Conference PaperPDF Available


Suman Sinha
, A. Santra
, S. S. Mitra
, C. Jeganathan
, L. K. Sharma
, M. S. Nathawat
, A. K. Das
and S. Mohan
Haldia Institute of Technology, Haldia 725657, India,
Haldia Institute of Technology, Haldia 725657, India,
Haldia Institute of Technology, Haldia 725657, India,
Birla Institute of Technology, Mesra 835215, India,
Central University of Rajasthan, Ajmer 305817, India,
Indira Gandhi National Open University, New Delhi 110068, India,
Space Application Centre (ISRO), Ahmedabad 380015, India,
Physical Research Laboratory, Ahmedabad, 380059, India,
KEY WORDS : ALOS PALSAR, Radarsat-2, COSMO-Skymed, Forest, Biomass.
Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon
sequestration in forests. Proper inventory of forest aboveground biomass (AGB) is required for accounting carbon
emissions that forms the most vital part of the carbon cycle modeling and climate change mitigation programs in
context to Reducing Emissions from Deforestation and Forest Degradation (REDD). Remote Sensing (RS)
technology provides cost and time effective means for accurate temporal monitoring over large synoptic extents at
local to global levels, and hence, is beneficial over conventional methods. The study presents a suitable approach
for estimating AGB through the synergic use of multi-frequency X-, C- and L-band Synthetic Aperture Radar
(SAR) data over tropical deciduous mixed forests of Munger (Bihar, India). Backscatter values generated from the
raw SAR images were correlated with field-based AGB values and then regressed to generate best-fit models for
AGB estimates with single and combined frequencies of COSMO-Skymed (X-band), Radarsat-2 (C-band) and
ALOS PALSAR (L-band). Among all the models for AGB estimation, the integrated model involving X, C- and L-
bands showed the best results with r
=0.95, RMSE=14.81 t/ha and Willmott’s index of agreement of 0.95. Resulting
modeled AGB were converted to carbon (C) and carbon dioxide (CO
) equivalents using conversion factors. Hence,
the study proposed L-band for single frequency analysis and the combination of X-, C- and L-bands for multi-
frequency analysis for tropical forest AGB and C estimation. The study revealed information regarding the spatial
distribution and quantification of forest AGB and C required for REDD monitoring.
‘400 ppm World’; an universal alarming situation where the global carbon dioxide (CO
) concentration surpassed
the 400 ppm threshold level during 2016; a phenomenon which is permanent not likely to revert back in future
(Betts et al., 2016). India being a mega-biodiversity country harbors forests that account for more than one fifth of
the geographical area. Indian tropical forests are likely to experience extreme hasty and significant climate and
vegetation changes over the next decades (Ravindranath et al., 2006). Globally this is a serious issue as tropical
forests sequester one fifth of the global carbon (C) stock, and almost one half of the above-ground C, stored in
vegetation of all biomes (Hunter et al., 2013). Biophysical indicators of forest C storage, viz. the above-ground
biomass (AGB) are important for realizing the terrestrial C equilibrium (Sinha et al., 2017). Sequestration of C in
the vegetation is the only possible viable strategy in milieu of Reducing Emissions from Deforestation and forest
Degradation (REDD) to maintain the atmospheric C balance and account for the CO
released from forests
(Waikhom et al., 2017). Henceforth, accurate estimates of biomass are a prerequisite for forest C accounting under
REDD framework (Sinha et al., 2016).
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
Sinha et al. (2015) have outlined detailed information regarding the biomass estimation methods, wherein, the
remote sensing (RS)-based approaches have clearly outshined other methods. Optical sensors are recurrently used
for AGB estimation (Kumar et al., 2013; Sharma et al., 2013), however, saturates early owing to poor sensitivity to
forest parameters, unlike Synthetic Aperture Radar (SAR) and Light Detecting and Ranging (LiDAR) sensors
(Sinha et al., 2015). Though both the systems are sensitive to forest spatial structure and standing biomass, SAR is
favoured due to its wall-to-wall coverage which is absent in all LiDAR systems (Su et al., 2016). Currently SAR is
extensively used in retrieving forest biomass (Sinha et al., 2015).
Usually SAR data are acquired in X, C, L bands and sometimes in S and P bands as well. SAR backscatter from
longer wavelengths, like L- and P-bands relate more to the forest biophysical parameters due to their greater
penetration capabilities through the vegetation surfaces and are scattered/attenuated by trunk and main branches
(Sinha et al., 2015). Synergic use of radar and optical sensor data has the potential to improve the estimation of
forest AGB (Sinha et al., 2016). However, optical sensors suffers several drawbacks, like occurrence of frequent
clouds in the tropics hampering the acquisition of the high quality satellite data, lack of volumetric estimations due
to absence of penetrability of visible bands and low saturation levels of the spectral bands for biomass estimation
(Sinha et al., 2015). Hence, the use of integrated multi-frequency SAR reveals greater potential for AGB estimation
that can possibly overcome the limitations of optical sensors and single SAR sensors. Alappat et al. (2011) applied
synergic model integrating SAR C- and L-bands and Englhart et al. (2011) used SAR X- and L-bands for biomass
assessment. This study in addition integrates SAR backscatter data from X-, C- and L-bands to investigate
relationships with field AGB to derive a synergic model to estimate above-ground bole biomass, from which C and
has been enumerated.
2.1 Site under investigation
Munger forests in the state of Bihar (India) comprising the Bhimbandh Wildlife Sanctuary with geographic
coordinates of 25º19'30''N-24º56'50''N latitude and 86º33'33''E-86º11'51''E longitude, covering an area of approx
672.5 km
has been considered as the test site for investigation (Figure 1). Summer temperature reaches 45
C, while
winter experiences nearly 3-9
C. The average annual rainfall is around 1079 mm.
Figure 1: Location of the study site
2.2 Land use land cover details
The backdrop of the site is a moist-deciduous mixed forest with over 89% of the area under forests. The study site
mainly comprises of open and degraded mixed forests, with Shorea robusta, Acacia catechu, Madhuca longifolia,
Dendrocalamus strictus, Diospyros melanoxylon and Terminalia tomentosa as the dominant floral species (Sinha et
al., 2013). Located south of River Ganges, the entire area is drained by several small rivers, like Kiul, Man,
Narokol, Morwe, Dudhpanian, Kandani, etc. The forest is a virgin patch with limited disturbances in terms of
deforestation mainly due to dispersed anthropogenic activities like settlements, agricultural development and
plantations and to some extent mining activities. Conservation of the forest is of high relevance for the conservation
of biodiversity and also in context to REDD/REDD+.
 
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
2.3 Data input
Multi-frequency SAR datasets of COSMO-Skymed, Radarsat-2 and ALOS PALSAR were procured to model for
AGB prediction. The COSMO-Skymed data is HH/VV dual polarized X-band obtained through PINGPONG
imaging mode with 15m (resampled to 25m) spatial resolution and 30km swath width. The Radarsat-2 data is
Standard Quad-pol C-band with 25m spatial resolution and 25km swath width. The ALOS PALSAR data is L-band
acquired in Fine Beam Single (HH) and Dual (HH,HV), and Quad Polarimetric (HH,HV,VH,VV) mode with 25m
spatial resolution, and off-nadir angles of 34.3° and 21.5°, and swath width of 70 and 30 km respectively.
Simultaneously, primary data was generated in terms of field inventory that included in-situ information generated
from field sample plots, like forest types, canopy density, species composition, stand height, and girth at breast
height (GBH).
2.4 In-situ field inventory for field biomass estimation
A total of 45 square sample plots of 0.1 ha (Hectare) were randomly selected, 36 of which were considered for
developing models, while the remaining for validating the models. The three most important data collected from
field within each plot i.e., tree species, stand height and GBH; from which the diameter at breast height (DBH) were
used to calculate the volume using the species-specific regional volumetric equations of FSI (1996). AGB is then
estimated by multiplying the resulting volume with the tree-specific specific gravity of FRI (1996). Global
Positioning System (GPS) was used to collect the latitude-longitude information of the plots for importing the
information in GIS framework. Volumetric equations and specific gravity of the tree species for the study site are
mentioned in Sinha (2016).
2.5 SAR processing
Raw SAR datasets were preprocessed, rectified, geocoded and calibrated using a series of standard steps in
SARscape software to generate the backscatter image following the equation (Sinha et al., 2016):
0ADNa +×=
= backscatter coefficient or sigma nought values in decibels (dB),DN is the power (or intensity) image, A
is the calibration factor that vary with sensor type. A
= -115 dB for ALOS PALSAR, A
= -59.62 dB for HH
polarized COSMO-Skymed, A
= -58.88 dB for VV polarized COSMO-Skymed. Radarsat-2 has different values of
for each line and is processed in Geomatica.
2.6 Model development
SAR backscatter values were regressed to the 36 field-based AGB values to find the best fit model for calculating
AGB. The field-based estimated AGB was correlated with modeled AGB for 9 additional random points for
validation of the best-fit model. The model performance was evaluated based on certain statistical measures (Sinha
et al., 2016). Resulting modeled AGB (in t/ha) were converted to carbon (C) and carbon dioxide (CO
) equivalents
using conversion factors of 0.5 and 3.67 respectively (Mushtaq & Malik, 2014, Rashid et al., 2016).
3.1 SAR interactions with AGB
X-, C- and L-bands were used to develop the AGB model. Figure 2 shows the relationship between the SAR
backscatter values and the in-situ plot AGB values, which showed the existence of a logarithmic relation. The figure
also revealed that X-band saturated earliest, then the C-band, and lastly the L-band. The saturation level depended
on the radar frequency or wavelength, radar wave polarization and vegetation types.
Figure 2: Relationship between multi-frequency SAR backscatter with plot AGB
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
X-band COSMO-Skymed VV polarized data interacted with the upper part of the canopy (leaves, small branches,
etc.); as revealed by the increased r
value in comparison to HH. With minimum penetration capability, X-band has
the minimum interaction with the trunk and the main branches, so has the poorest relationship with the bole AGB.
C-band having relatively greater penetration within the canopy layer interacts with the secondary branches and this
is evident from higher r
values. L-band ALOS PALSAR showed the best correlation with the bole AGB due to its
ability to penetrate the canopy layer and interact with the trunk. HH polarized data of L-band showed the greatest
interaction with the trunk due to its vertical structure. Hence, HH polarization was most sensitive towards the bole
AGB. It was observed that like-polarizations had high backscatter values than cross-polarizations and increase in
the wavelength leads to increase in the penetration capability of the radar signals, thus providing more accurate
information related to the bole above-ground biomass. Table 1 documents the correlation values. In this study, X-
band saturates earlier at about 40-50 t/ha, while C-band saturates next nearly at 100-120 t/ha, while, saturation is
highest for L-band among the three, at about 160-180 t/ha (Figure 2).
Table 1: Coefficient of determination (r
) between SAR backscatter and plot AGB
SAR datasets Polarization r
X-band COSMO-Skymed HH 0.01
X-band COSMO-Skymed VV 0.02
C-band Radarsat-2 HH 0.46
C-band Radarsat-2 HV 0.34
C-band Radarsat-2 VV 0.43
L-band ALOS PALSAR HH 0.85
L-band ALOS PALSAR HV 0.56
3.2 Integrated AGB model
Three best-fit models (Equations 2, 3 and 4) were generated from this logarithmic relationship between backscatter
values from each of the three SAR datasets (X-, C- and L-band respectively) and the plot AGB based on the
information in Table 1 from 36 plots used for model development and calibration. Synergic modeling was
developed using only those plots among the total 36 that were found in all the SAR datasets. An integrated AGB
model was developed using Multiple Linear Regression (MLR) of Equations 2, 3 and 4 where the data were used in
combination of all three datasets, expressed as Equation 5. The models are enlisted in Table 2. The models were
evaluated based on certain statistical calculation, also documented in Table 2. The table indicated that the synergic
model integrating SAR X-, C- and L-bands (Equation 5) showed the best results among all; while Equation 4
involving just the L-band showed better results among all the single band models.
Table 2: AGB model evaluation
Eq. Model SAR data used r
X (VV) 0.01 46.18
C (HH) 0.28 37.24
L (HH) 0.87 16.06
L (HH), C (HH), X (VV) 0.90 15.29
3.3 Validation
The AGB models were statistically validated with nine additional plot AGB data and the corresponding statistical
measures, like r
, RMSE, slope, average absolute accuracy () and Willmott's Index of agreement (d) were executed.
The results are summarized in Table 3. The table confirmed that the integrated AGB model (Equation 5) showed the
best results with the highest r
value of 0.954, least RMSE of 14.813 t/ha, good model accuracy of about 79% and
greatest d value of 0.95. Significantly high r
, model accuracy and d values nearing unity and low RMSE value
reveal the acceptability of the model. However, Equation 4 with L-band information also showed promising results
amongst the single sensor models with a fairly high r
value of 0.713, moderate RMSE of 22.34 t/ha, good model
accuracy of 61.7% and d value of 0.88.
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
Table 3: AGB model validation
Eq. r
RMSE (t/ha) Slope d
2 0.094 32.151 0.105 39.478 0.306
3 0.002 50.083 -0.050 7.961 -0.095
4 0.713 22.340 0.871 61.768 0.883
3.4 AGB and C maps
The AGB map represented as Figure 3 was prepared from Equation 5 in GIS and reclassified in ten classes
according to biomass levels from very low (<25t/ha and 25-50 t/ha), low (50-75t/ha and 75-100t/ha), moderate
(100-125t/ha and 125-150t/ha), high (150-175t/ha and 175-200t/ha) to very high (>250t/ha). Resulting modeled
AGB were converted to carbon (C) and carbon dioxide (CO
) equivalents using conversion factors and reclassified
in five classes. Figure 4 illustrates both C and CO
spatial distribution map. Both the figures 3 and 4 have white or
empty regions within the boundary of the study area that lack the input SAR data owing to data restrictions. North
portion of the of study site in the figures show erroneous results due to presence of non-overlapping portions of
multi-sensor satellite data, demarcated in blue dotted line.
Figure 3: AGB map developed from integrated model
Figure 3 portrays less vegetative parts with low biomass levels of <50 t/ha at the vicinity of the boundary of the
study area, water bodies and the built-up regions that are concentrated in the periphery and at the central parts. Most
of the forested region was observed to lie within the biomass range of 25–100 t/ha, with an average value of 56.24
t/ha. Most of the high density vegetation was observed to have biomass ranging from 75–125 t/ha, mostly covering
the interior parts of the study area; however, some scattered areas with even higher biomass values that too are
generally restricted to the interior regions. Likewise, Figure 4 shows similar observation as C and CO
observed to be concentrated more in the interior parts of the area. Average C was calculated to 28.12 t/ha, while
was 103.2 t/ha. It can be also noticed that the relative early saturation of biomass with the use of single SAR
frequency data alone can be counteracted with the integrated use of multi-frequency SAR data due to the better
relationship of AGB compartments to each of the SAR frequency bands. Hence, the integrated multi-frequency
SAR model provided the most accurate result for predicting AGB.
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
Figure 4: C and CO
spatial map
In this ‘400 ppm World’ , global climate change is the most alarming situation that this world is experiencing. In this
context, REDD and associated concepts are gaining attention; henceforth, biomass/carbon assessment is becoming
crucial to address this issue. The current study targets in using multi-frequency SAR to assess above-ground bole
biomass and in-turn the forest carbon stock over a tropical deciduous heterogeneous virgin forest patch of Munger
in India. Synergic use of multi-frequency SAR has the potential to augment the AGB estimations in comparison to
any optical data as well as any single frequency spaceborne SAR data till date. Out of the X-, C- and L-bands, the
best model predicting AGB comprises of L-band information. Subsequently, the estimation improves on integrating
all the three SAR wavelength bands. The exponential model was observed as the best fit model for estimating
biomass on regressing SAR backscatter values to plot estimated AGB. The integrated model was validated and r
0.95, RMSE of 14.81 t/ha, model accuracy of 79% and Willmott’s index of agreement of 0.95 was calculated
without much over or under-estimation as denoted by the slope value of 0.96. The consequence of the approach in
resolving the relative early saturation of biomass with optical RS data or single SAR frequency data alone is evident
thus can be counteracted with the integrated use of multi-frequency SAR data due to the better relationship of AGB
Hence, in this study, a synergy regression model for predicting AGB was developed with synergic use of SAR
multi-frequency X-, C- and L-band information that was further transformed to generate C stock and CO
models in GIS. Quantifying multi-temporal changes in CO
via this approach can account for the climate change.
The integrated multi-frequency SAR approach adopted in the study gave valuable information of the spatial
distribution and quantification of the forest biomass and carbon; important for REDD monitoring.
The authors sincerely acknowledge Space Application Centre (ISRO, India) for providing technical help and JAXA
(Japan), CSA (Canada) and ASI (Italy) for providing SAR data through ALOS-RA Cosmo Skymed A.O. project.
The first author expresses sincere gratitude to Science and Engineering Research Board (SERB), Department of
Science and Technology (DST), Government of India for providing funds under SERB National Post-Doctoral
fellowship (SERB NPDF) scheme (File Number: PDF/2015/000043).
Paper presented in the 38th Asian Conference on Remote Sensing, October 23-27, New Delhi (INDIA)
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... vegetation, soil, etc.). The effect of frequency on the backscatter response from different targets is addressed by several authors (Joerg 2018;Martinis and Rieke 2015;Suman et al. 2017). Low-frequency microwaves such as P-band and L-band have higher penetration capability through the soil and vegetation and hence find wide applications in subsidence monitoring. ...
The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrela-tion is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR data-sets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. ARTICLE HISTORY
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Aboveground biomass and carbon stock in the largest sacred grove of Manipur was estimated for trees with diameter >10 cm at 1.37 m height. The aboveground biomass, carbon stock, tree density and basal area of the sacred grove ranged from 962.94 to 1130.79 Mg ha⁻¹, 481.47 to 565.40 Mg ha⁻¹ C, 1240 to 1320 stem ha⁻¹ and 79.43 to 90.64 m² ha⁻¹, respectively. Trees in diameter class of 30–40 cm contributed the highest proportion of aboveground biomass (22.50–33.73%). The aboveground biomass and carbon stock in research area were higher than reported for many tropical and temperate forests, suggesting a role of spiritual forest conservation for carbon sink management. © 2017 Northeast Forestry University and Springer-Verlag GmbH Germany
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Accurate estimates of forest biomass are increasingly important in relation to sequestration of carbon by forest trees. Satellite remote sensing is a useful tool for biomass estimation and monitoring of forest ecological processes. Microwave synthetic aperture radar (SAR) can increase the accuracy of estimations of forest biomass in comparison to optical remote sensing, due to the unique capacities of SAR, including high penetrability, volumetric scattering, interaction with surface roughness, and dielectric property. We studied the potential of multi-polarized C-band Radarsat-2, a SAR technology, with HH, HV and VV polarization for estimating biomass of moist tropical Indian forest. Backscatter values are correlated with field-based biomass values and are regressed to generate models for estimating biomass. HH polarization provided maximum information regarding tree biomass. A coefficient of determination of 0.49 was calculated for HH polarized C-band image with in situ measurements. An exponential model was proved to be best suited for estimating forest biomass. Correlation of 0.62 and RMSE of 24.6 t ha⁻¹ were calculated for the relationship between estimated and predicted biomass values for the best fit model. The average absolute accuracy of the model was 61%, while Willmott’s index of agreement was 0.87. Results suggest that most of the biomass of the area ranged within 70 t ha⁻¹ a probably due to the saturation of C-band around 60–70 t ha⁻¹ for tropical forests.
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Tropical forests account for approximately half of above-ground carbon stored in global vegetation. However, uncertainties in tropical forest carbon stocks remain high because it is costly and laborious to quantify standing carbon stocks. Carbon stocks of tropical forests are determined using allometric relations between tree stem diameter and height and biomass. Previous work has shown that the inclusion of height in biomass allometries, compared to the sole use of diameter, significantly improves biomass estimation accuracy. Here, we evaluate the effect of height measurement error on biomass estimation and we evaluate the accuracy of recently published diameter : height allometries at four sites within the Brazilian Amazon. As no destructive sample of biomass was available at these sites, reference biomass values were based on allometries. We found that the precision of individual tree height measurements ranged from 3 to 20% of total height. This imprecision resulted in a 5–6% uncertainty in biomass when scaled to 1 ha transects. Individual height measurement may be replaced with existing regional and global height allometries. However, we recommend caution when applying these relations. At Tapajós National Forest in the Brazilian state of Pará, using the pantropical and regional allometric relations for height resulted in site biomass 26% to 31% less than reference values. At the other three study sites, the pan-tropical equation resulted in errors of less that 2%, and the regional allometry produced errors of less than 12%. As an alternative to measuring all tree heights or to using regional and pantropical relations, we recommend measuring height for a well distributed sample of about 100 trees per site. Following this methodology, 95% confidence intervals of transect biomass were constrained to within 4.5% on average when compared to reference values.
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The changes in the land use and land cover (LULC), above ground biomass (AGB) and the associated above ground carbon (AGC) stocks were assessed in Lidder Valley, Kashmir Himalayas, India using satellite data (1980-2013), allometric equations and phytosociological data. Change detection analysis of LULC, comprising of 8 vegetation and 5 non-vegetation types, indicated that 6% (74.5 km2) of the dense evergreen forest has degraded. Degraded forest and settlement increased by 20 km2 and 52.8 km2 respectively. Normalized Difference Vegetation Index (NDVI) was assessed and correlated with the field-based biomass estimates to arrive at best-fit models for remotely sensed AGB estimates for 2005 and 2013. Total loss of 1.018 Megatons of AGB and 0.5 Megatons of AGC was estimated from the area during 33 years period which would have an adverse effect on the carbon sequestration potential of the area which is already facing the brunt of climate change.
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Abstract Forest plays a vital role in regulating climate through carbon sequestration in its biomass. Biomass reflects the health and environmental conditions of a forest ecosystem. In context to the climate change mitigation mechanisms like REDD (reducing emissions from deforestation and forest degradation), an extensive forest monitoring campaign is especially important. Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes. Limitations of the conventional forest inventory procedures, like the extensive cost, labor and time, can be overcome through integrated geospatial techniques. Optical sensor or SAR data are suitable for extracting information about simple and homogeneous forest stand sites. However, optical sensors face serious limitations, specifically in tropical regions, like the cloud cover that SAR can overcome along with targeting saturation and penetration aspects. Simultaneous use of spectral information and image texture parameters improves the biomass assessment over undulating terrain and in radical conditions. Also, synergic use of multi-sensor optical and SAR has better potential than single sensor. Interferometric (InSAR) and polarimetric (PolSAR) SAR or a combination of the both (PolInSAR) serves as effective alternatives. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments. However, SAR data have its own limitations and complexities. Identifying, understanding and solving major uncertainties in different stages of the biomass estimation procedure are critical. In this regard, the current study provides a review of radar remote sensing-based studies in forest biomass estimation.
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The present study attempts to generate land-use/land-cover (LULC) and forest map using standard False-Colour composite (FCC) of satellite imagery of IRS P6 LISS III for a deciduous forest area of Munger in Bihar, India. The method adopted is an integration of geospatial techniques and field data to accurately map the LULC of the study area. Forest classification through unsupervised, supervised and visual interpretation is carried out to observe a corresponding gradual enhanced classification accuracy of the methods applied. Nearly 89% of the area is covered under forest out of which the dominant forest types are mixed Shorea robusta (Sal), Acacia catechu (Khair) and Dendrocalamus sp. (Bamboo) forests. The major constraint of the study is the inaccessibility of most of the area. The integrated geospatial approach overcomes this problem to a great extent and reveals its potential for gathering information from remote areas without directly intervening in the area. The study proposes the application of satellite remote sensing and geospatial techniques for future environmental monitoring and forest dynamics studies.
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Optical remote sensing data have been extensively used to derive biophysical properties that relate forest type and composition. However, stand density, stand height and stand volume cannot be estimated directly from optical remote sensing data owing to poor sensitivity between these parameters and spectral reflectance. The ability of microwave energy to penetrate within forest vegetation makes it possible to extract information on both the crown and trunk components from radar data. The type of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms within the canopy or trunk. This study mainly presents experimental results obtained with airborne E-SAR using polarimetric C and L bands over the tropical dry deciduous forest of Chandrapur Forest Division, Maharashtra. A detailed documentation of the relationship between SAR C & L bands backscattering and forest stand variables has been provided in the present study through linear correlation. Linear correlation of the single channel SAR derived estimates with the field measured means show a good correlation between L HV backscattering coefficient with stand volume (r2 = 0.71) and L HH backscattering coefficient with stand density (r2 = 0.75). The results imply that SAR data has significant potential for stand menstruation in operational forestry.
The recent El Nino event has elevated the rise in CO2 concentration this year. Here, using emissions, sea surface temperature data and a climate model, we forecast that the CO2 concentration at Mauna Loa will for the first time remain above 400 ppm all year, and hence for our lifetimes.
Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R 2 = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R 2 = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R 2 = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.