scholarly journals Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

2020 ◽  
Vol 12 (4) ◽  
pp. 597 ◽  
Author(s):  
Akash Anand ◽  
Prem Chandra Pandey ◽  
George P. Petropoulos ◽  
Andrew Pavlides ◽  
Prashant K. Srivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Carbon Stocks ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Total Carbon ◽  
Spectral Angle Mapper ◽  
Aboveground Carbon

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

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 HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 169-175 ◽  
Author(s):  
W. Pervez ◽  
S. A. Khan ◽  
Valiuddin
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Spectral Signature ◽  
Processing Application ◽  
Significant Difference

Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

scholarly journals Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1258
Author(s):  
Mengying Liu ◽  
Zhonghe Zhang ◽  
Xuelian Liu ◽  
Jun Yao ◽  
Ting Du ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Pearson Correlation ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Forest Damage ◽  
Stepwise Discriminant Analysis ◽  
Diagnostic Model ◽  
Test Accuracy ◽  
Damage Degree ◽  
Pine Shoot Beetle

Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R690 and R798 as variables had the highest accuracy (R2 = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.

scholarly journals Analysis and object markup of hyperspectral images for machine learning methods

2019 ◽  
pp. 309-317
Author(s):  
V P Gromov ◽  
L I Lebedev ◽  
V E Turlapov
Keyword(s):  
Machine Learning ◽  
Pearson Correlation ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Two Dimensional ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reference Signature ◽  
Selection Of

The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of analysis, as in Landgrebe sequence, is the method of principal component analysis, but it is no longer used to build classes and is applied to investigate the presence in the class of subclasses essential for the applied area. The experimental material includes such objects as water, swamps, soil, vegetation, concrete, pollution. Selection of object samples on the image is made by the user. From the studied images of HSI objects, a base of reference signatures for classes (subclasses) of objects is formed, which in turn can be used to automate HSI markup with the aim of applying machine learning methods to recognize HSI objects and their states.

scholarly journals Estimation of Carbon Stock Stands using EVI and NDVI Vegetation Index in Production Forest of Lembah Seulawah Sub-District, Aceh Indonesia

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Jhon Pandapotan Situmorang ◽  
Sugianto Sugianto ◽  
Darusman .
Keyword(s):  
Regression Analysis ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Close Correlation ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Coefficient Of Determination ◽  
Average Value ◽  
Vegetation Indexes ◽  
Aceh Province

This study aims to determine the distribution of the vegetation indexes to estimate the carbon stocks of forest stands in the Production Forest of Lembah Seulawah sub-district. Aceh Province, Indonesia. A non-destructive method using allometric equations and landscape scale method were applied, where in carbon stocks at the points of samples are correlated with the index values of each transformation of the vegetation indexes; EVI and NDVI.  Results show that EVI values of study area from 0.05 to 0.90 and NDVI values from 0.17 to 0.85. The regression analysis between EVI with carbon stock value of sample locations equation is Y = 151.7X-39.76. with the coefficient of determination (R2) is 0.83. From this calculation, the total carbon stocks in the Production Forest area of Lembah Seulawah sub-district using EVI is estimated 790.344.41 tonnes, and the average value of carbon stocks in average is 51.48 tons per hectare.  Regression analysis between NDVI values at the research locations for the carbon stack measured samples is Y = 204.Xx-102.1 with coefficient of determination (R2) is 0.728. Total carbon stocks in production forest of Lembah Seulawah sub-district using NDVI is estimated 711.061.81 tones. and the average value of carbon stocks is 46.32 tons per hectare. From the above results it can be concluded that the vegetation indexes: EVI and NDVI are vegetation indexed that have a very close correlation with carbon stocks stands estimation. The correlation between EVI with carbon stock and the correlation between NDVI with carbon stock is not significantly different

scholarly journals Estimasi Stok Karbon Mangrove Di Muara Sungai Kumbe Distrik Malind Kabupaten Merauke

2018 ◽  
pp. 24-32
Author(s):  
Reny Sianturi ◽  
Siti Masiyah
Keyword(s):  
Mangrove Forest ◽  
Carbon Stock ◽  
River Estuary ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Soil Conditions ◽  
Mangrove Forests ◽  
Unique Type ◽  
The Indian Ocean ◽  
Stock Values

Forests reduce and store CO2 through a process of "sequestration" that is the storage of carbon from the atmosphere and its storage in several copartments such as plants, litter, and soil organic matter. Mangrove forest is a unique and unique type of forest because it is able to adapt to environments with high salinity, soil conditions without oxygen and once in a while. ne example of mangrove forests in Indonesia is mangrove forest in the Kumbe river estuary, Merauke Regency, Papua Province. The Kumbe river estuary is one of the eastern Indonesian waters bordering the Indian Ocean. At the Kumbe river estuary there has been no research on carbon stocks in the mangrove community. So it is necessary to do research on carbon stocks in the region. In this study the measurement of carbon stocks used was done by measuring carbon above ground, and ground. Above ground carbon stock components include trees, understorey, and litter. The mechanism for measuring above ground carbon stock is done by estimating biomass, which is then converted to carbon concentration. The results of this study indicate that the carbon stocks in trees, Understorey and litter in sequence are 85.55 Mg / Ha, 392.93 Mg / Ha and 70.75 Mg / Ha; 0.78 Mg / Ha, 1.26 Mg / Ha and 1.24 Mg / Ha and 2.04 Mg / Ha, 1.28 Mg / Ha and 1.2 Mg / Ha. As for the proportion of carbon stock values ​​found in mangrove forests that trees contribute greatly to total carbon stock. Keyword : Mangrove, carbon stocks, Merauke, Kumbe

scholarly journals Mapping of mangrove forest tree density using SENTINEL 2A satelit image in remained natural mangrove forest of Sumatra eastern coastal

2021 ◽  
Vol 912 (1) ◽  
pp. 012001
Author(s):  
Samsuri ◽  
A Zaitunah ◽  
S Meliani ◽  
O K Syahputra ◽  
S Budiharta ◽  
...  
Keyword(s):  
Density Distribution ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Distribution Model ◽  
Coefficient Of Determination ◽  
Density Estimator ◽  
Mangrove Forests ◽  
Vegetation Density ◽  
Index Value

Abstract The mangrove ecosystem in Forest Managemen Unit - VII (FMU) Sumatera Utara is a natural forest. FMU has not managed and utilizes mangrove forests optimally. It can open up opportunities for illegal loggers and trigger damage to these natural ecosystems. This condition requires prevention and mitigation so that severe damage to mangrove forests does not occur. This study aims to determine the relationship between vegetation index and mangrove density in the field and map the mangrove density distribution based on the image vegetation index value. The density distribution mapping was carried out by compiling a vegetation density estimator model NDVI, GNDVI, and TVI as independent variables. Correlation test and regression analysis between the vegetation index value (NDVI, GNDVI, and TVI) to the number of trees per unit area. The distribution model for the density of mangrove stands was chosen based on the coefficient of determination (R2). The study resulted from NDVI selected as the vegetation index used to map the distribution of mangrove density with a Pearson correlation coefficient (R) of 0.738. The selected model is Y = 2.48e2.8667x, which is an exponential equation with a coefficient of determination (R2) of 61.3%. Based on this model, the distribution of mangrove density has the lowest density reaching 400, and the highest density is 2,200 trees per hectare

scholarly journals ANALISIS STOK KARBON HUTAN MANGROVE PADA BERBAGAI TINGKAT KERUSAKAN DI SEGARA ANAKAN CILACAP

2017 ◽  
Vol 3 (2) ◽  
pp. 161 ◽  
Author(s):  
Mia Azizah ◽  
Erwin Riyanto Ardli ◽  
Eming Sudiana
Keyword(s):  
Random Sampling ◽  
Mangrove Forest ◽  
Carbon Stock ◽  
Vegetation Type ◽  
Pearson Correlation ◽  
Carbon Stocks ◽  
Survey Method ◽  
Sampling Location ◽  
Damage Level ◽  
Arcview Gis

Cabrbon Stock Analysis of Mangrove Forest in Every Damaged Level in Segara Anakan Cilacap         Mangrove is a specific vegetation type, found in tropical and subtropical beach area which located in Cilacap  at a sloping beach area near the mouth of a river and the beach protected from the waves. Segara anakan is one of mangroves region which located at 108 º 46'-109 º 03 'E and 07 º 34' - 07 º 47 'South Latitude. Human activities series in Segara anakan mangrove lead the damage of this region, it affects to the ecological and biological or mangrove function as carbon storage place. The aims of this research was to analyze the damage level of mangrove in Segara anakan, Cilacap; to know the spatial distribution of mangrove damage level in Segara anakan; analyze the amount of biomass and carbon stocks at various of damage level in Segara anakan, and to know the number corelation of carbon stocks with damage level in Segara anakan, Cilacap.The research used survey method with purposive random sampling that determine the sampling location based on the damage level. Damage analysis used  assessment teristis method (field survey) and than spasial distribution used surfer 9.0 and ArcView GIS 3.2. Biomass analysis and the amount of carbon stock used descriptive methods, damage level correlation and the amount of carbon stock used Pearson correlation analysis (SPSS software vs. 19).The result was Segara anakan mangrove, Cilacap currently was divided into not damage (7 station), damaged (3 station) and  heavily damaged (5 station) categories. The amount of biomass and carbon stocks in not damaged area (57,67 tons/ha and 26,50 tons/ha); damaged area (23,40 tons/ha and 10,74 tons/ha, and the heavily damaged area (9,49 tons/ha and 4,37 tons/ha). The destruction of mangrove forest affected the amount of biomass and carbon stocks in Segara anakan, Cilacap.Keywords : mangrove,  carbon stock, damage level, Segara Anakan Cilacap ABSTRAK        Hutan mangrove merupakan tipe vegetasi khas, terdapat di daerah pantai tropis dan subtropis yang tumbuh subur di daerah pantai yang landai di dekat muara sungai dan pantai yang terlindung dari hempasan gelombang. Segara Anakan adalah salah satu kawasan hutan mangrove yang terletak pada koordinat 07º34’ - 07º47’ LS dan 108º46’- 109º03’ BT. Serangkaian aktivitas manusia di kawasan hutan mangrove Segara Anakan menyebabkan kawasan ini mengalami kerusakan, hal tersebut berpengaruh terhadap fungsi ekologis dan biologis serta fungsi hutan mangrove sebagai penyimpan karbon.  Penelitian ini bertujuan untuk menganalisis dan mengetahui tingkat kerusakan  hutan  mangrove di Segara Anakan Cilacap; mengetahui distribusi spasial potensi stok karbon hutan mangrove di Segara Anakan Cilacap  dan  mengetahui korelasi jumlah stok karbon dengan tingkat kerusakan di Segara Anakan Cilacap.Penelitian ini menggunakan metode survei dengan menggunakan teknik purposive random sampling  yaitu menentukan lokasi sampling berdasarkan  pada tingkat kerusakan. Analisis kerusakan menggunakan metode penilaian teristis (survey lapangan) yang selanjutnya didistribusi spasial menggunakan surfer  9.0 dan Arcview GIS 3.2. Analisis biomassa dan jumlah stok karbon menggunakan metode deskriptif, korelasi tingkat kerusakan, dan jumlah stok karbon menggunakan analisis korelasi Pearson (Software SPSS vs. 19). Hasil yang diperoleh adalah hutan mangrove Segara Anakan Cilacap saat ini terbagi menjadi area dengan kategori tidak rusak (7 stasiun), rusak (3 stasiun) dan rusak berat (5 stasiun). Jumlah biomassa dan stok karbon di area yang tidak mengalami kerusakan (57,67 ton/ha dan 26,50 ton/ha), area yang rusak (23,40 ton/ha dan 10,74 ton/ha, dan area yang rusak berat (9,49 ton/ha dan 4,37 ton/ha). Kerusakan hutan mangrove berpengaruh terhadap jumlah biomassa dan stok karbon di Segara Anakan.Kata Kunci: mangrove, stok karbon, tingkat kerusakan,SegaraAnakan Cilacap

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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