Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation.

BIOMASS AND CARBON STOCK ESTIMATION OF COASTAL MANGROVES AT HAI PHONG COAST USING REMOTE SENSING AND FIELD INVESTIGATION- BASED DATA

Estimated results of 17 plots evenly distributed across study sites showed that the amount of tree carbon stocks was significantly lower than in soil, normally ranged from 9.9 ÷ 29.55 (tons ha-1) and in contrast by using the Walkley-Black method, these total soil organic carbon were in the range from 81.76 ÷ 323.83 tons ha-1 (average = 161.47±15.85 tons ha-1), which pointed out strong relationship between tree density and soil organic carbon in the study areas.

Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review

Climate change is acknowledged as a global threat to the environment and human well-being. Forest ecosystems are a significant factor in this regard as they act both as a sink and a source of carbon. Forest carbon evaluation has received more attention after the Paris Agreement. Pakistan has 5.1% forest cover of its total land area, which comprises nine forest types. This study covers the studies conducted on above-ground biomass and carbon stock in various forest types of Pakistan. Most of the studies on biomass and carbon stock estimation have been conducted during 2015–2020. The non-destructive method is mostly followed for carbon stock estimation, followed by remote sensing. The destructive method is used only for developing allometric equations and biomass expansion factors. The information available on the carbon stock and biomass of Pakistan forest types is fragmented and sporadic. Coniferous forests are more important in carbon sequestration and can play a vital role in mitigating climate change. Pakistan is a signatory of the Kyoto Protocol and still lacks regional and national level studies on biomass and carbon stock, which are necessary for reporting under the Kyoto Protocol. This study will help researchers and decision-makers to develop policies regarding Reducing Emissions from Deforestation and forest Degradation (REDD+), conservation, sustainable forest management and enhancement of forest carbon stocks

Stok Karbon pada Ekosistem Lamun di Pulau Kemujan dan Pulau Bengkoang Taman Nasional Karimunjawa

Lamun memiliki kemampuan menyimpan karbon di dalam biomassanya. Penelitian ini bertujuan untuk mengetahui nilai estimasi simpanan karbon dalam biomassa pada vegetasi lamun di Pulau Kemujan serta Pulau Bengkoang, Taman Nasional Karimunjawa. Pengambilan data menggunakan metode purposive sampling dan metode Seagrass Watch dengan mempertimbangkan kondisi lamun di lokasi tersebut. Pengukuran estimasi karbon dilaksanakan di Laboratorium Ilmu dan Nutrisi Pakan FPP Undip menggunakan metode Loss on Ignition dengan prinsip pengabuan. Jenis lamun yang ditemukan di Pulau Kemujan yaitu Enhalus acoroides, Thalassia hemprichii, dan Cymodocea serrulata, dan pada Pulau Bengkoang ditemukan lamun jenis Thalassia hemprichii, Cymodocea rotundata, Halophila ovalis, dan Enhalus acoroides. Nilai biomassa bawah substrat dan atas substrat pada Stasiun I Pulau Kemujan (3104,5 gbk/m2 dan 1868 gbk/m2) menunjukkan nilai yang lebih besar dibandingkan nilai biomassa bawah substrat dan atas substrat pada Stasiun II Pulau Bengkoang (714,25 gbk/m2 dan 534,25 gbk/m2). Nilai estimasi simpanan karbon pada Stasiun I yaitu 138,47 – 1533,28 gC/m2 dan pada Stasiun II yaitu 17,02– 498,31 gC/m2. Mayoritas nilai karbon lebih tinggi pada jaringan lamun bawah substrat. Nilai estimasi simpanan karbon sedimen pada Stasiun I yaitu 52,60–339,81 gC/m2 dan 86,85–1329,08 gC/m2 pada Stasiun II. Penelitian ini dapat memberikan informasi mengenai fungsi lain ekosistem lamun yaitu sebagai penyerap karbon sehingga dapat dijadikan edukasi kepada masyarakat umum untuk melestarikan ekosistem lamun sebagai ekosistem yang dapat berperan penting dalam mengatasi masalah emisi gas rumah kaca dan pemanasan global. Seagrass have ability to store carbon mass in their biomass. The aim of this research is to find out the value of carbon stock on seagrass biomass in Kemujan Island and Bengkoang Island seagrass vegetation. The research was retrieval in purposive sampling method and collected seagrass vegetation data by using Seagrass Watch. Measurement of carbon stock estimation held in INP FPP Undip Laboratory by using Loss on Ignition method. The type of seagrass found in Kemujan Island were Enhalus acoroides, Thalassia hemprichii, and Cymodocea serrulata, meanwhile in Bengkoang Island there were found Thalassia hemprichii, Cymodocea rotundata, Halophila ovalis, and Enhalus acoroides. The value of below ground and above ground biomass in Station I Kemujan Island (3104,5 gbk/m2 dan 1868 gbk/m2) is higher than the value of below ground and above ground biomass in Station II Bengkoang Island (714,25 gbk/m2 and 534,25 gbk/m2). Carbon stock estimation value in Station I is 138,47–1533,28 gC/m2 and 17,02–498,31 gC/m2 in Station II. Most of carbon stock value is higher in below ground seagrass tissue. The value of carbon stock estimation of sediment in Station I is 52,60–339,81 gC/m2 and 86,85–1329,08 gC/m2 in Station II. The research gives information about another function of seagrass, as carbon absorber and can be as education for public to conserve seagrass ecosystem and has important role in resolving greenhouse gas emission and global warming.