We present annual estimates of the net ecosystem exchange (NEE) of carbon dioxide (CO₂) accumulated over one annual cycle (April 2012 to March 2013) in the world’s largest mangrove ecosystem, Sundarbans (India), using the eddy covariance method. An eddy covariance flux tower was established in April 2012 to study the seasonal variations of carbon dioxide fluxes due to soil and vegetation-atmosphere interactions. The half-hourly maximum of the net ecosystem exchange (NEE) varied from −6 µmol·m⁻²·s⁻¹ during the summer (April to June 2012) to −10 µmol·m⁻²·s⁻¹ during the winter (October to December 2012), whereas the half-hourly maximum of H₂O flux varied from 5.5 to 2.5 mmol·m⁻²·s⁻¹ during October 2013 and July 2013, respectively. During the study period, the study area was a carbon dioxide sink with an annual net ecosystem productivity (NEP = −NEE) of 249 ± 20 g·C m⁻²·year⁻¹. The mean annual evapotranspiration (ET) was estimated to be 1.96 ± 0.33 mm·day⁻¹. The gap-filled NEE was also partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (R_e). The total GPP and R_e over the study area for the annual cycle were estimated to be1271 g C m⁻²·year⁻¹ and 1022 g C m⁻²·year⁻¹, respectively. The closure of the surface energy balance accounted for of about 78% of the available energy during the study period. Our findings suggest that the Sundarbans mangroves are currently a substantial carbon sink, indicating that the protection and management of these forests would lead as a strategy towards reduction in carbon dioxide emissions.

Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure.

Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha⁻¹. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R² of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km² in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.