Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System

Regional mangrove vegetation carbon stocks predicted integrating UAV-LiDAR and satellite data

Using satellite RS data predicting mangrove vegetation carbon stock (MVC) is the popular and efficient approach at a large scale to protect mangroves and promote carbon trading. Satellite data have performed poorly in predicting MVC due to saturation issues. UAV-LiDAR data overcomes these limitations by providing detailed structural vegetation information. However, how to cross-scale integration of UAV-LiDAR and satellite RS data and the selection of features and machine learning methods hampered the practitioner in making a lightweight but efficient model to predict the MVC. Our study integrated UAV-LiDAR, Sentinel-1, and Sentinel-2 to extract spectral, structural, and textural features at the regional scale. We estimated the influences of different combinations between three vegetation features and machine learning methods (Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Regression Tree (GBDT), and Extreme Gradient Regression Tree (XGBOOST)) on the results of MVC prediction, and constructed a framework for estimating mangrove vegetation aboveground (ACG) and belowground (BCG) carbon storage in Zhanjiang, the largest mangrove area of China. Our research shows: 1) Compared to using satellite remote sensing (RS), integrating UAV and satellite RS data and fusing multiple vegetation features significantly improved the accuracy of mangrove vegetation carbon stock (MVC) predictions. 2) Structural features, particularly canopy height retrieved from UAV and satellite RS, are essential indicators for predicting MVC. Combined with spectral and structural features, regional MVC was precisely predicted. 3)Although the influence of different machine learning methods on MVC prediction was not significant, XGBOOST demonstrated relatively high precision. We recommend that mangrove practitioners integrate UAV and satellite RS data to predict MVC at a regional scale. Importantly, governments should prioritize the application of UAV-LiDAR in forestry monitoring and establish a long-term mangrove monitoring database to aid in estimating blue carbon resources and promoting blue carbon trading.

Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review

Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research. Using a systematic literature review approach, we reviewed 105 remote sensing-based peer-reviewed articles (1990 - June 2023). Despite their high mangrove extent, there was a paucity of studies from Myanmar, Bangladesh, and Papua New Guinea. The most frequently used sensor was Sentinel-2 MSI, accounting for 14.5 % of overall usage, followed by Landsat 8 OLI (11.5 %), ALOS-2 PALSAR-2 (7.3 %), ALOS PALSAR (7.2 %), Landsat 7 ETM+ (6.1 %), Sentinel-1 (6.7 %), Landsat 5 TM (5.5 %), SRTM DEM (5.5 %), and UAV-LiDAR (4.8 %). Although parametric methods like linear regression remain the most widely used, machine learning regression models such as Random Forest (RF) and eXtreme Gradient Boost (XGB) have become popular in recent years and have shown good accuracy. Among a variety of attributes estimated, below-ground mangrove blue carbon and the valuation of carbon stock were less studied. The variation in carbon sequestration potential as a result of location, species, and forest type was widely studied. To improve the accuracy of blue carbon measurements, standardized/coordinated and innovative methodologies accompanied by credible information and actionable data should be carried out. Technical monitoring (every 2-5 years) enhanced by remote sensing can provide accurate and precise data for sustainable mangrove management while opening ventures for voluntary carbon markets to benefit the environment and local livelihood in developing countries in the Asia-Pacific region.

China's conservation and restoration of coastal wetlands offset much of the reclamation-induced blue carbon losses

China's coastal wetlands have experienced large losses and gains with rapid coastal reclamation and restoration since the end of the 20th century. However, owing to the difficulties in mapping soil organic carbon (SOC) in blue carbon stocks of coastal wetlands on a national scale, little is known about the spatial pattern of SOC stock in China's coastal wetlands and the loss and gain of SOC stock following coastal reclamation, conservation, and restoration over the past decades. Here, we developed a SOC stock map in China's coastal wetlands at 30 m spatial resolution, analyzed the spatial variability and driving factors of SOC stocks, and finally estimated SOC losses and gains due to coastal reclamation and wetland management from 1990 to 2020. We found that the total SOC stocks in China's coastal wetlands were 77.8 Tg C by 2020 with 3.6 Tg C in mangroves, 8.8 Tg C in salt marshes, and 65.4 Tg C in mudflats. Temperature, rainfall, and seawater salinity exerted the highest relative contributions to SOC spatial variability. The spatial trend of SOC density gradually decreased from south to north except for Liaoning province, with the lowest density in Shandong province. About 24.9% (19.4 Tg C) of SOC stocks in China's coastal wetlands were lost due to high-intensity reclamation, but SOC stock gained from conservation and restoration offset the reclamation-induced losses by 58.2% (11.3 Tg C) over the past three decades. These findings demonstrated the great potential of conservation and restoration of coastal wetlands in reversing the loss trend of blue carbon and contributing to the mitigation of climate change toward carbon neutrality. Our study provides significant spatial insights into the stocks, sequestration, and recovery capacity of blue carbon following rapid urbanization and management actions, which benefit the progress of global blue carbon management.

Combining Sample Plot Stratification and Machine Learning Algorithms to Improve Forest Aboveground Carbon Density Estimation in Northeast China Using Airborne LiDAR Data

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R2 = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBoost algorithm can effectively improve the estimation accuracy of forest AGC. Integrating this strategy with national forest inventory could help improve the monitoring of forest carbon stock over large areas.

Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV” + “forest”. This result is even more surprising when compared with similar research for “UAV” + “agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely, (1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

Remote mapping of foodscapes using sUAS and a low cost BG-NIR sensor

The assessment of landscape condition for large herbivores, also known as foodscapes, is fast gaining interest in conservation and landscape management programs worldwide. Although traditional approaches are now being replaced by satellite imagery, several technical issues still need to be addressed before full standardization of remote sensing methods for these purposes. We present a low-cost method, based on the use of a modified blue/green/near-infrared (BG-NIR) camera housed on a small-Unmanned Aircraft System (sUAS), to create foodscapes for a generalist Mediterranean ungulate: the Iberian Ibex (Capra pyrenaica) in Northeast Spain. Faecal cuticle micro-histological analyses were used to assess the dietary preferences of ibexes and then individuals of the most common plant species (n = 19) were georeferenced to use as test samples. Because of the seasonal pattern in vegetation activity, based on the NDVI (Smooth term _Month = 21.5, p-value < .01, R² = 43%, from a GAM), images were recorded in winter and spring to represent contrasting vegetation phenology using two flight heights above ground level (30 and 60 m). Additionally, the range of image pixel sizes was 3.5-30 cm with the smallest pixel size representing the highest resolution. Boosted Trees were used to classify plant taxa based on spectral reflectance and create a foodscape of the study area. The number of target species, the sampling season, the height of flight and the image resolution were analysed to determine the accuracy of mapping the foodscape. The highest classification error (70.66%) was present when classifying all plant species using a 30 cm pixel size from acquisitions at 30 m height. The lowest error (18.7%), however, was present when predicting plants preferred by ibexes, at 3.5 cm pixel size acquired at 60 m height. This methodology can help to successfully monitor food availability and seasonality and to identify individual species.

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R2 and RMSE in a suggested GA fitness function. We calculated R2, RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model’s stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.