Emissions from grassland burning in Kaziranga National Park,India – analysis from IRS-P6 AWiFS satellite remote sensing datasets

Biomass burning from vegetation fires is an important source of greenhouse gas emissions. In this study, we quantify biomass burning emissions from grasslands from the highly sensitive Kaziranga National Park, Assam, Northeast India. Most of the fires in the park are ‘controlled burning fires’ set by the park officials for management purposes. We evaluated the short-term impacts of fires and the resulting air pollution through integrating biomass burnt information from satellite remote sensing datasets. IRS-P6 Advanced Wide Field Sensor (AWiFS) data during March and April corresponding to dry season were evaluated to delineate the burnt areas. These burnt area estimates were then integrated with biomass data and emission factors for quantifying the greenhouse gas emissions. Results suggested that of the total study area of 37,822 ha, nearly 3163.282 ha has been burnt during March, 2005. Within one month, the burnt area increased to 7443.92 ha by April, i.e., from 8.36% to 19.68%. In total, biomass burning from the grasslands contributed to 29.65 Tg CO₂, 1.19 Tg CO, 0.071 Tg NOx, 0.042 Tg CH₄, 0.0625 Tg total non-methane hydrocarbons, 0.152 Tg of particulate matter, and 0.062 Tg of organic carbon and 0.008 Tg of black carbon during April. The importance of ‘fire’ as a management tool for maintaining the wildlife habitat has been highlighted in addition to some of the adverse affects of air pollution resulting from such management practices. The results from this study will be useful to forest officials as well as policy makers to undertake some sustainable forest management practices to maintain an ideal habitat for Kaziranga's wildlife.     

Scenarios in tropical forest degradation: carbon stock trajectories for REDD+

Background

Human-caused disturbance to tropical rainforests—such as logging and fire—causes substantial losses of carbon stocks. This is a critical issue to be addressed in the context of policy discussions to implement REDD+. This work reviews current scientific knowledge about the temporal dynamics of degradation-induced carbon emissions to describe common patterns of emissions from logging and fire across tropical forest regions. Using best available information, we: (i) develop short-term emissions factors (per area) for logging and fire degradation scenarios in tropical forests; and (ii) describe the temporal pattern of degradation emissions and recovery trajectory post logging and fire disturbance.

Results

Average emissions from aboveground biomass were 19.9 MgC/ha for logging and 46.0 MgC/ha for fire disturbance, with an average period of study of 3.22 and 2.15 years post-disturbance, respectively. Longer-term studies of post-logging forest recovery suggest that biomass accumulates to pre-disturbance levels within a few decades. Very few studies exist on longer-term (>10 years) effects of fire disturbance in tropical rainforests, and recovery patterns over time are unknown.

Conclusions

This review will aid in understanding whether degradation emissions are a substantial component of country-level emissions portfolios, or whether these emissions would be offset by forest recovery and regeneration.

     

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO_x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO₂, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O₃ and NO_x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.     

Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands

Background

Peatlands are an important component of Canada’s landscape, however there is little information on their national-scale net emissions of carbon dioxide [Net Ecosystem Exchange (NEE)] and methane (CH₄). This study compiled results for peatland NEE and CH₄ emissions from chamber and eddy covariance studies across Canada. The data were summarized by bog, poor fen and rich-intermediate fen categories for the seven major peatland containing terrestrial ecozones (Atlantic Maritime, Mixedwood Plains, Boreal Shield, Boreal Plains, Hudson Plains, Taiga Shield, Taiga Plains) that comprise >?96% of all peatlands nationally. Reports of multiple years of data from a single site were averaged and different microforms (e.g., hummock or hollow) within these peatland types were kept separate. A new peatlands map was created from forest composition and structure information that distinguishes bog from rich and poor fen. National Forest Inventory k-NN forest structure maps, bioclimatic variables (mean diurnal range and seasonality of temperatures) and ground surface slope were used to construct the new map. The Earth Observation for Sustainable Development map of wetlands was used to identify open peatlands with minor tree cover.

Results

The new map was combined with averages of observed NEE and CH₄ emissions to estimate a growing season integrated NEE (±?SE) at ??108.8 (±?41.3) Mt CO₂ season^?1 and CH₄ emission at 4.1 (±?1.5) Mt CH₄ season^?1 for the seven ecozones. Converting CH₄ to CO₂ equivalent (CO₂e; Global Warming Potential of 25 over 100 years) resulted in a total net sink of ??7.0 (±?77.6) Mt CO₂e season^?1 for Canada. Boreal Plains peatlands contributed most to the NEE sink due to high CO₂ uptake rates and large peatland areas, while Boreal Shield peatlands contributed most to CH₄ emissions due to moderate emission rates and large peatland areas. Assuming a winter CO₂ emission of 0.9 g CO₂ m^?2 day^?1 creates an annual CO₂ source (24.2 Mt CO₂ year^?1) and assuming a winter CH₄ emission of 7 mg CH₄ m^?2 day^?1 inflates the total net source to 151.8 Mt CO₂e year^?1.

Conclusions

This analysis improves upon previous basic, aspatial estimates and discusses the potential sources of the high uncertainty in spatially integrated fluxes, indicating a need for continued monitoring and refined maps of peatland distribution for national carbon and greenhouse gas flux estimation.

     

Forest Fire Burnt Area Assessment in the Biodiversity Rich Regions Using Geospatial Technology: Uttarakhand Forest Fire Event 2016

The hills of Uttarakhand witness forest fire every year during the summer season and the number of these fire events is reported to have increased due to increased anthropogenic disturbances as well as changes in climate. These fires cause significant damage to the natural resources which can be mapped and monitored using satellite images by virtue of its synoptic coverage of the landscape and near real time monitoring. This study presents burnt area assessment caused by the fire episode of April 2016 to the forest vegetation. Digital classification of satellite images was done to extract the burnt area which was found to be 3774.14 km², representing 15.28% of the total forest area of the state. It also gives an account of cumulative progression of forest fire in Uttarakhand using satellite images of three dates viz. 23rd, 27th May and 2nd June, 2016. Results were analyzed at district, administrative and forest division level using overlay analysis. Separate area statistics were given for different categories of biological richness, forest types and protected areas affected by forest fire. The burnt area assessment can be used in mitigation planning to prevent drastic ecological impacts of the forest fire on the landscape.     

Application of hyperspectral remote sensing reflectance data to photochemical rate calculations in the Duplin River,a tidal river on the coast of Georgia,USA

Estuaries are photochemically dynamic environments with high carbon loads and relatively small areas. The small area poses problems for large-scale satellite-based remote sensing calculations, where the resolution is too coarse to distinguish land from water. Airborne remote sensing instruments have the potential to reveal the dynamics of these areas with fine-scale resolution. In June 2006, hyperspectral remote sensing imagery, using an AISA Eagle instrument, was collected over the tidal Duplin River, Georgia, USA. A dark-water updated version of the SeaUV algorithm was applied to the AISA remote sensing image to determine diffuse attenuation constants in the ultraviolet and calculate surface photochemical production rates of two inorganic products – carbon monoxide (CO) and carbon dioxide (CO₂). For an average day in June at the study site, the modeled photoproduction rates for CO₂ and CO averaged ~7 × 10^?1 nmol C/day/cm³ and ~3.5 × 10^?2 nmol C/day/cm³, respectively.     

Estimates of CO2 from fires in the United States: implications for carbon management

Background  

Fires emit significant amounts of CO₂ to the atmosphere. These emissions, however, are highly variable in both space and time. Additionally, CO₂ emissions estimates from fires are very uncertain. The combination of high spatial and temporal variability and substantial uncertainty associated with fire CO₂ emissions can be problematic to efforts to develop remote sensing, monitoring, and inverse modeling techniques to quantify carbon fluxes at the continental scale. Policy and carbon management decisions based on atmospheric sampling/modeling techniques must account for the impact of fire CO₂ emissions; a task that may prove very difficult for the foreseeable future. This paper addresses the variability of CO₂ emissions from fires across the US, how these emissions compare to anthropogenic emissions of CO₂ and Net Primary Productivity, and the potential implications for monitoring programs and policy development.     

Revised methane emissions factors and spatially distributed annual carbon fluxes for global livestock

Background

Livestock play an important role in carbon cycling through consumption of biomass and emissions of methane. Recent research suggests that existing bottom-up inventories of livestock methane emissions in the US, such as those made using 2006 IPCC Tier 1 livestock emissions factors, are too low. This may be due to outdated information used to develop these emissions factors. In this study, we update information for cattle and swine by region, based on reported recent changes in animal body mass, feed quality and quantity, milk productivity, and management of animals and manure. We then use this updated information to calculate new livestock methane emissions factors for enteric fermentation in cattle, and for manure management in cattle and swine.

Results

Using the new emissions factors, we estimate global livestock emissions of 119.1 ± 18.2 Tg methane in 2011; this quantity is 11% greater than that obtained using the IPCC 2006 emissions factors, encompassing an 8.4% increase in enteric fermentation methane, a 36.7% increase in manure management methane, and notable variability among regions and sources. For example, revised manure management methane emissions for 2011 in the US increased by 71.8%. For years through 2013, we present (a) annual livestock methane emissions, (b) complete annual livestock carbon budgets, including carbon dioxide emissions, and (c) spatial distributions of livestock methane and other carbon fluxes, downscaled to 0.05 × 0.05 degree resolution.

Conclusions

Our revised bottom-up estimates of global livestock methane emissions are comparable to recently reported top-down global estimates for recent years, and account for a significant part of the increase in annual methane emissions since 2007. Our results suggest that livestock methane emissions, while not the dominant overall source of global methane emissions, may be a major contributor to the observed annual emissions increases over the 2000s to 2010s. Differences at regional and local scales may help distinguish livestock methane emissions from those of other sectors in future top-down studies. The revised estimates allow improved reconciliation of top-down and bottom-up estimates of methane emissions, will facilitate the development and evaluation of Earth system models, and provide consistent regional and global Tier 1 estimates for environmental assessments.

     

The effects of land cover change on carbon stock dynamics in a dry Afromontane forest in northern Ethiopia

Background

Forests play an important role in mitigating global climate change by capturing and sequestering atmospheric carbon. Quantitative estimation of the temporal and spatial pattern of carbon storage in forest ecosystems is critical for formulating forest management policies to combat climate change. This study explored the effects of land cover change on carbon stock dynamics in the Wujig Mahgo Waren forest, a dry Afromontane forest that covers an area of 17,000 ha in northern Ethiopia.

Results

The total carbon stocks of the Wujig Mahgo Waren forest ecosystems estimated using a multi-disciplinary approach that combined remote sensing with a ground survey were 1951, 1999, and 1955 GgC in 1985, 2000 and 2016 years respectively. The mean carbon stocks in the dense forests, open forests, grasslands, cultivated lands and bare lands were estimated at 181.78?±?27.06, 104.83?±?12.35, 108.77?±?6.77, 76.54?±?7.84 and 83.11?±?8.53 MgC ha^?1 respectively. The aboveground vegetation parameters (tree density, DBH and height) explain 59% of the variance in soil organic carbon.

Conclusions

The obtained estimates of mean carbon stocks in ecosystems representing the major land cover types are of importance in the development of forest management plan aimed at enhancing mitigation potential of dry Afromontane forests in northern Ethiopia.

     

Pasture enclosures increase soil carbon dioxide flux rate in Semiarid Rangeland,Kenya

Background

Pasture enclosures play an important role in rehabilitating the degraded soils and vegetation, and may also influence the emission of key greenhouse gasses (GHGs) from the soil. However, no study in East Africa and in Kenya has conducted direct measurements of GHG fluxes following the restoration of degraded communal grazing lands through the establishment of pasture enclosures. A field experiment was conducted in northwestern Kenya to measure the emission of CO₂, CH₄ and N₂O from soil under two pasture restoration systems; grazing dominated enclosure (GDE) and contractual grazing enclosure (CGE), and in the adjacent open grazing rangeland (OGR) as control. Herbaceous vegetation cover, biomass production, and surface (0–10 cm) soil organic carbon (SOC) were also assessed to determine their relationship with the GHG flux rate.

Results

Vegetation cover was higher enclosure systems and ranged from 20.7% in OGR to 40.2% in GDE while aboveground biomass increased from 72.0 kg DM ha^?1 in OGR to 483.1 and 560.4 kg DM ha^?1 in CGE and GDE respectively. The SOC concentration in GDE and CGE increased by an average of 27% relative to OGR and ranged between 4.4 g kg^?1 and 6.6 g kg^?1. The mean emission rates across the grazing systems were 18.6 μg N m^?2 h^?1, 50.1 μg C m^?2 h^?1 and 199.7 mg C m^?2 h^?1 for N₂O, CH₄, and CO₂, respectively. Soil CO₂ emission was considerably higher in GDE and CGE systems than in OGR (P?<?0.001). However, non-significantly higher CH₄ and N₂O emissions were observed in GDE and CGE compared to OGR (P?=?0.33 and 0.53 for CH₄ and N₂O, respectively). Soil moisture exhibited a significant positive relationship with CO₂, CH₄, and N₂O, implying that it is the key factor influencing the flux rate of GHGs in the area.

Conclusions

The results demonstrated that the establishment of enclosures in tropical rangelands is a valuable intervention for improving pasture production and restoration of surface soil properties. However, a long-term study is required to evaluate the patterns in annual CO₂, N₂O, CH₄ fluxes from soils and determine the ecosystem carbon balance across the pastoral landscape.

     

Detection of Changes in Glacier Mass Balance Using Satellite and Meteorological Data in Tirungkhad Basin Located in Western Himalaya

Himalayan glaciers and their mass balance are poorly sampled through direct mass balance measurements. Thus, based on Landsat datasets of ETM⁺ (2000), ETM⁺ (2006) and TM (2011), mass balance studies of 32 glaciers was carried out using accumulation area ratio (AAR) method in the Tirungkhad river basin, a tributary of Satluj River, located in western Himalayan region. The overall specific mass balance was negative varying from ?27 cm (2000) to ?41 cm (2011). Out of 32 glaciers, 27 glaciers (81.2 %) showed negative mean mass balance and 5 glaciers (18.7 %) showed positive mean mass balance. Mean of specific mass balance for the year 2000, 2006 and 2011 was found to be ?48 cm, ?55 cm and ?0.61 cm respectively, in case of glaciers with negative mass balance while in case of glaciers with positive mass balance, it was 0.67 cm (2000), 0.56 cm (2006) and 0.47 cm (2011). The investigations suggested a loss of ?0.034 km³ of glacial ice for 2000, 0.036 km³ for 2006 and 0.038 km³ for 2011 respectively. The negative mass balance of the glaciers since 2000 correlates well with the increasing trend of annual mean temperature and decreasing trend of precipitation (snow water equivalent (SWE) and rainfall). Based on Mann Kendall test the temperature and SWE trends were significant at 95 % confidence level, however, the rainfall trend was insignificant.     

Atmospheric pollution assessment near potential source of natural aerosols in the South Gobi Desert region,China

ABSTRACT

Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM_2.5 and PM₁₀ are 38.2 ± 19.5 and 101 ± 80.7 μg/m³ exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m³, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM_2.5 and PM₁₀ ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM_2.5 and PM₁₀ content exceeded 200 and 1000 µg/m³, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.     

Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece

The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.     

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

Background

Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO₂ emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO₂ emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO₂ emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.

Results

This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year^?1 and subsequently caused a carbon loss of approximately 9 million Mg C year^?1, which is equal to emissions of 33 million Mg CO₂ year^?1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha^?1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.

Conclusion

The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+?implementation in Malaysia.

     

Effect of Forest Fire on Snow Reflectance in Uttarakhand Region: An Analysis Using AWiFS and INSAT-3D Data

In the summer of 2016, fire broke out in the forests of Uttarakhand state. The effect of forest fire was thought to have severe impact on nearby glaciated region in terms of faster melting. It is understood that contamination and heat enhance the snowmelt, which reduces the reflectance in different part of EM spectrum. In order to assess the effect, AWiFS and IMAGER data from Resourcesat-1 and INSAT-3D, respectively, for the months of April and May of the years 2012, 2013, 2014 and 2016 were used to compare the reflectance of snow. It was observed that pre- and post-fire data of 2016 show drop in reflectance in comparison with earlier years. The change in reflectance for locations near to forest fire was significantly high in comparison with previous year images, whereas far-away locations did not show much change. This drastic drop may be attributed to deposition of black carbon on nearby locations in snow-covered area. AWS data of nearby Joshimath observatory were also analysed to avoid any anomalous change in temperature for the same duration.     

Ionospheric tomography based on GNSS observations of the CMONOC: performance in the topside ionosphere

This study carries out a quantitative analysis of the performance of ionospheric tomography in the topside ionosphere, utilizing data of October 2011 collected from 260 Global Navigation Satellite System (GNSS) stations in the Crustal Movement Observation Network of China. This tomographic reconstruction with a resolution of 2° in latitude, 2° in longitude and 20 km in altitude has more than 70 % of voxels traversed by GPS raypaths and is able to provide reliable bottom parts of ionospheric profiles. Compared with the observations measured by the Defense Meteorological Satellite Program (DMSP) satellites (F16, F17 and F18) at an altitude of 830–880 km, the results show that there is an overestimation in the reconstructed plasma density at the DMSP altitude, and the reconstruction is better during daytime than nighttime. In addition, the reconstruction at nighttime also indicates a solar activity and latitudinal dependence. In summary, with respect to DMSP measurements, the daytime bias is on average from ?0.32 × 10⁵/cm³ to ?0.28 × 10⁵/cm³, while the nighttime bias is between ?0.37 × 10⁵/cm³ and ?0.24 × 10⁵/cm³, and the standard deviation at daytime and at nighttime is, respectively, 0.082 × 10⁵/cm³ to 0.244 × 10⁵/cm³ and 0.086 × 10⁵/cm³ to 0.428 × 10⁵/cm³. This study suggests that vertical ionospheric profiles from other sources, such as ionosondes or GNSS occultation satellites, should be incorporated into ground-based GNSS topside tomographic studies.     

Carbon dynamics of paper,engineered wood products and bamboo in landfills: evidence from reactor studies

Background

There has been growing interest in the development of waste-specific decay factors for estimation of greenhouse gas emissions from landfills in national greenhouse gas inventories. Although engineered wood products (EWPs) and paper represent a substantial component of the solid waste stream, there is limited information available on their carbon dynamics in landfills. The objective of this study was to determine the extent of carbon loss for EWPs and paper products commonly used in Australia. Experiments were conducted under laboratory conditions designed to simulate optimal anaerobic biodegradation in a landfill.

Results

Methane generation rates over incubations of 307–677 days ranged from zero for medium-density fibreboard (MDF) to 326 mL CH₄ g^?1 for copy paper. Carbon losses for particleboard and MDF ranged from 0.7 to 1.6%, consistent with previous estimates. Carbon loss for the exterior wall panel product (2.8%) was consistent with the expected value for blackbutt, the main wood type used in its manufacture. Carbon loss for bamboo (11.4%) was significantly higher than for EWPs. Carbon losses for the three types of copy paper tested ranged from 72.4 to 82.5%, and were significantly higher than for cardboard (27.3–43.8%). Cardboard that had been buried in landfill for 20 years had a carbon loss of 27.3%—indicating that environmental conditions in the landfill did not support complete decomposition of the available carbon. Thus carbon losses for paper products as measured in bioreactors clearly overestimate those in actual landfills. Carbon losses, as estimated by gas generation, were on average lower than those derived by mass balance. The low carbon loss for particleboard and MDF is consistent with carbon loss for Australian wood types described in previous studies. A factor for carbon loss for combined EWPs and wood in landfills in Australia of 1.3% and for paper of 48% is proposed.

Conclusions

The new suggested combined decay factor for wood and EWPs represents a significant reduction from the current factor used in the Australian greenhouse gas inventory; whereas the suggested decay factor for paper is similar to the current decay factor. Our results improve current understanding of the carbon dynamics of harvested wood products, and allow more refined estimates of methane emissions from landfills.

     

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment,South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F_4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F_2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.     

Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of the Teacapan-Agua Brava lagoon system,Mexico

Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ ?1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > ?1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.