Teknologi Drone untuk Estimasi Stok Karbon di Area Mangrove Pulau Kemujan, Karimunjawa

2021 ◽  
Vol 10 (2) ◽  
pp. 281-290
Author(s):  
Wanda Laras Farahdita ◽  
Nirwani Soenardjo ◽  
Chrisna Adhi Suryono
Keyword(s):  
Carbon Sequestration ◽  
Image Classification ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Measurement Data ◽  
Mangrove Forests ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Change Impact ◽  
Spatial Area

Hutan mangrove dapat mengurangi emisi karbon dengan menyerap CO2 yang berasal dari udara. Kawasan Tracking Mangrove Pulau Kemujan merupakan salah satu pulau di Taman Nasional Karimunjawa yang didominasi oleh mangrove. Jumlah serapan karbon yang tersimpan di mangrove perlu dihitung sebagai upaya penanganan iklim global dan menambah fungsi mangrove. Pendugaan karbon dapat dilakukan melalui teknologi penginderaan jauh, salah satunya dengan drone. Tujuan dari penelitian ini adalah menghitung dan memetakan area spasial distribusi stok karbon di area tracking mangrove Pulau Kemujan, Karimunjawa. Penelitian ini menggunakan data kuantitatif yang didapatkan dari pendekatan analisis spasial dan data pengukuran lapangan. Metode yang diaplikasikan terdiri dari fotogrametri, image classification, dan perhitungan pendugaan karbon. Resolusi hasil foto udara adalah 3,19 cm/pix, uji korelasi dan uji validasi antara nilai karbon dan indeks vegetasi (NDVI) adalah 0,658 dan 10,738%. Hasil penelitian menunjukkan bahwa area tracking mangrove Pulau Kemujan, Karimunjawa memiliki estimasi simpanan karbon antara 8,42–224,6 ton/ha, dominansi karbon tertinggi berkisar antara 19,43-31,20 ton/ha yang mencakup 8,159 ha. Total area yang terpetakan adalah 28,462 ha dengan rata -rata nilai karbon 56,93 ton/ha. Mangrove forests can reduce carbon emissions by absorbing CO2 from the air. Mangrove Tracking Area of Kemujan Island is one of the islands in Karimunjawa National Park which dominated by mangroves. The amount of carbon sequestration in mangroves needs to be calculated in order to reduce the climate change impact and increase the function of mangroves. Carbon estimation could be approached by remote sensing technology, drones are one of them. The study aims to calculate carbon sequestration and mapping the spatial area of carbon stock distribution in the mangrove tracking area of Kemujan Island, Karimunjawa. Quantitative data are obtained from the spatial analysis and field measurement data. The method applied consists of photogrammetry, image classification, and calculation of carbon estimation. Resolution of aerial photo is 3.19 cm/pix, correlation test and validation test between carbon value and vegetation index (NDVI) are 0.658 and 10.738%, respectively. The result showed that the mangrove tracking area of Kemujan Island, Karimunjawa had an estimated of carbon stock ranges from 8.42–224.6 tons/ha, the highest dominance is 19.43-31.20 tons/ha which covered 8,159 ha. The total area mapped as a spatial area of carbon stock distribution is 28,462 ha with an average carbon value of 56.93 tons/ha.

scholarly journals Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

2021 ◽  
Vol 15 (4) ◽  
pp. 21-43
Author(s):  
Esther O. Makinde ◽  
Cristina M. Andonegui ◽  
Ainhoa A. Vicario
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Green Space ◽  
Total Carbon ◽  
Spectral Indices ◽  
Moisture Index ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Forest Lands

Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing technology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999–2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 760
Author(s):  
Sifiso Xulu ◽  
Philani T. Phungula ◽  
Nkanyiso Mbatha ◽  
Inocent Moyo
Keyword(s):  
South Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Abandoned Mines ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Petroleum Resources ◽  
Google Earth Engine ◽  
The Government

This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

scholarly journals Monitoring of Carbon Sequestration in Iceland Using Remote Sensing Technology: An Overview of the LanDeg Project

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 39
Author(s):  
Hrabalikova ◽  
Finger
Keyword(s):  
Remote Sensing ◽  
Carbon Sequestration ◽  
Vegetation Cover ◽  
Remotely Sensed Data ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Rs Analysis ◽  
Sequestration Rate ◽  
Gis And Rs

The monitoring of restoration and forestation is essential to reduce future drought and flood risk as well as ongoing carbon sequestration projects in Iceland. This is especially relevant for Iceland’s efforts to become carbon neutral by 2040. Such a monitoring can be done by using the state-of-art remote sensing technology, using remotely sensed data and digital mapping approaches. The LanDeg project will use free Geographic Information System (GIS) and Remote Sensing (RS) data to map soil degradation, restoration and ongoing forestation efforts to assess carbon sequestration. For this purpose, we will validate GIS and RS data analysis with field mapping of vegetation and soil cover in a restored area in southern Iceland. The validated GIS and RS analysis will be used to assess restoration efforts and trends in vegetation cover in the area. Subsequently, the changes in the vegetation cover will be used to assess the carbon sequestration rate. Based on these results we will identify best-restoration and carbon sequestration practices.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY

Jurnal Segara ◽  
2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Anang Dwi Purwanto
Keyword(s):  
Remote Sensing ◽  
Mangrove Forest ◽  
Image Data ◽  
Field Measurements ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Remote Sensing Technology ◽  
Red Edge ◽  
Sensing Technology ◽  
Sentinel 2A

The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015. The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites (R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but image composites from SPOT 6 image still require additional of association elements to identify mangrove objects.The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015.The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites(R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but imagecomposites from SPOT 6 image still require additional of association elements to identify mangrove objects.

scholarly journals CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 73-77
Author(s):  
İ. Avcı ◽  
E. Farzaliyev ◽  
E. Kabullar
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Threshold Function ◽  
Vegetation Density ◽  
Sensitive Data ◽  
Index Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Plant Groups

Abstract. A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.

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