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

2019 ◽  
Vol 11 (24) ◽  
pp. 2914
Author(s):  
Xueyan Zhang
Keyword(s):  
Carbon Stock ◽  
Carbon Sink ◽  
Coefficient Of Determination ◽  
Good Prediction ◽  
Carbon Trading ◽  
Estimation Model ◽  
Ecological Compensation ◽  
Prediction Ability ◽  
Aboveground Carbon ◽  
Output Information

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.

scholarly journals Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3330
Author(s):  
Xiandie Jiang ◽  
Guiying Li ◽  
Dengsheng Lu ◽  
Emilio Moran ◽  
Mateus Batistella
Keyword(s):  
Carbon Stock ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Coefficient Of Determination ◽  
Lidar Data ◽  
Carbon Density ◽  
Modis Data ◽  
Aboveground Carbon ◽  
Aboveground Carbon Density ◽  
Airborne Lidar Data

Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.

scholarly journals Cadangan Karbon pada Ekosistem Padang Lamun di Siantan Tengah Taman Wisata Perairan Kepulauan Anambas

2020 ◽  
Vol 24 (1) ◽  
pp. 45-54
Author(s):  
Muhammad Al Rizky Ratno Budiarto ◽  
Johan Iskandar ◽  
Tri Dewi Kusumaningrum Pribadi
Keyword(s):  
Carbon Stock ◽  
Carbon Sink ◽  
Gravimetric Method ◽  
Total Carbon ◽  
Seagrass Beds ◽  
Thalassia Hemprichii ◽  
Enhalus Acoroides ◽  
Value Range ◽  
Seagrass Biomass ◽  
Cymodocea Rotundata

Secara global, ekosistem lamun dianggap sebagai penyerap karbon sehingga dapat berkontribusi terhadap mitigasi perubahan iklim. Penelitian bertujuan untuk mengetahui komposisi jenis, biomassa dan cadangan karbon pada komunitas padang lamun di perairan Siantan Tengah Taman Wisata Perairan (TWP) Kepulauan Anambas. Penelitian dilaksanakan pada bulan Agustus 2019 s.d Januari 2020. Uji kandungan karbon dilakukan dengan metode Welkley and Black sedangkan untuk mendapatkan biomassa menggunakan metode gravimetrik. Hasil penelitian menunjukkan bahwa terdapat tiga jenis lamun, yaitu Enhalus acoroides, Thalassia hemprichii, dan Cymodocea rotundata. Nilai biomassa lamun berkisar antara 171,89 – 275,68 gbk/m2 dan nilai cadangan karbon berada pada kisaran 51,89 – 80,66 gC/m2. Padang lamun di Siantan Tengah memiliki luas 130,45 ha, sehingga total Cadangan karbon pada ekosistem padang lamun di perairan Siantan Tengah diperkirakan 95,88 ton C. Penelitian ini membuktikan adanya kandungan karbon pada biomassa lamun sehingga dapat disimpulkan bahwa padang lamun berperan sebagai penyerap karbon (carbon sink).  Globally, seagrass ecosystems are considered as carbon sink so that it can contribute to climate change mitigation. This research aims to determine the species composition, biomass, and carbon stock in seagrass communities in Siantan Tengah Marine Tourism Park of Anambas Islands. The research was conducted in Agustus 2019 – January 2020.  The carbon content test was carried out by the Walkley and Black method while to obtain biomass using the gravimetric method. The result od study showed that there are three species of seagrasses, namely Enhalus acoroides, Thalassia hemprichii, and Cymodocea rotundata. Seagrass biomass value range 171,89 – 275,68 gbk/m2 and seagrass carbon stock value range 51,89 – 80,66 gC/m2. The area of seagrass beds in Central Siantan is 130,45 ha so that the total carbon stock estimated reach 95,88 tons C. This research proves the presence of carbon in the biomass of seagrass beds, so it can be concluded that seagrass beds act as carbon sinks.

scholarly journals ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY

2021 ◽  
Vol 18 (1) ◽  
pp. 103
Author(s):  
Bayu Elwanto Bagus Dewanto ◽  
Retnadi Heru Jatmiko
Keyword(s):  
Carbon Stock ◽  
Open Space ◽  
Exponential Model ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Empirical Modelling ◽  
Aboveground Carbon ◽  
Accuracy Test ◽  
Estimated Error ◽  
Total Carbon Stock

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.

scholarly journals ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 321-327
Author(s):  
P. Wicaksono ◽  
P. Danoedoro ◽  
U. Nehren ◽  
A. Maishella ◽  
M. Hafizt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Stepwise Regression ◽  
Species Level ◽  
Measured Parameter ◽  
Support Vector ◽  
Percent Cover ◽  
Svm Classification ◽  
Aboveground Carbon ◽  
Non Destructive

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

P1886Validation of the MB-LATER score prediction ability for recurrent atrial fibrillation after electrical cardioversion

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Marinkovic ◽  
N Mujovic ◽  
V Kovacevic ◽  
M Mihajlovic ◽  
L Vajagic ◽  
...  
Keyword(s):  
Univariate Analysis ◽  
Treatment Decision ◽  
Electrical Cardioversion ◽  
Good Prediction ◽  
Prediction Ability ◽  
Treatment Decision Making ◽  
C Statistic ◽  
Recurrent Atrial Fibrillation ◽  
First Recurrence ◽  
Af Recurrence

Abstract Introduction The MB-LATER score (Male, Bundle brunch block, Left atrium ≥47 mm, Type of AF [paroxysmal, persistent or long-standing persistent], and ER-AF=early recurrent AF during first three months) was originally developed for prediction of late AF recurrences post AF catheter ablation (CA-AF). Subsequently, the score has been internationally validated in multiple AF cohorts, showing a good prediction ability for recurrent AF post AF-CA. We assessed prediction ability of the MB-LATER score for recurrent AF after successful electrical cardioversion (ECV) of AF. Methods The retrospective study included a Serbian and Icelandic centre, enrolling patients post successful ECV of AF in the period between January 2014 and February 2016. Of 580 patients, 136 (23.4%) were excluded because incomplete data needed for the MB-LATER score calculation. AF episodes lasting ≤7 days before ECV were classified as paroxysmal AF, and the ER-AF component of the MB-LATER score was excluded from the analysis because of different clinical implications in the setting of ECV. The study outcome was defined as the time to first recurrence of AF post successful ECV. Patients post successful ECV were seen at 1 and 6 months post ECV and every 12 months thereafter. Results Among 444 patients (median age 68 years [IQR 60.0–74.6], 289 males [65.2%], 200 [45.0%] with non-paroxysmal AF. AF re-occurred in 283 patients (63.7%) after a median of 233.5 [IQR 44–366]) days post successful ECV. Patients with recurrent AF had significantly higher median MB-LATER score than those without (1 [IQR 1–2] vs. 2 [IQR 1–2], p<0.001). On univariate analysis, the MB-LATER score was significantly associated with time to AF recurrence post ECV (Hazard Ratio 1.20; 95% CI 1.07–1.35, p=0.003), showing modest but statistically significant prediction ability for recurrent AF post successful ECV (c-statistic 0.61; 95% CI 0.56–0.66, p<0.001). The Kaplan-Meyer survival free from AF post successful ECV was significantly better for patients with a MB-LATER score of <2 than for those with a score of ≥2 (log-rank p=0.005) (Fig 1.). Figure 1 Conclusion In our analysis of an international cohort of AF patients post successful ECV, the MB-LATER score showed a modest but statistically significant prediction ability for recurrent AF post ECV. Reliable prediction of recurrent AF post ECV could inform patient selection and treatment decision-making. Further prospective validation of the MB-LATER score prediction ability for recurrent AF post ECV is underway.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

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