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.     

Geospatial assessment and monitoring of historical forest cover changes (1920–2012) in Nilgiri Biosphere Reserve,Western Ghats,India

Deforestation in the biosphere reserves, which are key Protected Areas has negative impacts on biodiversity, climate, carbon fluxes and livelihoods. Comprehensive study of deforestation in biosphere reserves is required to assess the impact of the management effectiveness. This article assesses the changes in forest cover in various zones and protected areas of Nilgiri Biosphere Reserve, the first declared biosphere reserve in India which forms part of Western Ghats-a global biodiversity hotspot. In this study, we have mapped the forests from earliest available topographical maps and multi-temporal satellite data spanning from 1920’s to 2012 period. Mapping of spatial extent of forest cover, vegetation types and land cover was carried out using visual interpretation technique. A grid cell of 1 km?×?1 km was generated for time series change analysis to understand the patterns in spatial distribution of forest cover (1920–1973–1989–1999–2006–2012). The total forest area of biosphere reserve was found to be 5,806.5 km² (93.8 % of total geographical area) in 1920. Overall loss of forest cover was estimated as 1,423.6 km² (24.5 % of the total forest) with reference to 1920. Among the six Protected Areas, annual deforestation rate of >0.5 was found in Wayanad wildlife sanctuary during 1920–1973. The deforestation in Nilgiri Biosphere Reserve is mainly attributed to conversion of forests to plantations and agriculture along with submergence due to construction of dams during 1920 to 1989. Grid wise analysis indicates that 851 grids have undergone large-scale negative changes of >75 ha of forest loss during 1920–1973 while, only 15 grids have shown >75 ha loss during 1973–1989. Annual net rate of deforestation for the period of 1920 to 1973 was calculated as 0.5 followed by 0.1 for 1973 to 1989. Our analysis shows that there was large-scale deforestation before the declaration of area as biosphere reserve in 1986; however, the deforestation has drastically reduced after the declaration due to high degree of protection, thus indicating the secure future of reserve in the long term under the current forest management practices. The present work will stand as the most up-to-date assessment on the forest cover of the Nilgiri Biosphere Reserve with immediate applications in monitoring and management of forest biodiversity.     

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R ²) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R ² values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.     

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed,Ethiopia

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km² in 1973 to 95.2 km² in 2011, while that of Lake Cheleleka whose area was 11.3 km² in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The “change and no change” analysis revealed that more than one third (548.0 km²) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.     

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.     

Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary,Delhi, India

Biomass is an important entity to understand the capacity of an ecosystem to sequester and accumulate carbon over time. The present study, done in collaboration with the Delhi Forest Department, focused on the estimation of growing stock and the woody biomass in the so-called lungs of Delhi—the Asola-Bhatti Wildlife Sanctuary in northern Aravalli hills. The satellite-derived vegetation strata were field-inventoried using stratified random sampling procedure. Growing stock was calculated for the individual sample plots using field data and species-specific volume equations. Biomass was estimated from the growing stock and the specific gravity of the wood. Among the four vegetation types, viz. Prosopis juliflora, Anogeissus pendula, forest plantation and the scrub, the P. juliflora was found to be the dominant vegetation in the area, covering 23.43 km² of the total area. The study revealed that P. juliflora forest with moderate density had the highest (10.7 m³/ha) while A. pendula forest with moderate density had the lowest (3.6 m³/ha) mean volume. The mean woody biomass was also found to be maximum in P. juliflora forest with moderate density (10.3 t/ha) and lowest in A. pendula forest with moderate density (3.48 t/ha). The total growing stock was estimated to be 20,772.95 m³ while total biomass worked out to be 19,366.83 t. A strong correlation was noticed between the normalized difference vegetation index (NDVI) and the growing stock (R ²?=?0.84)/biomass (R ²?=?0.88). The study demonstrated that growing stock and the biomass of the woody vegetation in Asola-Bhatti Wildlife Sanctuary could be estimated with high accuracy using optical remote sensing data.     

Vegetation ecology and carbon sequestration potential of shrubs in tropics of Chhattisgarh,India

Tropical forests are well known to have great species diversity and contribute substantial share in terrestrial carbon (C) stocks worldwide. Shrubs are long-neglected life form in the forest ecosystem, playing many roles in the forest and human life. Shrub has great impact on vegetation attributes which in turn modify the C storage and capture. In the present investigation, an attempt has been made to explore the dynamics of shrub species in four fire regimes, viz. high, medium, low, and no fire zones of Bhoramdeo Wildlife Sanctuary of Kawardha forest division (Chhattisgarh), India. The variations in structure, diversity, biomass, productivity, and C sequestration potential in all the sites were quantified. The density and basal area of shrub varied from 1250 to 3750 individuals ha^?1 and 2.79 to 4.92 m² ha^?1, respectively. The diversity indices showed that the value of Shannon index was highest in medium fire zone (3.77) followed by high, low, and no fire zones as 3.25, 3.12, and 2.32, respectively. The value of Simpson’s index or concentration of dominance (Cd) ranged from 0.08 to 0.20, species richness from 0.56 to 1.58, equitability from 1.41 to 1.44, and beta diversity from 1.50 to 4.20, respectively. The total biomass and C storage ranged from 6.82 to 15.71 and from 2.93 to 6.76 t ha^?1, respectively. The shrub density, importance value index (IVI), and abundance to frequency ratio (A/F) significantly correlated between high fire and medium fire zone. The basal area was found to be significantly positively correlated between high fire and medium fire, and low and no fire zones, respectively. Two-way cluster analysis reflected various patterns of clustering due to influence of the forest fire which showed that some species have distant clustering while some have smaller cluster. Principal component analysis (PCA) reflects variable scenario with respect to shrub layer. Ventilago calyculata and Zizyphus rotundifolia showed higher correlation between themselves in terms of basal area (BA). The total shrub production was 1.59–3.53 t ha^?1 year^?1 while the C sequestration potential of 0.71–1.57 t ha^?1 year^?1 under different fire regimes. Shrub community in the medium fire zone reflected higher productivity and higher C sequestration in comparison to other fire zone. Among the different plant parts, the biomass accumulation ratio was highest in the root of shrub community among various fire regimes. Screening of species for restoration and different land-use pattern on the basis of biomass accumulation and carbon sequestering potential would be an effective strategy for decision-making in sustainable forest management.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Dynamics of aeolian desertification and its driving forces in the Horqin Sandy Land,Northern China

Aeolian desertification is one of the most serious environmental and socioeconomic problems in arid, semi-arid, and dry subhumid zones. Understanding desertification processes and causes is important to provide reasonable and effective control measures for preventing desertification. With satellite remote sensing images as data source to assess the temporal and spatial dynamics of desertification from 1975 to 2010 in the Horqin Sandy Land, dynamic changes of aeolian desertification were detected using the human–machine interactive interpretation method. The driving factors of local desertification were analyzed based on natural and socioeconomic data. The results show that aeolian desertified land in the study area covered 30,199 km² in 2010, accounting for 24.1 % of the study area. The total area of aeolian desertified land obviously expanded from 30,884 km² in 1975 to 32,071 km² in 1990, and gradually decreased to 30,199 km² in 2010; aeolian desertified land represented an increasing trend firstly and then decreased. During the past 35 years, the gravity centers of desertified lands that are classified as extremely severe and severe generally migrated to the northeast, whereas those that are moderate and slight migrated to the northwest. The migration distance of severely desertified land was the largest, which indicated the southern desertified lands were improved during the last few decades. In addition, the climatic variation in the past 35 years has been favorable to desertification in the Horqin Sandy Land. Aeolian desertified land rapidly expanded from 1975 to 1990 under the combined effects of climate changes and unreasonable human activities. After the 1990s, the main driving factors responsible for the decrease in desertification were positive human activities, such as the series of antidesertification and ecological restoration projects.     

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.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Land use change and landslide characteristics analysis for community-based disaster mitigation

On August 8, 2009, Typhoon Morakot brought heavy rain to Taiwan, causing numerous landslides and debris flows in the Taihe village area of Meishan Township, Chiayi County, in south-central Taiwan. In the Taihe land is primary used for agriculture and land use management may be a factor in the area’s landslides. This study explores Typhoon Morakot-induced landslides and land use changes between 1999 and 2009 using GIS with the aid of field investigation. Spot 5 satellite images with a resolution of 2.5 m are used for landslide interpretation and manually digitalized in GIS. A statistical analysis for landslide frequency–area distribution was used to identify the landslide characteristics associated with different types of land use. There were 243 landslides with a total area of 2.75 km² in the study area. The area is located in intrinsically fragile combinations of sandstone and shale. Typhoon Morakot-induced landslides show a power–law distribution in the study area. Landslides were mainly located in steep slope areas containing natural forest and in areas planted with bamboo, tea, and betel nut. Land covered with natural forest shows the highest landslide ratio, followed by bamboo, betel nut, and tea. Landslides thus show a higher ratio in areas planted with shallow root vegetation such as bamboo, betel nut, and tea. Furthermore, the degree of basin development is proportional to the landslide ratio. The results show that a change in vegetation cover results in a modified landslide area and frequency and changed land use areas have higher landslide ratios than non-changed. Land use management and community-based disaster prevention are needed in mountainous areas of Taiwan for hazard mitigation.     

Modeling Large Fire Frequency and Burned Area in Canadian Terrestrial Ecosystems with Poisson Models

Large wildland fires are major disturbances that strongly influence the carbon cycling and vegetation dynamics of Canadian boreal ecosystems. Although large wildland fires have recently received much scrutiny in scientific study, it is still a challenge for researchers to predict large fire frequency and burned area. Here, we use monthly climate and elevation data to quantify the frequency of large fires using a Poisson model, and we calculate the probability of burned area exceeding a certain size using a compound Poisson process. We find that the Poisson model simulates large fire occurrence well during the fire season (May through August) using monthly climate, and the threshold probability calculated by the compound Poisson model agrees well with historical records. Threshold probabilities are significantly different among different Canadian ecozones, with the Boreal Shield ecozone always showing the highest probability. The fire prediction model described in this study and the derived information will facilitate future quantification of fire risks and help improve fire management in the region.     

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.     

Monitoring and validating the temporal dynamics of interday streamflow from two upland head micro-watersheds with different vegetative conditions during dry periods of the growing season in the Bohemian Massif,Czech Republic

At present, dynamic land use, climate change, and growing needs for fresh water are increasing the demand on the ecosystem effects of forest vegetation. Mountainous areas are at the forefront of scientific interest in European forest ecology and forest hydrology. Although uplands cover a significant area of the Czech Republic and other countries and are often covered with forest formations, they do not receive an appropriate amount of attention. Therefore, two experimental upland head micro-watersheds in the Bohemian Massif were selected for study because they display similar natural conditions, but different vegetative conditions (forest versus meadow). During the 2011 growing season, short-term streamflow measurements were carried out at the discharge profiles of both catchments and were evaluated in relation to climatic data (rainfall and temperature). The basic premise was that the streamflow in a forested catchment must exhibit different temporal dynamics compared to that in treeless areas and that these differences can be attributed to the effects of woody vegetation. These conclusions were drawn from measurements performed during dry periods lasting several days. A decreasing streamflow trend during the day part of the day (0900–1900 hours) was observed in both localities. The decrease reached approx. 44 % of the initial morning streamflow (0.24 dm³ s^?1 day^?1) in the treeless catchment and approx. 20 % (0.19 dm³ s^?1 day^?1) in the forested catchment. At night (1900–0900 hours), the streamflow in the forested catchment increased back to its initial level, whereas the streamflow in the treeless catchment stagnated or slowly decreased. We attribute these differences to the ecosystem effects of woody vegetation and its capacity to control water loss during the day. This type of vegetation can also function as a water source for the hydrographic network during the night.     

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.     

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.     

Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest,Eastern Ghats,India

Tropical dry forests are one of the most widely distributed ecosystems in tropics, which remain neglected in research, especially in the Eastern Ghats. Therefore, the present study was aimed to quantify the carbon storage in woody vegetation (trees and lianas) on large scale (30, 1 ha plots) in the dry deciduous forest of Sathanur reserve forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by species-specific allometric equations using diameter and wood density of species whereas in juvenile tree population and lianas, their respective general allometric equations were used to estimate the biomass. The fractional value 0.4453 was used to convert dry biomass into carbon in woody vegetation of tropical dry forest. The mean aboveground biomass value of juvenile tree population was 1.86 Mg/ha. The aboveground biomass of adult trees ranged from 64.81 to 624.96 Mg/ha with a mean of 245.90 Mg/ha. The mean aboveground biomass value of lianas was 7.98 Mg/ha. The total biomass of woody vegetation (adult trees + juvenile population of trees + lianas) ranged from 85.02 to 723.46 Mg/ha, with a mean value of 295.04 Mg/ha. Total carbon accumulated in woody vegetation in tropical dry deciduous forest ranged from 37.86 to 322.16 Mg/ha with a mean value of 131.38 Mg/ha. Adult trees accumulated 94.81% of woody biomass carbon followed by lianas (3.99%) and juvenile population of trees (1.20%). Albizia amara has the greatest biomass and carbon stock (58.31%) among trees except for two plots (24 and 25) where Chloroxylon swietenia contributed more to biomass and carbon stock. Similarly, Albizia amara (52.4%) showed greater carbon storage in juvenile population of trees followed by Chloroxylon swietenia (21.9%). Pterolobium hexapetalum (38.86%) showed a greater accumulation of carbon in liana species followed by Combretum albidum (33.04%). Even though, all the study plots are located within 10 km radius, they show a significant spatial variation among them in terms of biomass and carbon stocks which could be attributed to variation in anthropogenic pressures among the plots as well as to changes in tree density across landscapes. Total basal area of woody vegetation showed a significant positive (R ² = 0.978; P = 0.000) relationship with carbon storage while juvenile tree basal area showed the negative relationship (R ² = 0.4804; P = 0.000) with woody carbon storage. The present study generates a large-scale baseline data of dry deciduous forest carbon stock, which would facilitate carbon stock assessment at a national level as well as to understand its contribution on a global scale.     

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand

This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February–April months of 2008–2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM₁₀) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM₁₀. The correlations between MODIS AOD and PM₁₀, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes.     

MAPPING TROPICAL DEFORESTATION IN CENTRAL AFRICA

The NASA Landsat Pathfinder Humid Tropical Deforestation Project was to map deforestation activities in the humid tropics using datasets from both the Landsat TM (Thematic Mapper) and MSS (Multispectral Scanner System). In Central Africa, its effort had been constrained by the availability of cloud-free satellite coverage, especially for the 1970s Landsat MSS imagery. Here, we reported the deforestation rate and its spatial variability in the region using 18 pairs of co-registered Landsat TM imagery from the 1980s to 1990s. Of the total classified area of 416000 km, there were approximately 217000 km² of dense forest and 24000 km² of degraded forest in the 1980s. A total of 1012 km² of forest, including 542 km² of dense forest and 470 km² of degraded forest, were cleared annually with an annual deforestation rate of 0.42%, varying among scenes ranging from 0.03 to 2.72%. Additionally, an average of 0.12% (ranging from 0.01 to 0.77% among scenes) or 257 km² of dense forest was degraded annually. Regression analyses indicated that extensive deforestation occurred in areas with larger forest cover, including dense and degraded forests. Image interpretation also confirmed the hypothesized relationship between deforestation and forest accessibility. The annual clearance of the dense forest was significantly related to the rural population density, and there was a positive relationship between the dense forest degraded during the 1980s–1990s and the degraded forest area in the 1980s.