Reservoir capacity estimation using SARAL/AltiKa altimetry data coupled with Resourcesat P6-AWiFS and RISAT 1 microwave data

Reservoir water levels extracted from SARAL/AltiKa GDR data for the period 2013–2014 and water spread areas delineated from Resourcesat P6-AWiFS sensor and RISAT 1 microwave data corresponding to SARAL/AltiKa cycles were used for assessment of reservoir capacity in the Mayurakshi reservoir, Jharkhand state, India. It was found that the reservoir capacity based on the SARAL is around 474.62 Mm³ in comparison to in situ based estimate i.e. around 486.6 Mm³, indicating variation of <3%. Further, comparison of these estimates computed using SARAL and in situ with original reservoir capacity (547.59 Mm³) indicated loss of reservoir capacity is around 13.33 and 11.14%, respectively, within a span of 59 years. The hydrographic survey in the year 1999–2000 also proved that the storage capacity has reduced from 547.6 Mm³ in 1955 to 474.8 Mm³ indicating loss of nearly 13.3 % of total live capacity over period of 45 years.     

Assessment of sedimentation in harakud reservoir using digital remote sensing technique

As per recommendations of Working Group for National Action for Reservoir Sedimentation Assessment, National Institute of Hydrology has taken up study on sedimentation for 25 reservoirs in India during X plan period using remote sensing technique. One such study for Hirakud reservoir in Mahanadi basin in Orissa for year 1999–2001 is described here. Reservoir’s original utilizable and gross volumes were 5818 and 8136 M m³, respectively. Minimum draw down level (MDDL) and full reservoir level (FRL) for reservoir are 179.83 and 192.02 m, respectively. Linear Imaging Self Scanning (LISS)- III data of Indian Remote Sensing Satellites (IRS) 1C and 1D, covering elevation range between 180.68 and 191.89 m, were used. Rule based classification was applied to ‘water index’ and radiances of near infrared band to determine water spread area. Revised live storage capacity was 4842 M m³. The silt index for the live storage area was 2.623 ha m (100 km² year)^-1 (0.376 % of live storage or 21.9 M m³ year^-1). Total live storage lost in sedimentation was 984 M m³ (16.90 % of live storage).     

Historical effects of dissolved organic carbon export and land management decisions on the watershed-scale forest carbon budget of a coastal British Columbia Douglas-fir-dominated landscape

Background

To address how natural disturbance, forest harvest, and deforestation from reservoir creation affect landscape-level carbon (C) budgets, a retrospective C budget for the 8500 ha Sooke Lake Watershed (SLW) from 1911 to 2012 was developed using historical spatial inventory and disturbance data. To simulate forest C dynamics, data was input into a spatially-explicit version of the Carbon Budget Model-Canadian Forest Sector (CBM-CFS3). Transfers of terrestrial C to inland aquatic environments need to be considered to better capture the watershed scale C balance. Using dissolved organic C (DOC) and stream flow measurements from three SLW catchments, DOC load into the reservoir was derived for a 17-year period. C stocks and stock changes between a baseline and two alternative management scenarios were compared to understand the relative impact of successive reservoir expansions and sustained harvest activity over the 100-year period.

Results

Dissolved organic C flux for the three catchments ranged from 0.017 to 0.057 Mg C ha^?1 year^?1. Constraining CBM-CFS3 to observed DOC loads required parameterization of humified soil C losses of 2.5, 5.5, and 6.5%. Scaled to the watershed and assuming none of the exported terrestrial DOC was respired to CO₂, we hypothesize that over 100 years up to 30,657 Mg C may have been available for sequestration in sediment. By 2012, deforestation due to reservoir creation/expansion resulted in the watershed forest lands sequestering 14 Mg C ha^?1 less than without reservoir expansion. Sustained harvest activity had a substantially greater impact, reducing forest C stores by 93 Mg C ha^?1 by 2012. However approximately half of the C exported as merchantable wood during logging (~176,000 Mg C) may remain in harvested wood products, reducing the cumulative impact of forestry activity from 93 to 71 Mg C ha^?1.

Conclusions

Dissolved organic C flux from temperate forest ecosystems is a small but persistent C flux which may have long term implications for C storage in inland aquatic systems. This is a first step integrating fluvial transport of C into a forest carbon model by parameterizing DOC flux from soil C pools. While deforestation related to successive reservoir expansions did impact the watershed-scale C budget, over multi-decadal time periods, sustained harvest activity was more influential.

     

Analysis of snow cover and climatic variability in Bhaga basin located in western Himalaya

Bhaga Basin has complex mountainous terrain; little study has been done on the spatial and temporal characteristics of snow cover in the region. The Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow cover products between 2001 and 2012 for winter period (November–April) have been used to study the variation in snow cover area (SCA). The statistical analysis based on non-parametric Mann Kendall and Sen’s slope methods have been used for detecting and estimating trends for climatic variables (temperature and snowfall) and SCA for winter period. Results of statistical analysis indicate rise in minimum temperature (0.02 °C year^?1) and fall in maximum temperature (0.17 °C year^?1). It also shows decrease in mean seasonal snowfall (0.07 cm year^?1). The seasonal SCA was found to decrease at the rate of 0.002% year^?1. This study indicates that the climate change is probably one of the major causes for depleting SCA.     

Reservoir capacity estimation of Singoor reservoir,India, by per-pixel and sub-pixel processing of multi-date satellite images

Traditional approaches of image classification, such as maximum likelihood and the band thresholding method, involve the per-pixel approach to delineate the water spread area of a reservoir. One of the limitations of these approaches is that the pixels representing the reservoir border, containing a mixture of water, soil and vegetation, are classified entirely as water, thereby resulting in inaccurate estimates of the water spread area. To compute the water spread area accurately, the sub-pixel approach has been used in this study. The water spread areas extracted using per-pixel and sub-pixel approaches from IRS-1D and P6 satellite image data were in turn used to quantify the capacity of the Singoor reservoir, Andhra Pradesh, India. The estimated capacity of the reservoir using the per-pixel and sub-pixel approaches was 727.75 Mm³ and 716.11 Mm³, respectively. The validation shows that the sub-pixel approach produced much less error (1.08%) than the per-pixel based approach (3.14%).     

Assessment of sedimentation in the Linganmakki Reservoir using remote sensing

The reservoir surveys carried out using the conventional equipments like theodolite, plane table, sextant, range finders, sounding rods, echo-sounders and slow moving boats etc. are time consuming and sometime it takes up to three years to complete the survey of a major reservoir like Hirakud. During such longtime of survey, the siltation pattern and the bed levels also get changed. So, the need to update the sediment measurement techniques and to introduce latest technology available in the field was felt to overcome the difficulties faced in the conventional method especially in major reservoirs. With this objective, the present study of the assessment of the sedimentation in Linganmakki reservoir is carried out using remote sensing and GIS techniques. The revised capacities in the zone of reservoir levels (533.05 – 548.78 m) for the year 1989–1990 and 2001–2002 in the zone of 532.20 – 545.91 m of reservoir levels were determined, which is found 2837.84 M cum and 2207.95 M cum respectively. Based on these results, the sedimentation rates in the zone (533.05 – 548.78 m) come out to be 8.96 ham/100 km²/year and for the zone (532.20 – 545.91 m) is 8.57 ha-m/100 km²/year. It is found that the results are comparable during and the uniform sediment rate of 8.57 ha-m/100 km²/ year can be considered.     

Estimation of Partial Pressure of Carbon Dioxide and Air-Sea Fluxes in Hooghly Estuary Based on In Situ and Satellite Observations

An empirical model is developed and used with remotely sensed predictors: sea surface temperature (SST) and chlorophyll-a concentration (Chl-a), to compute surface water partial pressure of carbon dioxide (pCO_2w) and air-sea fluxes of CO₂ in the Hooghly estuary and its adjacent coastal oceans. In situ observations used here were based on measurements carried out in this region during winter and summer periods in 2008. The estimated pCO_2w compares well with the in situ observations at root mean square error ±18 μatm. In winter, estimated pCO_2w ranges between 320 and 500 μatm with large values (>400 μatm) on the south-western and south-eastern flanks of the coastal domain and lower values (340–375 μatm) on the main-channel. In summer, it remained spatially uniform at 450 μatm. Extrapolation of the results over the study region based on the Moderate Imaging Specroradiometer (MODIS) measured SST and Chl-a suggests that the region is a strong source of atmospheric CO₂ during the summer with net release of 0.095 Tg C year^?1 (equivalent to mean flux of 90 molC m^?2 year^?1) and is a weak source during the winter with net release of 0.006 Tg C yr^?1 (0.5 molC m^?2 year^?1) from the geographical extent of 6000 Km² area.     

A carbon balance model for the great dismal swamp ecosystem

Background

Carbon storage potential has become an important consideration for land management and planning in the United States. The ability to assess ecosystem carbon balance can help land managers understand the benefits and tradeoffs between different management strategies. This paper demonstrates an application of the Land Use and Carbon Scenario Simulator (LUCAS) model developed for local-scale land management at the Great Dismal Swamp National Wildlife Refuge. We estimate the net ecosystem carbon balance by considering past ecosystem disturbances resulting from storm damage, fire, and land management actions including hydrologic inundation, vegetation clearing, and replanting.

Results

We modeled the annual ecosystem carbon stock and flow rates for the 30-year historic time period of 1985–2015, using age-structured forest growth curves and known data for disturbance events and management activities. The 30-year total net ecosystem production was estimated to be a net sink of 0.97 Tg C. When a hurricane and six historic fire events were considered in the simulation, the Great Dismal Swamp became a net source of 0.89 Tg C. The cumulative above and below-ground carbon loss estimated from the South One and Lateral West fire events totaled 1.70 Tg C, while management activities removed an additional 0.01 Tg C. The carbon loss in below-ground biomass alone totaled 1.38 Tg C, with the balance (0.31 Tg C) coming from above-ground biomass and detritus.

Conclusions

Natural disturbances substantially impact net ecosystem carbon balance in the Great Dismal Swamp. Through alternative management actions such as re-wetting, below-ground biomass loss may have been avoided, resulting in the added carbon storage capacity of 1.38 Tg. Based on two model assumptions used to simulate the peat system, (a burn scar totaling 70 cm in depth, and the soil carbon accumulation rate of 0.36 t C/ha^?1/year^?1 for Atlantic white cedar), the total soil carbon loss from the South One and Lateral West fires would take approximately 1740 years to re-amass. Due to the impractical time horizon this presents for land managers, this particular loss is considered permanent. Going forward, the baseline carbon stock and flow parameters presented here will be used as reference conditions to model future scenarios of land management and disturbance.

     

Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km², respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km², respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km². Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha^?1 year^?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha^?1 year^?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future.     

Global carbon dioxide removal rates from forest landscape restoration activities

Background

Forest landscape restoration (FLR) has been adopted by governments and practitioners across the globe to mitigate and adapt to climate change and restore ecological functions across degraded landscapes. However, the extent to which these activities capture CO₂ with associated climate mitigation impacts are poorly known, especially in geographies where data on biomass growth of restored forests are limited or do not exist. To fill this gap, we developed biomass accumulation rates for a set of FLR activities (natural regeneration, planted forests and woodlots, agroforestry, and mangrove restoration) across the globe and global CO₂ removal rates with corresponding confidence intervals, grouped by FLR activity and region/climate.

Results

Planted forests and woodlots were found to have the highest CO₂ removal rates, ranging from 4.5 to 40.7 t CO² ha^?1 year^?1 during the first 20 years of growth. Mangrove tree restoration was the second most efficient FLR at removing CO₂, with growth rates up to 23.1 t CO₂ ha^?1 year^?1 the first 20 years post restoration. Natural regeneration removal rates were 9.1–18.8 t CO₂ ha^?1 year^?1 during the first 20 years of forest regeneration, followed by agroforestry, the FLR category with the lowest and regionally broad removal rates (10.8–15.6 t CO₂ ha^?1 year^?1). Biomass growth data was most abundant and widely distributed across the world for planted forests and natural regeneration, representing 45% and 32% of all the data points assessed, respectively. Agroforestry studies, were only found in Africa, Asia, and the Latin America and Caribbean regions.

Conclusion

This study represents the most comprehensive review of published literature on tree growth and CO₂ removals to date, which we operationalized by constructing removal rates for specific FLR activities across the globe. These rates can easily be applied by practitioners and decision-makers seeking to better understand the positive climate mitigation impacts of existing or planned FLR actions, or by countries making restoration pledges under the Bonn Challenge Commitments or fulfilling Nationally Determined Contributions to the UNFCCC, thereby helping boost FLR efforts world-wide.

     

Capacity assessment of the Qattara Depression: Egypt as a sink for the global sea level rise

One of the most prominent geomorphological features in the Western Desert of Egypt is the occurrence of the Qattara Depression (20,800 km² or 2% of Egypt’s area), which has the deepest point in Africa. Topographic analysis of the depression was carried out using fine resolution digital elevation models acquired from the Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER-GDEM) in order to assess the capacity of the depression. Results showed that the lower point in the depression occurs at ?136 m and its capacity at the sea level is 1340 billion m³, which equals 10 times the live storage capacity of the Lake Nasser behind the Aswan High Dam. Although the depression has the aptitude to be a reservoir for any augmentation in the sea level, detailed environmental impact assessment studies should be implemented to address geologic, climatic, biologic as well as socio-economic impacts.     

Assessment and planning for integrated river basin management using remote sensing,SWAT model and morphometric analysis (case study: Kaddam river basin,India)

Abstract

River basin assessment is crucial for water management and to address the watershed issues. So, an integrated river basin management and assessment model using morphometric assessment, remote sensing, GIS and SWAT model was envisaged and applied to Kaddam river basin, Telangana state, India. Morphometric results showed high drainage density ranging from 2.19 to 5.5?km²/km, with elongated fan shape having elongation ratio of 0.60–0.75 with sparse vegetation and high relief. Land use change assessment showed that 265.26?km² of forest land is converted into irrigated land and has increased sediment yields in watersheds. The calibration (r ²?=?0.74, NSE?=?0.84) and validation (r ²?=?0.72, NSE?=?0.84) of SWAT model showed that simulated and observed results were in agreement and in recommended ranges. The SWAT simulations were used to compute mean annual water and sediment yield from 1997 to 2012, along with morphometric results to categorize critical watersheds and conservation structures were proposed accordingly.     

Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin,Ethiopia

Integration of remote sensing data sets from multiple satellites is tested to simulate water storage variation of Lake Ziway, Ethiopia for the period 2009-2018. Sixty Landsat ETM+/OLI images served to trace temporal variation of lake surface area using a water extraction index. Time series of lake levels were acquired from two altimetry databases that were validated by in-situ lake level measurements. Coinciding pairs of optical satellite based lake surface area and radar altimetry based lake levels were related through regression and served for simulating lake storage variation. Indices for extracting lake surface area from images showed 91–99 % overall accuracy. Lake water levels from the altimetry products well agreed to in-situ lake level measurements with R² = 0.92 and root mean square error of 11.9 cm. Based on this study we conclude that integrating satellite imagery and radar altimetry is a viable approach for frequent and accurate monitoring of lake water volume variation and for long-term change detection. Findings indicate water level reduction (4 cm/annum), surface area shrinkage (0.08km²/annum) and water storage loss (20.4Mm³/annum) of Lake Ziway (2009–2018).     

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m². In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r ² = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m³/m³. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m³/m³. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m³/m³). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.     

Understanding the Spatial Variability of Chlorophyll a and Total Suspended Matter Distribution Along the Southwest Bay of Bengal Using In-Situ and OCM-2 & MODIS-Aqua Measurements

Spatial and temporal distribution of chlorophyll a (chl a) and Total Suspended Matter (TSM) and inter comparison of Ocean Color Monitor-2 (OCM-2) and Moderate Resolution Imaging Spectro-radiometer (MODIS-Aqua) derived chlorophyll a and TSM was made along the southwest Bay of Bengal (BoB). The in-situ chl a and TSM concentration measured during different seasons were ranged from 0.09 to 10.63 μgl^?1 and 11.04–43.75 mgl^?1 respectively. OCM-2 and MODIS derived chl a showed the maximum (6–8 μgl^?1) at nearshore waters and the minimum (0–1 μgl^?1) along the offshore waters. OCM-2 derived TSM imageries showed the maximum (50–60 mgl^?1) along the nearshore waters of Palk Strait and the moderate concentration (2–5 mgl^?1) was observed in the offshore waters. MODIS derived minimum TSM concentration (13.244 mgl^?1) was recorded along the offshore waters, while the maximum concentration of 15.78 mgl^?1 was found along the Kodiakarai region. The inter-comparison of OCM-2 and MODIS chl a data (R ² ?=?0.549, n?=?49, p?<?0.001, SEE?=?±0.117) indicate that MODIS data overestimates chl a concentration in the nearshore waters of the southern BoB compared to the OCM-2. The correlation between OCM-2 and MODIS-Aqua TSM data (R ² ?=?0.508, N?=?53, P?<?0.001 and SEE?=?±0.024) confirms that variation in the range of values measured by OCM-2 (2–60 mgl^?1) and the MODIS (13–16 mgl^?1) derived TSM values. Despite problems in range of measurements, persistent cloud cover etc., the launch of satellites like OCM-2 with relatively high spatial resolutions makes job easier and possible to monitor chl a distribution and sediment discharges on day to day basis in the southwest BoB.     

Frontal recession of Parkachik Glacier between 1971-2015, Zanskar Himalaya using remote sensing and field data

Parkachik Glacier is located in the Suru sub-basin of the Upper Indus River, Zanskar Himalaya. The Glacier has been analysed using Corona KH-4B (1971), Landsat-TM (1999), field survey (2015), Google Earth^TM (2015) and ASTER GDEM (2015) for frontal recession and area changes. Overall, from 1971 to 2015, the Glacier has retreated by 127 ± 0.09 m i.e. (0.75 ± 0.07%) at a rate of 2.9 ± 0.004 ma^?1 with a simultaneous decrease in area from 49.5 to 48.8 km² i.e. 740 ± 0.7 m² (1.5 ± 0.09%) at a rate of 74 ± 0.7 m²a^?1. However, during recent decade (1999–2015), the rate of glacier recession of 3.9 ± 0.004 ma^?1 with a corresponding area loss of 500 ± 0.74m² (1 ± 0.1%) was higher than the retreat rate of 2.3 ± 0.001 ma^?1 and an area loss of 240 ± 0.02m² (0.48 ± 0.08%) during 1971–1999. In the field, the evidences of glacier recession are present in the form of separated dead ice blocks from the main Glacier, recessional dumps/moraines, active ice calving activity and a small proglacial pond/lake at the terminus/snout of the Glacier. However, the recession over the studied period has been very slow and is controlled by its topographic configuration, particularly the large altitudinal range (6030–3620 m), almost northerly aspect and steep slope (average ~ 30°).     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

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.

     

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.

     

Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands

This study assessed the strength of Sentinel-2 multispectral instrument (MSI) derived Red Edge (RE) bands in estimating Leaf Area Index (LAI) and mapping canopy storage capacity (CSC) for hydrological applications in wattle infested ecosystems. To accomplish this objective, this study compared the estimation strength of models derived, using standard bands (all bands excluding the RE band) with those including RE bands, as well as different vegetation indices. Sparse Partial Least Squares (SPLSR) and Partial Least Squares Regression (PLSR) ensembles were used in this study. Results showed that the RE spectrum covered by the Sentinel-2 MSI satellite reduced the estimation error by a magnitude of 0.125 based on simple ratio (RE SR) vegetation indices from 0.157 m²· m^?2 based on standard bands, and by 0.078 m²· m^?2 based on red edge normalised difference vegetation (NDVI-RE). The optimal models for estimating LAI to map CSC were obtained based on the RE bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a). A root mean square error of prediction (RMSEP) of 0.507 m²· m^?2 a relative root mean square error of prediction (RRMSEP) of 11.3% and R² of 0.91 for LAI and a RMSEP of 0.246 m²/m² (RRMSEP = 7.9%) and R² of 0.91 for CSC were obtained. Overall, the findings of this study underscore the relevance of the new copernicus satellite product in rapid monitoring of ecosystems that are invaded by alien invasive species.