Estimating mangrove canopy height and above-ground biomass in the Everglades National Park with airborne LiDAR and TanDEM-X data

Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal mangrove forest of the Everglades National Park (ENP) provides an ideal location for studying these processes, as harmful human activities are minimal. We estimated mangrove canopy height and AGB in the ENP using Airborne LiDAR/Laser (ALS) and TanDEM-X (TDX) datasets acquired between 2011 and 2013. Analysis of both datasets revealed that mangrove canopy height can reach up to ~25 m and AGB can reach up to ~250 Mg·ha-1. In general, mangroves ranging from 9 m to 12 m in stature dominate the forest canopy. The comparison of ALS and TDX canopy height observations yielded an R2 = 0.85 and Root Mean Square Error (RMSE) = 1.96 m. Compared to a previous study based on data acquired during 2000-2004, our analysis shows an increase in mangrove stature and AGB, suggesting that ENP mangrove forests are continuing to accumulate biomass. Our results suggest that ENP mangrove forests have managed to recover from natural disturbances, such as HurricaneWilma

Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

Mangroves are ecologically and economically important forested wetlands with the highest carbon (C) density of all terrestrial ecosystems. Because of their exceptionally large C stocks and importance as a coastal buffer, their protection and restoration has been proposed as effective mitigation strategy for climate change. The inclusion of mangroves in mitigation strategies requires the quantification of C stocks (both above and belowground) and changes to accurately calculate emissions and sequestration. A growing number of countries are becoming interested in using mitigation initiatives, such as REDD+, in these unique coastal forests. However, it is not yet clear how methods to measure C traditionally used for other ecosystems can be modified to estimate biomass in mangroves with the precision and accuracy needed for these initiatives. Airborne lidar (ALS) data has often been proposed as the most accurate way for larger-scale assessments but, the application of ALS for coastal wetlands is scarce, primarily due to a lack of contemporaneous ALS and field measurements. Here, we evaluated the variability in field and lidar-based estimates of aboveground biomass (AGB) through the combination of different local and regional allometric models and standardized height metrics that are comparable across spatial resolutions and sensor types. The end result being a simplified approach for accurately estimating mangrove AGB at large-scales and determining the uncertainty by combining multiple allometric models. We then quantified wall-to-wall aboveground biomass stocks of a tall mangrove forest in the Zambezi Delta, Mozambique. Our results indicate that the Lidar H100 height metric correlates well with AGB estimates, with R2 between 0.80 and 0.88 and RMSE of 33% or less. When comparing lidar H100 AGB derived from three allometric models, mean AGB values range from 192 Mg. ha-1 up to 252 Mg. ha-1. We suggest the best model to predict AGB was based on the East Africa specific allometry and a power based regression that used Lidar H100 as the height input with a R2 of 0.85 and a RMSE of 122 Mg.ha-1 or 33%. The total AGB of the lidar inventoried mangrove area (6654 ha) was 1,350,902 Mg with a mean AGB 203 Mg. ha-1. Because the allometry suggested here was developed using standardized height metrics, it is recommended that the models can generate AGB estimates using other remote sensing instruments that are more readily accessible over other mangrove ecosystems on a large scale, and as part of future carbon monitoring efforts in mangroves

Comprehensive comparison of airborne and spaceborne SAR and LiDAR estimates of forest structure in the tallest mangrove forest on earth

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quereet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions

Remote sensing of seasonal changes and disturbances in mangrove forest: a case study from South Florida

Knowledge of the spatial and temporal changes caused by episodic disturbances and seasonal variability is essential for understanding the dynamics of mangrove forests at the landscape scale, and for building a baseline that allows detection of the effects of future environmental change. In combination with LiDAR data, we calculated four vegetation indices from 150 Landsat TM images from 1985 to 2011 in order to detect seasonal changes and distinguish them from disturbances due to hurricanes and chilling events in a mangrove-dominated coastal landscape. We found that normalized difference moisture index (NDMI) performed best in identifying both seasonal and event-driven episodic changes. Mangrove responses to chilling and hurricane events exhibited distinct spatial patterns. Severe damage from intense chilling events was concentrated in the interior dwarf and transition mangrove forests with tree heights less than 4 m, while severe damage from intense hurricanes was limited to the mangrove forest near the coast, where tree heights were more than 4 m. It took 4–7 months for damage from intense chilling events and hurricanes to reach their full extent, and took 2–6 yr for the mangrove forest to recover from these disturbances. There was no significant trend in the vegetation changes represented by NDMI over the 27-yr period, but seasonal signals from both dwarf and fringe mangrove forests were discernible. Only severe damage from hurricanes and intense chilling events could be detected in Landsat images, while damage from weak chilling events could not be separated from the background seasonal change

Measuring Mangrove Carbon Loss and Gain in Deltas

Demand for mangrove forest resources has led to a steady decline in mangrove area over the past century. Land conversions in the form of agriculture, aquaculture and urbanization account for much of the deforestation of mangrove wetlands. However, natural processes at the transition zone between land and ocean can also rapidly change mangrove spread. In this study, we applied a robust field-based carbon inventory and new structural and temporal remote sensing techniques to quantify the magnitude and change of mangrove carbon stocks in major deltas across Africa and Asia. From 20002016, approximately 1.6% (12 270 ha) of the total mangrove area within these deltas disappeared, primarily through erosion and conversion to agriculture. However, the rapid expansion of mangroves in some regions during this same period resulted in new forests that were taller and more carbon-dense than the deforested areas. Because of the rapid vertical growth rates and horizontal expansion, new mangrove forests were able to offset the total carbon losses of 5 332 843 Mg C by 44%. Each hectare of new mangrove forest accounted for 84% to 160% of the aboveground carbon for each hectare of mangrove forest lost, regardless of the net change in mangrove area. Our study highlights the significance of the natural dynamics of erosion and sedimentation on carbon loss and sequestration potential for mangroves over time. Areas of naturally regenerating mangroves will have a much larger carbon sequestration potential if the rate of mangrove deforestation of taller forests is curbed

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

Assessing mangrove canopy heights in Myanmar using GEDI & Sentinel-2 for effective monitoring

Mangrove forests are crucial ecosystems that store over three times as much carbon per hectare as terrestrial tropical forests (Donato et al., 2011) and host an essential role in regulating global and local climate systems (Estoque et al.2018). Alarmingly, the global mangrove area shrank by 50% between 1997 and 2016, with the most significant losses occurring in Southeast Asia (Estoque et al., 2018; Romañach et al., 2018). Projections suggest that mangroves could vanish entirely within the next century (Polidoro et al., 2010), highlighting the urgent need for accurate mapping of their structural and spatial characteristics to aid conservation and restoration efforts. While readily available multispectral satellite data like Sentinel-2 provide insights into mangrove coverage, they offer limited information on three-dimensional (3D) structural characteristics such as Canopy Height (CH). Traditional methods for 3D mapping, such as Airborne Lidar Surveying (ALS) and Synthetic Aperture Radar (SAR), are expensive and geographically unscalable. Spaceborne Lidar missions, and specifically the launch of the Global Ecosystem Dynamics Investigation (GEDI), offer a new opportunity to obtain 3D mangrove canopy data. As GEDI samples only 4% of the Earth, it is often fused with contiguous imagery like Landsat and Sentinel-2 to produce Global Canopy Height Maps (CHMs), albeit with limitations at local scales and for non-standard forest structures like mangroves (Potapov, 2021; Lang et al., 2022). This study leverages a Random Forest (RF) algorithm to combine Sentinel-2 and GEDI data for producing a contiguous Mangrove CHM for a restored local region in Myanmar for the year 2019. Field heights were obtained from Worldview International, the project facilitators in 32 sample field plots (Vanniarachchy and Jayakody, 2020). Three models, trained on data sets from 2019 and 2020, were tested against a GEDI validation set and the field heights. The Relative Height (Rh) at the 60th Percentile Waveform Energy Return (Rh60) from GEDI's Level 2A product (that provides elevation and height metrics) was identified as the best predictor of field heights out of other Rh metrics, yielding an R2 value of 0.24, a Mean Error (ME) of 0.28 m, and a Root Mean Squared Error (RMSE) of 0.37m. These results were compared with 3 baseline Global CHMs by Potapov et al. (2021), Lang et al. (2022), and Simard et al. (2019). These comparisons revealed that the CH predictions by Lang et al. (2022) had the highest ME and RMSE, followed by that of Simard et al. (2019) and lastly Potapov et al. (2021). Further, vertical structural analysis using GEDI's L2B (that provides biophysical metrics) product indicated that the mangroves studied are vertically uniform, unlike typical forests. This could explain why the mean Rh metrics provide a better approximation of true CH as opposed to the commonly used Rh90+ metrics in the baseline Global C estimates are overestimated by over 50%, and as a consequence Above Ground Biomass (AGB) estimates could be grossly inaccurate in short stature (<3m) mangroves if current Global CHMs are used, emphasizing the wider need for their local calibration. These findings have a direct impact on estimating National Carbon Stocks, contribute to the accreditation of community-based forest conservation and afforestation projects, and aid wider efforts in understanding and mitigating climate change