Normalized Difference Vegetation Index Method Research Articles

Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images.

The increasingly serious harmful algal blooms (HABs) in Taihu Lake has brought huge losses to the local economy and people's life in Taihu Lake. Satellite remote sensing technology has become one of the most important monitoring methods for HAB disasters due to its large-scale and long-term advantages. GOCI image has become the new data source of HAB monitoring because of its large size and high time resolution. Due to the low spatial resolution (500 m) and the existence of mixed pixels, the error of HAB area obtained by the NDVI method is large. In this paper, the linear mixing model (LMM) and the normalized difference vegetation index (NDVI) threshold method are combined to extract the HAB area from GOCI images with 500-m spatial resolution. Compared with the results of the HAB area extracted by Landsat8 OLI and MODIS data, three small areas in the study area were selected to verify the accuracy of the HAB area extracted from the GOCI image on October 2, 2015. The results show that when the NDVI threshold is 0.1, the area error of HABs is the smallest when the extracted HAB pixels mask the decomposition results of mixed pixels; besides, the area error of HABs extracted from the GOCI image is smaller than that from MODIS image; finally, GOCI image can extract the spatial dynamic distribution of HABs in Taihu Lake within 8 h a day, which has higher temporal resolution than the MODIS image. Compared with the NDVI threshold method and LMM method, the inversion accuracy is greatly improved, and the accuracy is stable in different regions. It can provide technical support for the decision-making and assessment of HAB ecological disasters.

Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?

Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a Soil Plant Analysis Development (SPAD) meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (p < 0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods.

Availability and needs of urban forest vegetation in industrial areas: Analyzing Landsat 8 Imagery using the normalized difference vegetation index method

An increase in the area of built-up space can lead to a decrease in the green open space of urban forest in industrial areas so that the minimum standard for urban forest area is not met. One of the environmental management that can provide thermal comfort in industrial areas is urban forests. This article aims to analyze and evaluate the availability and needs of urban forest vegetation to provide thermal comfort in industrial estates by utilizing Landsat 8 satellite imagery. The methods used in this article are qualitative and quantitative analysis. The qualitative methods were using secondary data in the form of literature studies. Meanwhile, the quantitative methods were following to obtain information on the availability and needs of urban forest vegetation using the Normalized Difference Vegetation Index (NDVI) method on Landsat 8 images. The analysis of urban forest vegetation in industrial areas found that forest vegetation city in industrial area X has a fairly low area. The distribution is not evenly distributed and only at a few points. The current urban forest vegetation needs for each workforce in industrial areas X is 3.96 m2 / person.

A spatial study of rubber plant health using sentinel 2-A imagery in Cibungur Plantation, Sukabumi, West Java

Rubber plants are one of the plantation commodities which occupy an important position as a source of non-oil and gas foreign exchange for Indonesia. Rubber production is currently experiencing a decline due to reduced productivity in rubber plantations. Rubber plantation problems related to land resources require effective and efficient technological input to determine the health of rubber plants so that they can increase rubber production. Remote sensing technology is one input that can be used as a decision maker to decide problems spatially and quickly, accurately, and dynamically. This study aims to determine the health condition of rubber plants in Cibungur Plantation, Sukabumi, West Java by using high-resolution satellite imagery, namely Sentinel 2-A images processed using the NDVI (Normalized Difference Vegetation Index) method in monitoring the spatial health of rubber plants. The results of this study indicate that the level of distribution of NDVI values in Cibungur Plantation is quite high and rubber plants with very good health conditions have the largest area of 5,365.98 hectares. The NDVI method and Sentinel 2-A image processing used in this study can be used to determine the health of rubber plants in Cibungur Plantation.

Mangrove vegetation health index analysis by implementing NDVI (normalized difference vegetation index) classification method on sentinel-2 image data case study: Segara Anakan, Kabupaten Cilacap

Segara Anakan, Cilacap District is an area that embodies the largest mangrove forest in Indonesia, namely 8,576.06 ha in 2016. The observation and mapping of vegetation index surrounding the forest are done by utilizing remote sensing to acquire satellite images. Plants have adequate reflectance to Near Infrared (NIR) lights. Normalized Vegetation Index (NDVI) is classified to have high accuracy in mapping mangrove in an area. The concept of NDVI is considerably simple, namely by observing the best NIR light reflected by the chlorophyll and red light radiation absorbed by green plants. Algorithm or equation used is (NIR - RED)/(NIR + RED). The results from the algorithm will then be used to derive index value ranging from -1 to 1. This index value will be employed as a parameter to indicate whether the mangroves are healthy or not. This study is done in 2020 incorporating data from 2017 to 2020 that are relevant to Segara Anakan, Cilacap District and implementing NDVI method to analyze the mangroves’ health. Results from the satellite images show that mangroves in Segara Anakan are experiencing a negative trend regarding their health from year to year. The result in 2017 shows a majority of 0.9 index with pixel vegetation frequency of 17,500 which drastically declined in 2020 to only 7,000 with majorities of 0,2;0,6;0,8 which is caused by deforestation and natural causes inhibiting the sowing and growth of mangroves.

Land cover identification using Pleiades satellite imagery by comparison of NDVI and BI method in Jatinangor, West Java

Land cover monitoring is important to maintaining human needs and ecological function balance that will be able to observe, analyse, and present land use form. Some methods that can be implemented to this case are Normalized Difference Vegetation Index (NDVI) and Bare soil Index (BI). Land cover identification is using Pleiades images in 2018 for Jatinangor area, with land cover classes were built in five class, divided by producer’s and user’s accuracy and validated with field survey results. Accuracy of NDVI method is 69.56% with kappa 0.57 also producer’s accuracy is 73.94% water bodies, 84.88% built land, 67.37% open field, 98.79% crops field, and 98.79% vegetation. User’s accuracy in a row for each class are 26.86% 82.78%, 20.01%, 76.15%, and 75.37%. The Accuracy of BI method is 69.17% with kappa 0.52. Producer’s accuracy for each class is 73.28% water bodies, 70.71% land build, 52.83% open filed, 63,04% crop field, and vegetation with 81.29%. User’s accuracy in a row for each class is 17.05%, 81.29%, 21.44%, 65.13%, and 88.54%. Validation of NDVI more fit in the field than BI that can be concluded NDVI has a good result than BI.

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cndvi) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cndvi often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cndvi values and compare the values with those of literature values (Clit) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cndvi and Clit improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cndvi values were generally higher compared with Clit values. Consequently, modelling erosion using Cndvi resulted in two times higher rates than modelling with Clit. The Cndvi values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cndvi values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cndvi estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cndvi values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

Estimation of rice productivity using Sentinel-2 imagery with NDVI algorithm in Cariu sub-district, Bogor, West Java

Cariu Sub-District is one of the leading rice producers in Bogor Regency, West Java, Indonesia and in a population of 45,921 in 2017 with a population growth rate of 0.64% based on projection results. The consequence of the increasing population growth rate is increasing rice consumption. Cariu Sub-District has not been able to self-sufficient food. In 2017, Cariu Sub-District had 5,107 hectares of rice harvested area with the productivity of 5.99 ton/hectares. Therefore, it is important to update information about rice planting areas in order to estimate its productivity accurately and to maintain Indonesian especially Cariu Sub-District food needs. This study aims to estimate the rice productivity in Cariu Sub-District, Bogor in 2017 using Sentinel-2 imagery based on remote sensing. In this study, NDVI (Normalized Difference Vegetation Index) method used for determines the rice growth phase. The result shows that the Sentinel-2 image utilization with NDVI algorithm can estimate rice productivity in 2017 in Cariu Sub-District. It was expected that the rice productivity obtained is almost by the data from the Department of Agriculture and Forestry in the Cariu Sub-District.

Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

A Research on Identify Building Objects from Satellite Images

Paper presents an innovative Building Object Detection method from the satellite image, which is using the method - Normalized Difference Vegetation Index (NDVI). Remote Sensing and Geographical Information System methods are used frequently in the field of planning and determination of land changes. Recently, Google earth images are used in most of the applications like urban and travel planning etc. The NDVI method is the key aspect to detect the building object automatically. The idea of the proposed method is to detect and identify the building object and periodic changes in the area were detected automatically.

URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY

Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

Change of Vegetation Cover at Rohingya Refugee Occupied Areas in Cox’s Bazar District of Bangladesh: Evidence from Remotely Sensed Data

Nearly one million Rohingya Refugees are living in Cox’s Bazar—a south-eastern district of Bangladesh; among them, more than half a million have fled Myanmar since August 2017. There are always some impacts of refugee settlements on the host environment. Hence, this study has made an initiative to investigate the changes of vegetation covers in four refugee occupied Unions of Teknaf and Ukhia Upazila. Analysing the remotely sensed Landsat imageries using Normalized Difference Vegetation Index method, the spatial extent of sparse vegetation, moderate vegetation, and dense vegetation before and after the occurrence of 2017 Influx have been quantified. The result reveals that nearly 21,000 acres of dense vegetation and more than 1700 acres of moderate vegetation have been reduced within the period of one year in-between 2017 and 2018. On the other hand, during the same period, the refugee sites have been expanded by almost 6000 acres. The main reasons for this drastic reduction of vegetation include the construction of refugee camps by felling the forest and consumption of firewood by refugees from the surrounding forest of their camps. Arrangement of alternative cooking fuel, relocation of refugees, reforestation, and accelerating the repatriation process may reduce the further degradation of vegetation.
 J. Environ. Sci. & Natural Resources, 11(1-2): 9-16 2018

THE UTILIZATION OF REMOTE SENSING DATA TO SUPPORT GREEN OPEN SPACE MAPPING IN JAKARTA, INDONESIA

Green open space becomes critical in maintaining the balance of the environment and improving the quality of urban living for a healthy life. The use of remote sensing data for calculation of green open space has been done notably using NDVI (Normalized Difference Vegetation Index) method from Landsat 8 and SPOT data. This research aims to calculate the accuracy of the green open space classification from multispectral data of Landsat 8 and SPOT 6 using the NDVI methods. Green open space could be assessed from the value NDVI. The value of NDVI generated from Landsat 8 and SPOT 6’s Red and NIR channels. The accuracy of NDVI values is then examined by comparing with Pleiades data. Pleiades data which has 50 cm panchromatic resolution and 2 m multispectral with 4 bands (B, G, R, NIR) can precisely visualize objects. So, it can be used as the reference in the calculation of the green open space based on NDVI. The results of the accuracy testing of Landsat 8 and SPOT 6 image could be used to identify the green open space by using NDVI SPOT of 6 can increase the accuracy of 5.36% from Landsat 8.

SISTEM PENDETEKSI PERUBAHAN LAHAN HIJAU DI JABODETABEK

Change of green area in Jakarta, Bogor, Depok, Tangerang and Bekasi (Jabodetabek) has been something very important. Classification of green area aims to do classification in Jabodetabek using Landsat 8 satellite images, band 1, 2, 3, 4 and 5. Before classification was done, the satellite images will be corrected using Radiometric Correction method called Mini-max algorithm. After doing radiometric correction, the classification will use the NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) method. The selected area will be classified as green when NDVI values similar or has more than 0.3. After perform two categories, Y1 and Y2 are selected by NDVI values using dummy dependent variable. Linear regression method use that dummy dependent variable to classify the selected area in Jabodetabek. To see how can we trust the result, the classified area will be compared with the appearance of selected area in Google Earth. The highest degradation of green area is in Bogor, May 2015, 325.7368 Km2.

Identification of rice field using Multi-Temporal NDVI and PCA method on Landsat 8 (Case Study: Demak, Central Java)

Paddy is one of the most important agricultural crop in Indonesia. Indonesia’s consumption of rice per capita in 2013 amounted to 78,82 kg/capita/year. In 2017, the Indonesian government has the mission of realizing Indonesia became self-sufficient in food. Therefore, the Indonesian government should be able to seek the stability of the fulfillment of basic needs for food, such as rice field mapping. The accurate mapping for rice field can use a quick and easy method such as Remote Sensing. In this study, multi-temporal Landsat 8 are used for identification of rice field based on Rice Planting Time. It was combined with other method for extract information from the imagery. The methods which was used Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA) and band combination. Image classification is processed by using nine classes, those are water, settlements, mangrove, gardens, fields, rice fields 1st, rice fields 2nd, rice fields 3rd and rice fields 4th. The results showed the rice fields area obtained from the PCA method was 50,009 ha, combination bands was 51,016 ha and NDVI method was 45,893 ha. The accuracy level was obtained PCA method (84.848%), band combination (81.818%), and NDVI method (75.758%).

Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2m(2) in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3cm height. At 500°Cday, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r (2) =0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15% for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

Multi-sensor monitoring of Ulva prolifera blooms in the Yellow Sea using different methods

The massive Ulva (U.) prolifera bloom in the Yellow Sea was first observed and reported in summer of 2008. After that, the green tide event occurred every year and influenced coastal areas of Jiangsu and Shandong provinces of China. Satellite remote sensing plays an important role in monitoring the floating macroalgae. In this paper, U. prolifera patches are detected from quasisynchronous satellite images with different spatial resolution, i.e., Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting), CCD (Charge-Coupled Device), Landsat 8 OLI (Operational Land Imager), and ENVISAT (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) images. Two comparative experiments are performed to explore the U. prolifera monitoring abilities by different data using detection methods such as NDVI (Normalized Difference Vegetation Index) with different thresholds. Results demonstrate that spatial resolution is an important factor affecting the extracted area of the floating macroalgae. Due to the complexity of Case II sea water characteristics in the Yellow Sea, a fixed threshold NDVI method is not suitable for U. prolifera monitoring. A method with adaptive ability in time and space, e.g., the threshold selection method proposed by Otsu (1979), is needed here to obtain accurate information on the floating macroalgae.

Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District

Abstract This article presents an enhanced Change Detection method for the analysis of Satellite image based on Normalized Difference Vegetation Index (NDVI). NDVI employs the Multi-Spectral Remote Sensing data technique to find Vegetation Index, land cover classification, vegetation, water bodies, open area, scrub area, hilly areas, agricultural area, thick forest, thin forest with few band combinations of the remote sensed data. Land Resources are easily interpreted by computing their Normalized Difference Vegetation Index for Land Cover classification. Remote Sensing data from Landsat TM image along with NDVI and DEM data layers have been used to perform multi-source classification. The Change Detection method used was NDVI differencing. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4 and 0.5. The Simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for policy makers in decision making. The Vegetation analysis can be helpful in predicting the unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. From the empirical study, the forest or shrub land and Barren land cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4% and 7% respectively. Curvature, Plan curvature, Profile curvature and Wetness Index areas are also estimated.

Recent spring phenology shifts in western Central Europe based on multiscale observations

AbstractAimAlthough numerous studies have reported advanced Northern Hemisphere spring phenology since the 1980s, recent studies based on remote sensing have reported a reversal or deceleration of this trend. This study aimed (1) to fully understand recent spring phenology shifts using both in situ observations and satellite‐based normalized difference vegetation index (NDVI) datasets, and (2) to test whether the NDVI methods capture the trends observed in situ.LocationWestern Central Europe.MethodsTemporal spring phenology trends (leaf unfolding dates) were examined using 1,001,678 in situ observations of 31 plant species at 3984 stations, as well as NDVI‐based start‐of‐season dates, obtained using five different methods, across the pixels that included the phenology stations.ResultsIn situ and NDVI observations both indicated that spring phenology significantly advanced during the period 1982–2011 at an average rate of −0.45 days yr−1. This trend was not uniform across the period and significantly weakened over the period 2000–2011. Furthermore, opposite trends were found between in situ and NDVI observations over the period 2000–2011. Averaged over all species, the in situ observations indicated a slower but still advancing trend for leaf unfolding, whereas the NDVI series showed a delayed spring phenology.Main conclusionsThe recent trend reversal in the advancement of spring phenology in western Central Europe is likely to be related to the response of early spring species to the cooling trend in late winter. In contrast, late spring species continued to exhibit advanced leaf unfolding, which is consistent with the warming trend during spring months. Because remote sensing does not distinguish between species, the signal of growing‐season onset may only reflect the phenological dynamics of these earliest species in the pixel, even though most species still exhibit advancing trends.

Estimating CO2 Sequestration by Forests in Oita Prefecture, Japan, by Combining LANDSAT ETM+ and ALOS Satellite Remote Sensing Data

CO2 sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO2 sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO2/yr for coniferous, 0.77 MtCO2/yr for deciduous broadleaf, and 2.25 MtCO2/yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO2/yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO2/yr (coniferous) and 0.31 MtCO2/yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO2 sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources.