India is experiencing energy crisis and a widening gap between energy supply and demand. The country is, however, endowed with considerable, commercially and technically available renewable resources, from which surplus agro-biomass is of great importance and a relatively untapped resource. In the policy making process, knowledge of existing biomass use, degree of social reliance, and degree of biomass availability for energy production is unequivocal and pre-conditional. Field observations, documentation, and fill-in sheet tools were used to investigate the potential of biomass resources and the existing domestic, commercial, and industrial uses of biomass in selected Indian states. To do so, a team of field observers/supervisors visited three Indian states namely: Maharashtra (MH), Madhya Pradesh (MP), and Tamil Nadu (TN). Two districts from each state were selected to collect data regarding the use of biomass and the extent of biomass availability for energy production. In total, 471 farmers were interviewed, and approximately 75 farmers with various land holdings have been interviewed in each district. The existing uses of biomass have been documented in this survey study and the results show that the majority of biomass is used as fodder for domestic livestock followed by in-site ploughing, leaving trivial surplus quantities for other productive uses. Biomass for cooking appeared to be insignificant due to the availability and access to Liquefied Petroleum Gas (LPG) cylinders in the surveyed districts. Opportunities exist to utilize roadside-dumped biomass, in-site burnt biomass, and a share of biomass used for ploughing. The GIS-based maps show that biomass availability varies considerably across the Taluks of the surveyed districts, and is highly dependent on a number of enviromental and socio-cultural factors. Developing competitive bioenergy market and enhancing and promoting access to more LPG fuel connections seem an appropriate socio-economic and environmental approach to reduce the use of biomass for indoor cooking and increasing the share of surplus biomass for energy production.

Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme—or LAMP—for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution.

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.

Participatory forest monitoring has been promoted as a means to engage local forest-dependent communities in concrete climate mitigation activities as it brings a sense of ownership to the communities and hence increases the likelihood of success of forest preservation measures. However, sceptics of this approach argue that local community forest members will not easily attain the level of technical proficiency that accurate monitoring needs. Thus it is interesting to establish if local communities can attain such a level of technical proficiency. This paper addresses this issue by assessing the robustness of biomass estimation models based on air-borne laser data using models calibrated with two different field sample designs namely, field data gathered by professional forester teams and field data collected by local communities trained by professional foresters in two study sites in Nepal. The aim is to find if the two field sample data sets can give similar results (LiDAR models) and whether the data can be combined and used together in estimating biomass. Results show that even though the sampling designs and principles of both field campaigns were different, they produced equivalent regression models based on LiDAR data. This was successful in one of the sites (Gorkha). At the other site (Chitwan), however, major discrepancies remained in model-based estimates that used different field sample data sets. This discrepancy can be attributed to the complex terrain and dense forest in the site which makes it difficult to obtain an accurate digital elevation model (DTM) from LiDAR data, and neither set of data produced satisfactory results. Field sample data produced by professional foresters and field sample data produced by professionally trained communities can be used together without affecting prediction performance provided that the correlation between LiDAR predictors and biomass estimates is good enough.