Subrata Nandy

Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data

Abstract Mangroves are active carbon sequesters playing a crucial role in coastal ecosystems. In the present study, aboveground biomass (AGB) was estimated in a 5-year-old Avicennia marina plantation (approximate area ≈ 190     ha ) of Indian Sundarbans using high-resolution satellite data in order to assess its carbon sequestration potential. The reflectance values of each band of LISS IV satellite data and the vegetation indices, viz., normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), and transformed difference vegetation index (TDVI), derived from the satellite data, were correlated with the AGB. OSAVI showed the strongest positive linear relationship with the AGB and hence carbon content of the stand. OSAVI was found to predict the AGB to a great extent ( r 2 = 0.72 ) as it is known to nullify the background soil reflectance effect added to vegetation reflectance. The total AGB of the entire plantation was estimated to be 236 metric tons having a carbon stock of 54.9 metric tons, sequestered within a time span of 5 years. Integration of this technique for monitoring and management of young mangrove plantations will give time and cost effective results.

Comparing tree diversity and population structure between a traditional agroforestry system and natural forests of Barak valley, Northeast India

We studied population structure, composition and diversity in a traditional Indian agroforestry system, called paan jhum, in comparison to natural forests of the Barak valley, Assam, northeast India. The phytosociological data from these forests were analysed quantitatively, to determine species richness, diversity, importance value, stand density and the basal area. The analysis showed that species richness and diversity were higher in paan jhum than in natural forests, in all three study sites. A total of 47, 37 and 48 tree species were recorded in paan jhum, compared with 35, 32 and 42 species in natural forests of the three study sites, respectively. Paan jhum had higher stand density (790, 934 and 763) and basal area (74.05, 41.60 and 55.88 m2 ha−1), whereas natural forests had lower stand density (775, 865 and 522) and basal area (68.75, 40.50 and 48.04 m2 ha−1) in all the study sites, respectively. An F-test showed significant differences in the variance in species richness, basal area and the stand density at 95% confidence level in the two forest categories. Paan jhum might become a component of a forested landscape that is valued for contributing to resource production, other ecosystem services and biodiversity conservation.

Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms:

Forests sequester large quantity of carbon in their woody biomass and hence accurate estimation of forest biomass is extremely crucial. The present study aims at combining information from spaceborne LiDAR (ICESat/GLAS) and high resolution optical data to estimate forest biomass. Estimation of aboveground biomass (AGB) at ICESat/GLAS footprint level was done by integrating data from multiple sensors using two regression algorithms, viz. random forest (RF) and support vector machine (SVM). The study used forest height and canopy return ratio (rCanopy) for determination of effective size of ICESat/GLAS footprints for field data collection. The forest height was predicted with root mean square error (RMSE) of 1.35 m. The study showed that six most important parameters derived from LiDAR, and passive optical data were able to explain 78.7% (adjusted) variation in the observed AGB with an RMSE of 13.9 Mg ha–1. It was also observed that 15 most important parameters were able to explain 83% (adjusted) variation in the observed AGB. It was found that SVM regression algorithm explained 88.7% of variation in AGB with an RMSE of 13.6 Mg ha–1 on the combined datasets while RF regression algorithm explained 83.5% of variation in AGB with an RMSE of 20.57 Mg ha–1. The study demonstrated that RF regression algorithm performs equally well on datasets irrespective of the correlation of underlying variables with the predicted variable whereas SVM regression was found to perform well on those datasets which had a subset of underlying variables that are correlated with the predicted variable. The study highlighted that sensor integration approach is more accurate than single sensor approach in predicting the AGB.

Neural network-based modelling for forest biomass assessment

ABSTRACT Forest biomass is an important parameter for assessing the status of forest ecosystems. In the present study, forest biomass was assessed by integrating remotely-sensed satellite data and field inventory data using an artificial neural network (ANN) technique in Barkot forest, Uttarakhand, India. Spectral and texture variables were derived from Resourcesat-1 (RS1) LISS-III (Linear Imaging Self-Scanning Sensor) data of April 24, 2013. ANN was used for finding the relation of spectral and texture variables to field-measured biomass. The top 10 variables, namely shortwave infrared (SWIR) band reflectance, near infrared (NIR) band reflectance, normalized difference vegetation index (NDVI), difference vegetation index (DVI), green band contrast, green band variance, SWIR band contrast, NIR band dissimilarity, SWIR band second angular moment, and red band mean, were selected for generating a multiple linear regression model to predict the biomass. The predicted biomass showed a good relationship (R2 = 0.75 and root mean square error (RMSE) = 85.32 Mg ha−1) with field-measured biomass. The model was validated yielding R2 = 0.74 and RMSE = 93.41 Mg ha−1. The results showed that RS1 LISS-III satellite data have good capability to estimate forest biomass, and the ANN technique can be used to enhance the scope of biomass estimation with a minimum number of spectral and texture variables.

Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data

SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

Trends of sea level in the Bay of Bengal using altimetry and other complementary techniques

Abstract In the present study, the different components of sea level (sea surface height, halosteric height, and thermosteric height) were estimated in the Bay of Bengal during 1993–2010 (Altimeter) and 2004–2010 (GRACE, Argo and Altimeter). The altimetry-based sea level (sea surface height) data showed a generally positive trend of sea level rise during 1993–2010 in the Bay of Bengal. The thermal expansion of ocean volume and discharge of water mass into the bay may be responsible for such changes. The temporal pattern of sea level variation derived from altimeter observations was compared with ocean mass and steric signals derived from GRACE and Argo data. It was observed that the sum of steric sea level and the ocean mass components has a positive trend of 6.26 ± 1.29 mm/yr, which was in conformity with the total sea level rise of 4.36 ± 1.45 mm/yr estimated from altimeter data, within a 95 percent confidence interval, during 2004–2010.

Remote Sensing-Based Forest Biomass Assessment in Northwest Himalayan Landscape

Forests cover around one-third of the global land cover (4.03 billion hectares) (FAO 2010; Pan et al. 2013) and are among the richest ecosystems in terms of biological and genetic diversity (Kohl et al. 2015). Forests are considered as reservoirs of carbon, and it is stored as biomass (phytomass). The total amount of above- and belowground organic matter of both living and dead plant parts is called biomass (FAO 2005). Net primary productivity (NPP) is majorly accumulated as biomass. Around two-thirds (262.1 PgC) of the global terrestrial biomass is stored by the tropical forests (Pan et al. 2013; Negron-Juarez et al. 2015). Therefore, forests act as one of the keystones of the global carbon cycle and play a vital role in designing the mitigating strategies for climate change and reducing the emission of greenhouse gases. Hence, forest biomass estimation is useful in quantifying the carbon stock, carbon emissions due to forest degradation and disturbances, carbon budget, productivity, forest planning and management and policy-making (Caputo 2009). Biomass monitoring in regular interval is utmost necessary for understanding the nature (source/sink) of the forest (Kushwaha et al. 2014). In addition, forests are vital sources of livelihood and economic development of any country (Kohl et al. 2015). Forest ecosystems offer numerous goods (timber, fodder, food, etc.) and ecological services (MEA 2005).

CO2 Flux Tower and Remote Sensing: Tools for Monitoring Carbon Exchange over Ecosystem Scale in Northwest Himalaya

Carbon accounts for nearly half of the total dry mass of all living things (Schlesinger 1991). Forests are the major reservoir of terrestrial carbon on the Earth and play vital role in balancing the steadily rising concentration of carbon dioxide in the atmosphere owing to fossil fuel and biomass burning (IPCC 2005). A forest is called the sink or source of carbon dioxide depending on net removal or release of carbon dioxide into the atmosphere. India supports a vast mosaic of forest ecosystems and contributes significantly to its carbon dynamics (Chhabra and Dadhwal 2004). Accurate quantification of carbon fluxes of forest ecosystems at local, regional, and global scales is necessary for understanding the feedback mechanism between the terrestrial biosphere and the atmosphere. Deep insight into the role of forests in the regional carbon cycle is critical for taking policy-oriented decisions on forest-based initiatives to mitigate global warming.