Landsat time series data, digital elevation model (DEM)-derived data, ordinary kriging, and geographical information system. Climate change impact was based on spatiotemporal trends of August land surface temperature (LST) between 1987 and 2016. Ecological stability was based on DEM, slope, aspect, and spatiotemporal trends of normalized difference vegetation index (NDVI), while socioeconomic stability was quantified as a function of spatiotemporal trends of land cover, population density, per capita gross domestic product, and illiteracy. The zones ranked on the five classes of no-to-extreme vulnerability were identified where highly and moderately vulnerable lands covered 0.02% (12 km²) and 11.8% (6374 km²) of the study region, respectively, mostly occurring in the interior central part. The adoption of this composite CCVI approach is expected to lead to spatiotemporally dynamic policy recommendations towards sustainability and tailor preventive and mitigative measures to locally specific characteristics of coupled ecological–socioeconomic systems.     

A study on the phytoaccumulation of waste elements in wetland plants of a Ramsar site in India

Some wetland plant species are adapted to growing in the areas of higher metal concentrations. Use of such vegetation in remediation of soil and water contaminated with heavy metals is a promising cost-effective alternative to the more established treatment methods. Throughout the year, composite industrial effluents bringing various kinds of heavy metals contaminate our study site, the East Calcutta Wetlands, a Ramsar site at the eastern fringe of Kolkata city (formerly Calcutta), India. In the present study, possible measures for remediation of contaminated soil and water (with elements namely, Ca, Cr, Cu, Pb, Zn, Mn, and Fe) of the ecosystem had been investigated. Ten common regional wetland plant species were selected to study their efficiency and diversity in metal uptake and accumulation. Results showed that Bermuda grass (Cynodon dactylon) had the highest total Cr concentration (6,601 ± 33 mg kg^???1 dw). The extent of accumulation of various elements in ten common wetland plants of the study sites was: Pb (4.4?C57 mg kg^???1 dw), Cu (6.2?C39 mg kg^???1 dw), Zn (59?C364 mg kg^???1 dw), Mn (87?C376 mg kg^???1 dw), Fe (188?C8,625 mg kg^???1 dw), Ca (969?C3,756 mg kg^???1 dw), and Cr (27?C660 mg kg^???1 dw) indicating an uptake gradient of elements by plants as Ca>Fe>Mn>Cr>Zn>Cu>Pb. The present study indicates the importance of identification and efficiency of metal uptake and accumulation capabilities by plants in relation to their applications in remediation of a contaminated East Calcutta Wetland ecosystem.     

Determining the size of a complete disturbance landscape: multi-scale,continental analysis of forest change

The scale of investigation for disturbance-influenced processes plays a critical role in theoretical assumptions about stability, variance, and equilibrium, as well as conservation reserve and long-term monitoring program design. Critical consideration of scale is required for robust planning designs, especially when anticipating future disturbances whose exact locations are unknown. This research quantified disturbance proportion and pattern (as contagion) at multiple scales across North America. This pattern of scale-associated variability can guide selection of study and management extents, for example, to minimize variance (measured as standard deviation) between any landscapes within an ecoregion. We identified the proportion and pattern of forest disturbance (30 m grain size) across multiple landscape extents up to 180 km². We explored the variance in proportion of disturbed area and the pattern of that disturbance between landscapes (within an ecoregion) as a function of the landscape extent. In many ecoregions, variance between landscapes within an ecoregion was minimal at broad landscape extents (low standard deviation). Gap-dominated regions showed the least variance, while fire-dominated showed the largest. Intensively managed ecoregions displayed unique patterns. A majority of the ecoregions showed low variance between landscapes at some scale, indicating an appropriate extent for incorporating natural regimes and unknown future disturbances was identified. The quantification of the scales of disturbance at the ecoregion level provides guidance for individuals interested in anticipating future disturbances which will occur in unknown spatial locations. Information on the extents required to incorporate disturbance patterns into planning is crucial for that process.     

Statistically Extracted Fundamental Watershed Variables for Estimating the Loads of Total Nitrogen in Small Streams

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km²) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.