Quick Aboveground Carbon Stock Estimation of Densely Planted Shrubs by Using Point Cloud Derived from Unmanned Aerial Vehicle

Abstract

Carbon sink trading is an important aspect of carbon trading in China, and can have important significance in offsetting carbon emissions and improving ecological compensation. The use of unmanned aerial vehicles (UAVs) offers new opportunities for shrub carbon sink and accounts as a substitute for time-consuming and expensive plot investigations to estimate the carbon sink by using the aboveground carbon stock monitored by UAV. However, the UAV-based estimation of the aboveground carbon stock of densely planted shrubs still faces certain challenges. The specific objectives of this research are as follows: (1) to test the statistical relationship between the aboveground carbon stock and volume of a densely planted shrub belt, and (2) to develop a model to estimate aboveground carbon stock by monitoring the volume of the densely planted shrub belt using a UAV. The study showed that (i) the aboveground carbon stock would increase with the increase in the volume of the shrub belt, (ii) an estimation model of the aboveground carbon stock of the densely planted shrub belt was developed ( R 2 = 0.89 , P < 0.01 ), and (iii) the validation assessment to estimate aboveground carbon stock by using the UAV-based estimation model produced a coefficient of determination of R2 = 0.74 and an overall root mean square error of 18.79 kg CO2e. Good prediction ability of the model was determined using leave-one-out cross-validation (LOOCV). This output information is valuable for the design of operations in the framework of precise carbon-sink accounting of shrubs. In addition, a method using an UAV was developed and validated for the quick estimation of aboveground carbon stock for densely planted shrubs, thereby providing a potential alternative to time-consuming and expensive plot investigations of aboveground carbon-stock accounting, which is necessary for shrub projects in the carbon trading market in China.

Above Ground Biomass Estimation of Syzygium aromaticum using structure from motion (SfM) derived from Unmanned Aerial Vehicle in Paninggahan Agroforest Area, West Sumatra

“…Teknologi pesawat tanpa awak (Unmanned Aerial Vehicle) dan sistem informasi geografis telah mempercepat perkembangan metode dengan biaya dan waktu yang lebih efisien untuk melakukan pengukuran AGB (Wikantika, 2009;Ota et al 2015). Beberapa penelitian menggunakan metode ini menunjukan hasil yang cukup baik pada pengukuran AGB di beberapa tipe vegetasi (Fernandes et al 2020;Hashem, 2019;Zhang, 2019;Li et al 2019;Warfield dan Leon, 2019). Oleh karena itu, penelitian ini bertujuan untuk melakukan perhitungan individu cengkeh dengan cepat dan akurat serta mengetahui biomassa diatas permukaan tanah (AGB) cengkeh dengan menggunakan pesawat tanpa awak (UAV) di Kawasan Agroforestri Paninggahan, Solok, Sumatera Barat.…”

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

“…The images were collected with an 80% overlap to account for corrupted images affected by the wind speed. High imagery overlap (80%) was selected to provide more details of the vegetation geometry and to minimize the vegetation height error [ 59 , 60 , 61 ]. Imagery products were georeferenced and radiometrically corrected and mosaicked using Pix4Dmapper software (version 4.3).…”

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

“…Largescale mapping of forest carbon stocks requires validation and calibration of individual tree carbon stocks. Studies estimating biomass carbon stocks using Global Ecosystem Dynamics Investigation GEDI, ICESat-2, GLAS, optical satellite imagery, SAR data, and airborne laser scanning (ALS) data, either alone or in combination with parametric and non-parametric techniques, have been developed at a range of spatial resolutions, forest types, and ecosystems [8,[10][11][12][13]. In addition, machine learning techniques can be combined to bring accurate measurements of forest canopy height and carbon stocks to large-scale, more efficient, and operational forest mitigation assessments.…”