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Article

Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

1
Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India
2
Division of Geosciences and Environmental Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, 971 87 Norrbotten County, Sweden
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010029
Received: 23 November 2020 / Revised: 27 December 2020 / Accepted: 11 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)

Abstract

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.
Keywords: Sentinel-2; regression modeling; fraction of vegetation cover; forest AGB Sentinel-2; regression modeling; fraction of vegetation cover; forest AGB

1. Introduction

Forests are ecosystems maintaining bioresources and providing a range of products and services to meet human needs [1,2]. As reported, 31% of the total Earth′s land surface is covered by forests that play a significant role in maintaining ecosystems and providing food, fodder, and shelter. Forests are also instrumental in controlling land degradation by minimizing soil erosion and desertification. Furthermore, even biological diversity in a regional ecosystem is directly affected by surrounding forest cover [3]. Forest biomass is considered reservoirs of carbon. Forests directly have a crucial role in the carbon and water cycle, gross and net primary production (GPP and NPP), climate changes and global warming, radiation and environment balance, even air quality, and human activities [1,4]. Accurate forest above ground biomass (AGB) assessment is crucial for forest resources evaluation, carbon cycle assessment, and sustainable forest management [1,2,3,4]. Therefore, forest AGB evaluation is essential to recognize possible climate change due to deforestation and global ecological balance, as well as promoting global biological evolution, community succession, and mitigation processes [1,5,6]. Globally, the rapidly deteriorating forest might be a big threat not only for forest resources but also for future ecological balance [1]. Thus, accurate assessment and monitoring of forest AGB for sustainable management and mitigation, and also to recognize climate change scenario due to deforestation are important and crucial [1,5,6]. Forests have great potential to fuel the next generation by harvesting byproducts (e.g., brushwood, lumber, firewood) and supplying a large portion of renewable materials (e.g., edible fruits, nuts, paper, medicinal herbs) that can be converted into renewable energy [1,2,3,4,7,8,9]. The forest biomass evaluation is extremely important to assess renewable energy sources and future planning to keep balance in our environment, economy, and energy security [1,2,3,4]. Therefore, forest biomass evaluation and monitoring are crucial to assess the bioresources and terrestrial ecosystem. Forests are considered to be a carbon sink. Forests absorb carbon from the atmosphere through photosynthesis and store it in plant tissue. At a regional as well as global level, estimating the biomass of forest vegetation is an important exercise in determining the storage of carbon in the dominant tree component and computing carbon cycling. It then becomes deposited in forest biomass (that is, trunks, branches, roots, and leaves), in dead organic matter (litter and deadwood), and in soils. This process of carbon absorption and deposition by the forest vegetation is known as carbon sequestration, which can help sustainable management of natural resources. For mitigating CO2 emissions through carbon sequestration, this driver of climate change has drawn considerable attention to forest ecosystems as a viable option [10].
Globally, the advanced and rapid global observation systems and analyses of geospatial natural resources using remote sensing techniques are being developed. Remote sensing is state-of-the-art, fast, cost-effective, and ecofriendly technique for monitoring and mapping many geospatial natural resources and climate including forest, vegetation water dynamics and change monitoring, and biomass evaluations [1,6,11,12,13,14]. Advanced and high-quality data acquisition and distribution facilities are offered by different space organizations, including the National Aeronautics and Space Administration (NASA), Canada Centre for Remote Science (CCRS) and the European Space Agency (ESA) [1,15]. Generally, remote sensing techniques do not derive forest biomass directly but enable acquisition of parameters for forest biomass estimation, such as forest height, leaf area index, the fraction of vegetation cover (FVC), or net primary production. Today, due to relatively higher accuracy than conventional systems, fast and cost-effective performance remote sensing systems have gained much attention in the evaluation of the forest biomass and change assessment. Remote sensing technique usually evaluates biomass and change detection/information indirectly rather than directly by retrieving remotely sensed parameters. These extracted parameters help in evaluating AGB, forest, and vegetation mapping and monitoring by integration, inversion, and modeling approaches [1,6,13,14]. Further, high-quality data available from the state-of-the-art sensors onboard recently launched satellites by various space agencies are helpful in modeling ecosystems in an innovative manner, as well as to derive new improved land-based inventory systems for estimation of forest canopy height, biomass, carbon stocks, and spatiotemporal changes with relatively high accuracy [6,13,14]. The availability of Sentinel-2 imagery at high spatial resolution (10 to 60 m) from the Copernicus programme offers a new opportunity to study and evaluate forest bioresources and natural carbon stocks. Currently, remotely sensed multiple datasets through passive and active sources are being acquired from different platforms including spaceborne, airborne, and ground data. These data are being used for the noted applications by utilizing innovative processing approaches with advanced statistical, mathematical, and machine learning skills [11,16,17]. Usually, data from active sensors contain a high signal-to-noise ratio (S:N) and better penetration compared to passive sensors. Thus, active systems such as synthetic aperture radar (SAR), light detection and ranging (LIDAR) can penetrate the canopy of the forests, vegetations, and land covers. Consequently, active remote sensing has become a powerful tool for vertical structural and volumetric measurements of the forests and biomass. Data from passive sensors have great potential for identification, classification, and mapping of forest cover, types, and vegetation classifications [14,18,19,20].
Since the last decade, forest resource evaluation and atmospheric investigation were remarkably boosted by remote sensing techniques [11]. These techniques are frequently used for forest AGB estimation utilizing various datasets. Microwave data such as SAR is useful for texture characteristics and backscattered coefficient information extraction, and optical data such as Sentinel-2 are used for vegetation indices and leaf area index (LAI) information extraction [1,14,21]. For example, Sentinel-1 as SAR data C-band and Sentinel-2 optical imagery at high spatial resolution (10 to 60 m) are available freely from the European Copernicus Programme for effective vegetation and forest mapping and monitoring, biomass estimation, and natural resources assessments [1,11]. Similarly, there are plenty of remote sensing datasets freely available from NASA, ESA, and other space agencies for the above applications. Different modeling and regression approaches between remote sensing microwave data (viz., Sentinel-1—vertical-vertical (VV) and vertical-horizontal (VH) polarizations) and optical data (viz., Sentinel-2—LAI and normalized difference vegetation index (NDVI)) and field data are being developed at present for biomass assessments and ground truth validation [22]. For biophysical structure estimation, microwave data of Sentinel-1 were used to derive InSAR-based tree height estimation of a hilly forest. This study indicated a reduction in tree height estimation uncertainty even from an undulated terrain [23]. The optical images provide great potential for multitemporal monitoring, improvement in the classification of the forest type with high spatial resolution, and quick revisit time availability [24]. For example, optical remote sensing images viz. Sentinel-2 have been used by various researchers in forest classification, mapping, and monitoring [18,19,20,25]. Various approaches and experiments are available to extract different parameters in forest AGB estimation using optical and microwave remote sensing datasets, including tree height, tree diameter at breast height (DBH), and tree volume [25,26]. There are various empirical models and allometric equations developed to estimate forest AGB from field data at plot level using hand-measured techniques, viz., tree DBH, height, and sometimes wood density to improve accuracy [27]. There are various relational models developed between basal area or tree diameter, weighted height (also known as Lorey′s height) parameters, and AGB for more accurate biomass evaluation [28,29,30], and direct forest AGB achieved from SAR and LiDAR data using the above models from large areas [7,31,32,33]. Similarly, optical remote sensing studies show that LAI, FVC, and vegetation indices have significant potential to evaluate forest AGB [34]. The FVC and LAI have proved useful in leaf biomass evaluation through a proportionality function [35]. Usually, AGB using field data at the plot level calculates the sum of foliage and wood biomass [36]. To calculate the LAI or FVC, several vegetation indices have been experimented [37,38,39], which are still challenging for specific forest types. Furthermore, the accuracy of the AGB estimation from both optical and microwave remote sensing data is limited by the accuracy level of data processing and parameter extraction, including ecological, biotic, and topographic factors [6,13,14,22,23,24,25,26,40,41,42]. In this study, an empirical model based on field AGB and FVC from optical remote sensing has been worked to simplify and more rapidly evaluate forest AGB. The field information (tree height, tree DBH, number of trees at plot level (30 by 30 m), tree stem density, and FVC) were collected for field AGB calculation from a test site within the Tundi reserved forest, Jharkhand state of eastern India.

2. Materials and Methods

For the test site within the Tundi reserved forest, the forest AGB was assessed using Sentinel-2 imagery. Brief description of the test site, data details, and the proposed methodology respectively follow.

2.1. Study Area

The study area covers 144 km2 within Tundi reserved forest in Dhanbad District, Jharkhand state, India. This Indian state is one of the richest in biodiversity due to diverse climatic and physiographic conditions. It spreads from a northwest to northeast direction with the Domunda hills having two heads (tops) on its eastern fringe.
There are a few isolated hills of varying dimensions scattered in the northern half of this district. The area is a typical deciduous forest that has a total forested area of 23,605 km2 constituting about 29.61% of the total land area of Jharkhand. In India, reserved and protected forests are declared by the respective state governments. In reserved forests, all activities are prohibited unless permitted; it is vice versa in protected forests, i.e., specific activities such as hunting, grazing, etc., are permitted unless prohibited. Out of the total forest land of Jharkhand, 81.28% is under the protected forest and 18.58% is under the reserved forest category.
Geographically, Jharkhand has two major forest categories, namely tropical zone dry forests and tropical zone wet forests [43]. The test site falls under the tropical zone dry forest category and is covered with Shorea robusta (local name Sal) as the most predominant tree species, lies within 23°45′40″ to 24°05′50″ N and 85°57′30″ to 86°35′55″ E (Figure 1a). Tundi reserved forests under Dhanbad Forest Division fall under subgroup 5-B, namely northern tropical dry deciduous forest [8]. The Tundi reserve forest cover contains an almost pure crop of Shorea robusta saplings and poles. The test site is dominated by minor hillocks with undulating topography; Shorea robusta is dominant and constitutes the top story of the forest, with an average height up to 12 m in the test site. Humus is absent except in remote areas where the forest cover is well protected. The area receives typical monsoon-type climate with three marked seasons: hot, rainy, and winter. Humidity is very high during the rainy season and very low during the hot weather. The maximum temperature rises above 45 °C and a hot wind known as ″Loo″ blows frequently from April to June, until the onset of monsoon. Thunderstorms usually occur in May, accompanied by a temporary fall of a few degrees. Due to heavy industrialization and mining activities, abundant suspended particles and specks of dust are found in the atmosphere during this season. The monsoon usually breaks in the middle of June and continues until the end of September. The average rainfall is between 1100 to 1200 mm.
Geologically, Tundi comprises Precambrian rock, the country rock being quartz-feldspathic schist intruded rather profusely by some igneous bodies, viz., metadolorites and amphibolites. The prominent hill and hillocks in the area comprise mostly dark color, hard, and compact intrusive rocks (mainly amphibolite and metadolorite). The soil and alluvium are derived largely from gneisses and quartz-feldspathic schist. The soil formation in the forest area is shallow to very shallow, and depth on the plain to undulating land rarely exceeds 60 cm (24 inches). It is generally red loam with pockets of clay. Erosion occurs and varies with slope. The topsoil is hard and compact [44].

2.2. Datasets

Sentinel-2 multispectral image (MSI)-level-1C product acquired on 4 December 2019, covering the study area has been chosen as test data of optical remote sensing. Figure 1b shows the false-color composite (FCC) image from Sentinel-2 (L1C) that encompasses the selected test site within the study area. The spatial resolutions vary between 10 to 60 m and spectral resolutions vary between 15 to 180 nm for different bands of the collected Sentinel-2 image (Table 1).
Sentinel-2 outperforms other available spaceborne datasets when using near-equivalent image bands when Sentinel-2 data are downsampled to 30 m pixel resolution (for example, Landsat 8). Additionally, Sentinel-2 includes high quality red edge band. Prediction of forest AGB using Sentinel-2 confirmed better accuracy compared to Landsat 8 [45]. The field survey was carried out to collect ground forest information for remote sensing-based method development and result validation. In order to cover the complete forest variability of the study area, ground truth locations were chosen randomly based on accessibility in the forest and irrespective to tree height categories. Moreover, ground samples were collected from different locations in such a pattern to cover the entire forest diversity of the study area.
The sample plots selected are a representation of the whole study area. The ground samples were collected from three different profiles separated by nearly 1 km. The distance between samples was also set at approximately 1 km. The ground samples were collected randomly from these profiles based on accessibility into the forest with independent directions. Field data collection was conducted using a global positioning system (GPS), altimeter, and ancillary field equipment. The ground samples, including tree height, tree DBH and the number of trees per sample plot, tree stem density, and FVC from 22 different locations with plot size approximately 30 m × 30 m (approx. 0.09 hectare), were collected from the study site. The field samples from 22 locations comprised approximately 550 individual trees, and an average of 25 trees per plot were measured in different elevation strata. The average measured tree height for each field location was used as tree height of the location for further study. In the ground FVC estimation at each sample location, initially, the total number of trees and the radius of each tree cover were calculated. The surface cover of individual trees was assumed circular. Therefore, surface area cover by individual trees was calculated using their covering radius. The total tree cover area at each sample location was estimated by summing individual tree cover area. Lastly, field FVC was calculated by taking the ratio of area covered by trees to the total area of the sample location. Consequently, ground data and FVC were utilized for ground AGB calculation and validation of the modeled AGB from Sentinel-2 data. Further details of the ground data and locations are provided in Table 2. The ground samples sequence provided in Table 2 is the order of data collection regardless of profile composition. Therefore, the sample sequence provided in Table 2 shows random sample location irrespective to spatial pattern of the profile.

2.3. Methodology

The methodology is presented in two phases. In the first phase, a developed general model for forest AGB assessment from optical remote sensing imagery is presented, whereas the second phase shows the experimentation of the approach developed specific to the Sentinel-2 image, vegetation indices, and statistical parameters used in this study.
Phase I. Standard procedure for forest AGB estimation using optical remote sensing data.
The procedure for estimation of the forest AGB was articulated into the following six steps and the flow diagram of the method is shown in Figure 2.
Step 1: Preprocessing—In this step, the optical remote sensing image was preprocessed for noise reduction, radiometric correction and calibration, atmospheric correction, and spatial resampling. The noise persists in most of the satellite images due to sensor malfunctioning and heating, and environmental influences reduce the quality of the data. Hence, suppression of noise contents in an image is essential to enhance the quality of the data. The distributed remote sensing images usually contain the digital number, which is a calibrated radiance value by sensor-specific gain and offset. Thus, radiometric correction and calibration are required to convert digital numbers to radiance.
Further, atmospheric correction is performed to reduce the effects of atmospheric scattering and absorptions in the radiance data and convert the radiance into reflectance data. The Sentinel-2 image contains different spatial resolution in various spectral bands. Spatial resampling was used to achieve a single spatial resolution from different spatial resolutions in different spectral bands. In preprocessing, noise reduction and radiometric correction were performed using image statistics-based algorithms developed using the gain and offset of the sensor. A combined flat-field and dark object subtraction method was developed for atmospheric correction and conversion of a radiometric image into a reflectance image. The Gram–Schmidt pan sharpening algorithm was used to achieve the entire spectral bands into a single high spatial resolution.
Step 2: Forest FVC calculation—In this step, the forest FVC at each pixel level was calculated from optical remote sensing imagery using the FVC model proposed by Zhang et al. [46] as follows:
Suppose spectral information of pure vegetation pixel and pure soil pixel are represented as Sv and Ss. Then spectral information (S) of an image at each pixel is composed of spectral information of vegetation cover (Sveg) and nonvegetation cover (So). If forest FVC in a pixel is represented as Fc, then a fraction of nonvegetation cover should be 1−Fc. Thus, mathematically, forest FVC at a pixel level can be calculated as follows:
S v e g = S v × F c ,
S o = S s × ( 1 F c )
S = ( S v e g + S o ) { ( S v × F c ) + ( S s × ( 1 F c ) ) } ,
hence ,   F c = S S s S v S s
Using the Equation (4), the FVC is calculated from both the optical remote sensing image and field data.
Step 3: Field forest AGB estimation—In this step, the field forest AGB was calculated from the collected field data. The field forest AGB was obtained at individual plot level in kilogram (kg) using the following simplified empirical model proposed by Chave et al. [27] as follows:
A G B = κ × μ D 2 H ,
where μ is specific gravity (g cm−3) of tree stem, D is the DBH (cm), H is the tree height (m), and κ is the constant for forest type.
Step 4: Model development between FVC and AGB—In this step, linear regression modeling is performed to find the best fit curve between the calculated ground forest AGB from field data using Step 3 and the image forest FVC obtained using Step 2.
The best fit linear model between the field forest AGB and the corresponding image forest FVC is represented as follows:
A G B = α × F V C + β
where α and β are the gain and offset, respectively, for image forest FVC used to calculate the forest AGB.
Step 5: Forest AGB image generation using the forest FVC image—Suppose the forest FVC image is presented as FVC(i,j) and the image of forest AGB is represented as AGB(i,j), where (i,j) is the pixel location. Then the pixelwise forest AGB in kg is calculated as follows:
A G B ( i , j ) = α × { F V C ( i , j ) } + β
Step 6: Accuracy assessment—The accuracy of the forest AGB calculated from the optical remote sensing image was evaluated by ground validation. The ground validation was performed by comparing the AGB value between the field calculated and the image estimate at different ground locations. The accuracy of the result in percentage is represented as:
A c c u r a c y   ( % ) = n N × 100 ,
where N is the total number of validation points, and n is the number of validation points agreed with when equal or less than one standard deviation between image and field measurements.
Finally, the generalized forest AGB obtained by individual plot level in kg are standardized into tons per hectare (t ha−1).
Phase II. Forest AGB model evaluation using Sentinel-2 data.
To test and verify the effectiveness of the proposed method, experimentation in sequential procedure was applied on Sentinel-2 as an optical remote sensing image to calculate the forest AGB from the Tundi reserved forest test site, Jharkhand, India. The sequential steps in the forest AGB calculation are given below.
After preprocessing, the spatial resampling was initially performed to bring all of the spectral bands to 10 m spatial resolution (e.g., bands 2–4 with spatial resolution 10 m and bands 5–7, 8a with spatial resolution 20 m) and then resampled to 30 m spatial resolution, i.e., approximately equal to the field data spatial resolution.
The direct vegetation and nonvegetation (soil) spectral information extraction for the forest FVC calculation is tricky. Thus, different vegetation indices were used to quantify the vegetation information within the pixel of an image. In our experiment, for quantifying vegetation amount in a pixel of Sentinel-2, four vegetation indices were assessed: normalized difference vegetation index (NDVI), modified vegetation index (MVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI). The NDVI, MVI, SAVI, and MSAVI images were produced from the Sentinel-2 image using the following equations:
N D V I = ( N I R R E D N I R + R E D ) ,
M V I = ( N I R R E D ) ( N I R R E D ) + 0.5
S A V I = ( 1 + L ) × ( N I R R E D ) ( N I R + R E D + L ) ,   and
M S A V I = ( 2 × N I R + 1 ( 2 × N I R + 1 ) 2 8 × ( N I R R E D ) ) 2
where L is the canopy background adjustment factor (L value as 0.5 in reflectance data) and NIR and RED are the reflectance values in near infrared and red bands of the multiband remote sensing image.
In our experiment, NIR (band 8) and RED (band 4) were carefully chosen from the Sentinel-2 reflectance image to calculate these vegetation indices. The highest vegetation index value in an image was considered as a pure vegetation pixel whereas the lowest value was decided as a pure soil pixel. Consequently, pure pixels for vegetation and nonvegetation (soil) information were identified to calculate the forest FVC images from the vegetation indices calculated using Equation (4). The best vegetation index among vegetation indices for the forest AGB calculation was selected based on the correlation coefficient (R) and mean absolute error (MAE) between the ground forest FVC and image forest FVC. R is the degree of statistical similarity between a pair of variables, whereas MAE is a measure of errors between paired observations. R and MAE were calculated between the ground FVC and the FVC from Sentinel-2 by NDVI, MVI, SAVI, and MSAVI corresponding to ground sample locations. Based on the highest correlation and lowest MAE criteria, the MSAVI-based image FVC was chosen in AGB calculation. Further, the trend line by linear regression between the ground and MSAVI-based FVC was fitted, which is shown in Equation (13):
F V C i m a g e = 0.7314 × F V C G r o u n d + 0.0432   and   R 2 = 0.9995
In Tundi reserved forest, the predominant tree species is Shorea robusta (Sal). Accordingly, based on [27], the ground forest AGB was calculated using the following equation from the field data (Table 2):
A G B = 0.509 × μ D 2 H
where AGB is the forest above ground biomass (kg), μ (0.667) is specific gravity (tree stem density) (g cm−3), D is the DBH (cm), H is the tree height (m), and the constant 0.509 obtained from [27]. The calculated ground forest AGB from field data and the selected image forest FVC are shown in Table 5.
A trend line is obtained between calculated ground forest AGB and image-retrieved forest FVC by linear regression. The following model (Equation (15)) was obtained from 15 ground training samples (Table 5) and the remaining 7 ground samples were used for validation of the results obtained from the developed model (Table 6).
A G B ( i , j ) = 92361 × { F V C ( i , j ) } ,   and   R 2 = 0.7494
The accuracy of the forest AGB based on Sentinel-2 was assessed by comparing the remaining seven field measurements to the corresponding locations. Kindly note that the samples selected for training were not chosen for the validation to avoid the biasness in the accuracy assessment. Thus, training and validation samples were separated before trend modeling. For data processing and implementation of the proposed procedure and experimentation in forest AGB assessment, mainly the Interactive Data Language (IDL) programing and Environment for Visualizing Images (ENVI) software were used.

3. Results and Discussion

The forest AGB evaluation results from the study area using the methodology and experimental analysis described in Section 2 are produced below.
The estimated forest FVC images based on NDVI, MVI, SAVI, and MSAVI, respectively, are shown in Figure 3. The ground and image FVC calculated from field data and Sentinel-2 respectively, for each vegetation indices extracted from the corresponding locations, are shown in Table 3. The R and MAE were analyzed between the ground FVC and the FVC estimated using vegetation indices from Sentinel-2. The estimated R and MAE values are shown in Table 4. The results show that MSAVI-based image FVC delivered the highest correlation and lowest MAE. Based on the AGB empirical model [27], the calculated ground forest AGB from field data and the selected image forest FVC are shown in Table 5.
As per the derived results from the experimentation using the satellite data together with ground-based primary data, the image-based forest FVC was calculated through MSAVI. This demonstrated the highest correlation and least mean absolute error with respect to ground FVC compared to the other vegetation indices used in this study (Table 4; Figure 3). These comparisons revealed that the MSAVI was the best vegetation index for image-based forest FVC calculation. Further, image-based forest FVC demonstrated a linear relation with a high correlation value of 0.9995 derived using Equation (13).
The empirical model (Equation (14)) was useful in calculating ground forest AGB. Further, the linear model between the ground forest AGB and selected image-based forest FVC showed a good correlation value of 0.75 (Equation (15)). The derived model was applied to the image-based forest FVC to calculate image-based AGB of the study area (Figure 4). This model was trained using 15 among the 22 samples collected from the field. The accuracy of the Sentinel-2-based forest AGB was assessed by comparing the remaining seven field measurements to the corresponding locations. The standard deviation was 3166.50 kg per plot, which was calculated from the forest AGB estimate from all 22 locations. The result shows that all the evaluation between the image-derived and the field calculated forest AGB agreed within one standard deviation. The above evaluation was performed based on a single tree species, i.e., Shorea robusta. This tree species dominates in the study area by approximately 90%.
In this study, we have ignored the other tree species to avoid the complexity in the developed method. Thus, we can consider that this study provided approximately more than 90% accuracy. The uncertainty of the final outcome was tested using the chi-squared test. The chi-squared test value was 0.19, i.e., 19% of uncertainty in the estimate.
Finally, the developed model was used to calculate the forest AGB of the study area using the Sentinel-2 image (Figure 4). Based on the forest AGB map produced, the total calculated forest AGB of the study area is estimated at 7,914,875,916.57 kg (7,914,875.92 tons) in 170,704,500 m2 (17,070.45 hectares). Consequently, the study area contains forest AGB on an average of 463.66 tons per hectare.
Further, the efficacy of the method was compared to the Qazi et al. model [47] in forest AGB estimates. The Qazi et al. model for forest AGB estimate is a linear relationship between crown projected area (CPA) achieved from an optical remote sensing image and field based AGB.
The model above was derived for Sentinel-2 and ground AGB obtained from the study area, which is shown in Equation (16):
A G B ( i , j ) = 17419 × { C P A ( i , j ) } 7430.2 ,   and   R 2 = 0.667
Likewise, the effectiveness of the developed method was compared to the Pandit et al. model [9]. This model was developed based on random forest regression for forest AGB estimation. These models were applied to calculated forest AGB for the validation, which is shown in Table 5 and Table 6.
The results show that five with Qazi et al. model and six by Pandit et al. model among seven validation points matched within one standard deviation with the ground estimates. Note that the forest AGB standard deviation used for comparison was calculated from the total ground data. Further, the potency of the proposed approach was tested using correlation coefficient and mean absolute error.
The results achieved by this approach, the Qazi et al. [47] model, and the Pandit et al. [9] model were evaluated by comparing their outcomes with ground forest AGB estimates. The correlation between results from Sentinel-2 image and ground data by the above approach was higher (0.9937) compared to Qazi et al. model (0.9796) and Pandit et al. model (0.9884). Whereas the mean absolute error between outcomes from image and ground data by the proposed approach was lower (582.97) than the Qazi et al. model (18428.41) and the Pandit et al. model (1509.64). The results revealed that the developed approach is superior compared to the Qazi et al. [47] and Pandit et al. [9] models. The forest AGB evaluation by the Qazi et al. model was performed by NDVI-based CPA whereas AGB estimation by the proposed approach is achieved by MSAVI-based FVC. Similarly, a random forest-based regression model was developed for forest AGB valuation by Pandit et al. [9]. This model developed a relation between RED-edge-derived vegetation index and ground AGB using a random forest algorithm [9].
These results likewise revealed that the selection of the vegetation indices might also be important parameters in forest AGB estimation with accuracy. There could be a reason that MSAVI is more accurate compared to NDVI for vegetation cover recognition in the forest. Moreover, precise CPA estimation is difficult compared to FVC estimation from optical remote sensing data could be the other reason. Furthermore, the Pandit et al. model revealed that not only robust algorithm selection is valuable, but rather precise vegetation identification and evaluation are also crucial in forest AGB model development. However, the developed model considered vegetation index selection for precise forest cover quantification before forest AGB model development. The developed approach is straight forward and simple to use for accurate assessment of forest AGB. In addition, a generalized forest AGB approach is presented so that the developed approach can be applied to other optical remote sensing data for forest AGB estimation from different study locations. Overall, these results and analysis from the study area show that Sentinel-2 has remarkable potential for estimation of the forest AGB. Based on the developed approach and the Sentinel-2 image, the average forest AGB obtained was 463.66 tons per hectare from the study area, which effectively agreed with the ground observation-based estimates.
One of the key aims of this work was to develop a novel method for precise, accurate, and quick evaluation of the forest above ground biomass from satellite optical remote sensing data. Sentinel-2 satellite data was utilized for this purpose. The ground FVC from optical remote sensing imagery at each ground sample location was derived. Apart from various available vegetation indices, a new method has been developed for precise, accurate, and rapid evaluation and validation of above ground forest biomass estimation using optical remote sensing in Tundi reserved forest area, India. This forest area typically represents a subtropical forest type found abundantly in the region for which the study was undertaken. The ground validation and comparisons with the developed CPA were validated. This method demonstrated better results than any other known method developed so far. Using this method, AGB was derived to be 463 tons per hectare for the study area and further, the satellite-derived data were also compared with the ground estimated data. Finally, the desired level of accuracy was achieved using the model developed. This leads to understanding and appreciating the value of the research outcome from this work.
This study also revealed that the biophysical variables of the vegetation types obtained from Sentinel-2 are useful in the forest AGB calculation using local regression. However, this study assumed that the whole study area is composed of a single tree species, whereas in reality forests are composed of multiple tree species with geospatially varying amounts over undulating terrain conditions. In comparison with available datasets and existing techniques, however, the proposed method is highly useful in rapid forest AGB assessment. In addition, the availability of high quality optical multispectral remote sensing data on a global scale, free or at low cost from multiple sources, may help global society for forest AGB estimation.
Finally, these results lead to conclude that the model obtained quickly produces the forest AGB from optical remote sensing images directly with high accuracy. The use of optical remote sensing data is rapid and simple compared to microwave remote sensing data, which need complex data processing for vegetation biophysical parameters extraction in AGB estimation. Further, the previous methods [9,47] and microwave data in the forest AGB calculations depend on multiple factors, such as ecological, biotic, and topographic factors, including actual tree height, diameter, stem density, and shape of tree crown information.

4. Conclusions

The goal of this research was to develop a robust and cost-effective method for rapid forest AGB evaluation. Initially, ground FVC using a fraction of land cover by trees and image FVC using vegetation index from the optical remote sensing data were computed. Next, the ground forest AGB was derived from the ground data (tree density, tree height, and tree DBH) using an empirical model [27]. Subsequently, a relational model was developed between ground forest AGB and remote sensing FVC by blending this information by using a linear curve fitting approach.
To estimate the forest AGB, the devised model was applied to the FVC image derived from the optical remote sensing image. The effectiveness of the approach developed was assessed by utilizing Sentinel-2 imagery as test optical remote sensing data. The Tundi reserved forest area, located in Jharkhand state of eastern India, was selected as a test site for this study. The results obtained by the developed approach demonstrated excellent matching with the results achieved by ground-based estimates. The application of the developed method in Tundi reserved forest revealed that the test site contains forest AGB on an average of 463 tons per hectare. The result obtained by the proposed approach matches well with the result reported in previous studies from the study area. The result demonstrated more than a 90% match with the ground measurement.
This study still has a future improvement scope, which will employ topographic information in the developed model.

Author Contributions

All authors have made significant contributions to the manuscript. Conceptualization, Praveen Kumar and Mahendra Kumar Pal; methodology, Praveen Kumar and Mahendra Kumar Pal; software, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna; writing—original draft preparation, Praveen Kumar and Mahendra Kumar Pal; writing—review and editing, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna; formal analysis, Praveen Kumar, Mahendra Kumar Pal, Akhouri Pramod Krishna, and Thorkild Maak Rasmussen; validation, Praveen Kumar, Mahendra Kumar Pal, Akhouri Pramod Krishna and Thorkild Maak Rasmussen; data curation, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna; funding acquisition, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna; supervision, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna; project administration, Praveen Kumar, Mahendra Kumar Pal, Thorkild Maak Rasmussen and Akhouri Pramod Krishna. All authors have read and agreed to the published version of the manuscript.

Funding

There is no external funding available for this research work.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Field based primary data have been used. The satellite data used for this study is freely provided by the ′European Space Agency′ (ESA), Copernicus Sentinel-2 archived data, which has been duly acknowledged.

Acknowledgments

The authors would like to thank the ′European Space Agency′ for providing freely the Copernicus Sentinel-2 data used in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Geographic location, (b) field photographs, (c) false-color composite (FCC) image from Sentinel-2 of the study area.
Figure 1. (a) Geographic location, (b) field photographs, (c) false-color composite (FCC) image from Sentinel-2 of the study area.
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Figure 2. Flow diagram of the proposed methodology.
Figure 2. Flow diagram of the proposed methodology.
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Figure 3. Calculated forest fraction of vegetation cover (FVC) images based on (a) normalized difference vegetation index (NDVI), (b) modified vegetation index (MVI), (c) soil-adjusted vegetation index (SAVI), and (d) modified soil-adjusted vegetation index (MSAVI), from the study area.
Figure 3. Calculated forest fraction of vegetation cover (FVC) images based on (a) normalized difference vegetation index (NDVI), (b) modified vegetation index (MVI), (c) soil-adjusted vegetation index (SAVI), and (d) modified soil-adjusted vegetation index (MSAVI), from the study area.
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Figure 4. The forest AGB calculated from Sentinel-2 imagery in kg per pixel (a) and tons per hectare (b) for the Tundi reserved forest, Jharkhand, India.
Figure 4. The forest AGB calculated from Sentinel-2 imagery in kg per pixel (a) and tons per hectare (b) for the Tundi reserved forest, Jharkhand, India.
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Table 1. Band specification of the Sentinel-2 image.
Table 1. Band specification of the Sentinel-2 image.
SubsystemBand NumberWavelength Range (μm)Band Central Wavelength (μm)Bandwidth (nm)Spatial Resolution (m)
VNIR10.43–0.450.443 (Coastal aerosol)2060
20.46–0.520.490 (BLUE)6510
30.54–0.580.560 (GREEN)35
40.65–0.680.665 (RED)30
50.70–0.710.705 (Vegetation RED edge)1520
60.73–0.750.749 (Vegetation RED edge)15
70.77–0.790.783 (Vegetation RED edge)20
80.78–0.900.842 (NIR)11510
8a0.86–0.880.865 (Vegetation RED edge)2020
90.93–0.950.945 (Water vapor)2060
101.37–1.391.375 (SWIR—Cirrus)30
SWIR111.57–1.661.610 (SWIR)9020
122.10–2.282.190 (SWIR)180
Note: VNIR, visible and near infrared; SWIR, short-wave infrared; NIR, near infrared.
Table 2. Details of the ground data from the study area.
Table 2. Details of the ground data from the study area.
Sample Location NumberEastingNorthingNumber of TreesAverage Tree Height (m)Average DBH (cm) (at 4.5 ft Above Ground)
SL123°57′53.10″86°28′23.80″1710.971.01
SL223°58′06.38″86°23′08.47″1923.33142.49
SL323°54′49.91″86°19′30.53″2415.880.57
SL423°57′58.28″86°24′18.87″2319.791.64
SL523°57′21.22″86°22′35.17″2217.8104.33
SL623°57′40.13″86°23′06.37″2019.8495.18
SL723°57′05.10″86°22′26.32″1615.55104.23
SL823°53′59.99″86°18′55.75″1517.497.02
SL923°56′51.50″86°22′22.10″1612.692.76
SL1023°54′53.80″86°19′35.49″2212.8687.76
SL1123°58′26.81″86°25′03.14″159.6582.56
SL1223°58′27.79″86°26′25.76″1710.577.59
SL1323°54′51.86″86°17′49.35″219.873.48
SL1423°58′04.77″86°27′31.11″1813.475.88
SL1523°58′19.68″86°25′56.25″1912.875.34
SL1623°57′39.16″86°22′51.61″1813.678.12
SL1723°57′02.19″86°19′53.71″2214.3577.62
SL1823°58′04.77″86°23′01.88″278.7567.34
SL1923°57′37.53″86°27′11.29″2410.868.41
SL2023°56′41.77″86°20′56.10″1813.677.78
SL2123°57′34.62″86°20′45.56″1611.971.27
SL2223°58′18.06″86°23′36.30″2511.8566.6
Table 3. Ground forest FVC and image forest FVC calculated from different vegetation indices.
Table 3. Ground forest FVC and image forest FVC calculated from different vegetation indices.
Sample Location NumberFVC (GROUND)FVC (NDVI)FVC (MVI)FVC (SAVI)FVC (MSAVI)
SL10.290.580.560.570.26
SL20.990.980.970.970.77
SL30.670.780.770.770.54
SL40.910.900.900.890.71
SL50.900.930.920.920.71
SL60.840.900.900.890.66
SL70.570.800.800.780.47
SL80.560.780.770.770.46
SL90.410.710.700.700.35
SL100.550.700.680.690.44
SL110.260.590.560.570.24
SL120.310.590.570.580.27
SL130.330.610.570.580.29
SL140.400.620.600.610.34
SL150.410.630.600.610.34
SL160.420.670.640.640.35
SL170.540.700.670.680.44
SL180.350.550.500.530.29
SL190.390.600.560.580.33
SL200.420.640.620.630.35
SL210.300.590.560.570.26
SL220.440.630.590.610.36
Table 4. Correlation coefficient (R) and mean absolute error (MAE) of image-based forest FVC from different vegetation indices with ground forest FVC.
Table 4. Correlation coefficient (R) and mean absolute error (MAE) of image-based forest FVC from different vegetation indices with ground forest FVC.
Vegetation IndicesNDVIMVISAVIMSAVI
R0.96960.95990.96770.9981
MAE0.19410.17290.17930.0947
Table 5. Ground forest AGB and selected image forest FVC.
Table 5. Ground forest AGB and selected image forest FVC.
Sample Location NumberAGB (kg) (Ground)FVC (MSAVI)
SL118659.890.26
SL2160815.210.77
SL334821.500.54
SL456166.840.71
SL565778.210.70
SL661020.630.66
SL757353.440.47
SL855605.200.46
SL936807.440.35
SL1033626.200.44
SL1122331.110.24
SL1221460.730.27
SL1317964.200.29
SL1426194.070.34
SL1524666.350.34
SL1628177.790.35
SL1729352.340.44
SL1813470.940.29
SL1917159.580.33
SL2027933.050.35
SL2120521.260.26
SL2217844.750.36
Table 6. Comparison and validation of the image retrieved forest AGB with ground forest AGB.
Table 6. Comparison and validation of the image retrieved forest AGB with ground forest AGB.
Sample Location NumberGround AGB (in kg)Image AGB (Proposed)Image AGB (Qazi et al. Model)Image AGB (Pandit et al. Model)
SL118659.8918840.1017989.5418469.38
SL1729352.3430196.8334432.8932746.29
SL1813470.9414310.0913934.3215583.64
SL1917159.5817551.7916930.8317184.53
SL2027933.0528272.1831030.8630734.47
SL2120521.2619412.0324418.6121435.69
SL2217844.7518221.1620692.2218974.25
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