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Forest Canopy density is a major factor in evaluation of forest status
and is an important indicator of possible management interventions. Estimation
of forest canopy cover has recently become an important part of forest
inventories. The increasing use of satellite remote sensing for civilian use
has proved to be the most cost effective means of mapping and monitoring
environmental changes in terms of vegetation and non-renewable resources,
especially in developing countries. Data can be obtained as frequently as
required to provide information for determination of quantitative and
qualitative changes in terrain. Forests as a part of the wild life interaction
of the human societies have a special place in economic development and
stability of water and soil resources in most of the countries around the
world. But because of various reasons such as increase and development of
population, increasingly altering forests for other unsuitable applications
such as agriculture, providing energy and fuel, millions of hectares from this
natural resource are destroyed every year and the remainder of the surfaces
changes quantitatively and qualitatively. For better management of the forests,
the change of forest area and rate of forest density should be investigated
using advanced techniques. It is possible that there is no change in the area
of forest during the time but the density of forest canopy is changed. This
model calculates forest density using the four indexes of soil, shadow, thermal
and vegetation. For this, the LANDSAT TM and OLI images from different dates
and years are used. At first, the forest density map was prepared by using
Biophysical Spectral Response Modeling for two images. Overall accuracy 84.25%
for 2007 and 80.06% for 2017 and kappa coefficient 0.8236 for TM 2007 and kappa
coefficient 0.8225 for OLI 2017 image was achieved. Then, the changing of the
area and forest density during these periods was distinguished. Aster DEM is
used to calculate Aspect, Elevation, Hill shade maps. Aspect, Elevation and
Hill shade map point out the dense forest from our region. At last, the change
in area of forest is distinguished by using the Forest Canopy Density model map
and map formed by interpretation of Aspect, Elevation, Hill shade map.
Keywords: Forest canopy density model, Aster DEM, Aspect, Elevation, Hill shade
map
INTRODUCTION
Forest canopy cover, also known as canopy
coverage or crown cover, is defined as the proportion of forest floor covered
by the vertical projection of the tree crowns [1]. The human intervention in
nature reduces the number of trees per unit area and canopy closure [2].
International Tropical Timber Organization (ITTO) has developed a new
methodology wherein, forest position is the value put on the base of its cover
relation between mass and size. Forest stands or cover types consist of a plant
community made up of trees and other woody vegetation, growing more or less
closely together [3-6]. It has become compulsory to monitor the current status
of forests .Understanding of altitudinal variation of forest cover density in
Jharkhand state area thus plays a major role in this context. The forest cover
condition defines land cover classification and various altitudinal ranges with
forest cover density. Forest density expressing the stocking status build up
single major stand physiognomic character of forest: so for the scientific
forest management,
The Forest Canopy Density (FCD) Mapping and
Monitoring Model utilizes forest canopy density as an essential parameter for
characterization of forest conditions. FCD data indicates the degree of
degradation, thereby also indicating the intensity of rehabilitation treatment
that may be required. The remote sensing data used in FCD model is LANDSAT TM &
OLI data. The FCD model comprises of bio-physical phenomenon modeling and
analysis utilizing data derived from four indices: Advanced Vegetation Index
(AVI), Bare Soil Index (BI), Shadow Index or Scaled Shadow Index (SI, SSI) and
Thermal Index (TI). It determines FCD by modeling operation and obtaining from
these indices. The canopy density is calculated in percentage for each pixel.
The FCD model requires less information of ground truth just for accuracy check
and so on. FCD model is based on the growth phenomenon of forest.
OBJECTIVES OF THE STUDY
The main aim of this study was Forest Canopy
Estimation using FCD model using satellite images of Noamundi and Jagarnathpur
areas in Jharkhand. To accomplish this, the specific objectives are as follows:
1.
To derive Land
Use Land Cover map (LU/LC) of the study area (Noamundi and Jagarnathpur).
2.
To derive
forest canopy indices such as Advance Vegetation Index, Bare Soil Index, Canopy
Shadow Index, Thermal Index using satellite data in the year 2007 and 2017 for
studying temporal dynamics of canopy cover.
3.
To estimate
chlorophyll content of the species present in the study area with the
instrument.
STUDY AREA
Noamundi and Jagarnathpur district forms the
southern part of Jharkhand state and is the largest district in the state. The
district spreads from 21.97°N to 23.60°N and from 85.00°E to 86.90°E.The forest
covers an area of 820 km² (Figure 1).
DATA USED AND METHODS
Satellite imageries and ancillary data were collected in order to
identify forest canopy estimation using forest canopy density model. The image
data that were used for this study are Landsat TM and OLI. Topographic maps of
Open Series at the scale of 1:50,000 were procured from the Survey of India
(SOI). Study area boundary was generated from collateral or ancillary data that
was a block-level map of the West Singhbhum, the study area. The methodological
flow chart of the study, Figure 2
represents all about the procedure, methods, and steps used to achieve the main
aim of the study that is to map the Forest Canopy Estimation Using Forest
Canopy Density Model (FCDM) and Satellite Data of Noamundi and Jagarnathpur
(West Singhbhum District), Jharkhand.
RESULTS AND DISCUSSION
Objective 1
Using supervised classification method LANDSAT 8, OLI and 5 TM
classifies in the year 2017 and 2007. While preparing land use land cover
change of Noamundi and Jagarnathpur, it was distributed into eight classes’
viz., settlement, agriculture, river, barren land, surface water, scrub forest,
agriculture fallow land and dense vegetation. The dominating classes in land
use land cover change for the year 2017 were dense vegetation (2972.12 sq. km),
settlement (11.43 sq. km) and scrub forest (350.87 sq. km). The dominating
classes in land use land cover change for the year 2007 were dense vegetation
(3257.04 sq. km), settlement (06.24 sq. km) and scrub forest (233.71 sq. km).
In the year 2017, settlement showed a major increase as a result of which,
dense vegetation and light vegetation decreased rapidly and the surface water
content decreased due to various factors. In the year 2007 the settlement was
less as compared to the year 2017 as a result of which the forestland was
greater in amount (Figures 3 and 4).
Objective 2. Using forest
canopy density model
·
We have to take TIFF file where each
band represents where it is downloaded from USGS.
·
Total angle radiation and solar
correction is applied.
·
Now we are interested to determine the
following indices that are used for calculating Forest Canopy Density.
Advance vegetation index
NDVI is unable to highlight subtle differences in canopy density. It
has been found to be improved by using power degree of the infrared response.
The calculated index has been termed as advanced vegetation index (AVI). It has
been found to be more sensitive to forest density and physiognomic vegetation
classes. For this reason, it is getting better by using power degree of the
infrared response.
AVI = {(B4+1) (256-B3) (B4-B3)} 1/3
AVI = {(B5+1) (65536-B4) (B5-B4)} 1/3
The value AVI for a given pixel ranges from minus one (-1) to plus one
(+1); however, no green leaf gives a value close to zero. A zero means no
vegetation and close to +1 (0.6 to 0.8) indicate the highest possible density
of green leaves and negative values mainly represent water and other
non-vegetated surface.
Bare soil index
The bare soil areas, fallow lands, vegetation with marked background
response are enhanced using this index. Similar to the concept of AVI, the bare
soil index (BI) is a normalized index of the difference and the sums of two
separating the vegetation with different background viz. completely bare,
sparse canopy and dense canopy, etc. This index helps us to get a clear idea of
vegetation from the surroundings. The range of BSI varies between 0 to 200. The
reflectance spectra of the soil mainly depend on soil moisture and
hydroxylions.IR region have low reflectance due to absorption by soil moisture
(soil water).
BI= (Band 5 + Band 3) – (Band 4 + Band 1) / (Band 5 + Band 3) + (Band 4
+ Band 1)
BI= (Band 6 + Band 4) – (Band 5 + Band 2) / (Band 6 + Band 4) + (Band 5
+ Band 2)
Canopy shadow index
The crown arrangement in the forest stand leads to shadow pattern
affecting the spectral responses. In the study area, matured even aged stands
have high canopy shadow index (SI) compared to the young forest stands. The
latter forest stands show flat and low spectral axis in comparison to that of
the open area. The canopy shadow index is more for the year 2007 as compared to
the year 2017. This index works out with a shadow pattern affecting the
spectral response with the crown arrangement in any forest.
SI=√ (256-B2) (256-B3)
SI= ((65536 –B2)*(65536−B3)*(65536−B4)) 1/3
The range of BSI varies between 0 and 100. The even aged stands have
high canopy shadow index (SI) compared to the young forest stands.
Thermal index
Two factors account for the relatively cool temperature inside a
forest. One is the shielding effect of the forest canopy, which blocks and
absorbs energy from the sun. The other is evaporation from the leaf surface,
which mitigates warming. These two factors were found in both forest regions of
Noamundi and Jagarnathpur. Formulation of the thermal index is based on this
phenomenon. The source of thermal information is the infrared band of TM data.
L=Lmin + ((Lmax-Lmin)/255)*Q
T=K2/(ln (K1/L+1))
Where,
L: Value of radiance in thermal infrared
T: Ground temperature (k)
Q: Digital record
K1, K2: Calibration coefficients
K1=666.09 watts / (meter squared * ster* μm)
K2=1282.71 Kelvin
Lmin= 0.1238 watts / (meter squared * ster* μm)
Lmax= 1.500 watts / (meter squared * ster* μm)
The range of TI varies between 0 to 100. It is used to separate soil
and non-tree shadow.
Forest canopy density model
VCD = VD (SSI + 1) 1/2 – 1
Where,
VD=Advanced Vegetation Index and Bare Soil Index
SSI=Canopy Shadow Index and Thermal Index
RESULTS AND DISCUSSION
By analyzing the above map we observe that the maximum dense forest is
given by the FCD model, whereas other two techniques which were applied on
Aster DEM, i.e., Aspect Map And Elevation Map give approximate extend of forest
by interpretation, which is not as good as FCD model. The Landsat Imagery of
2007 and 2017 has been represented by Band combination of bands (True Colour
Composite). These images were subjected to the PCA1 scaled and scaled SSI,
following which Forest Canopy Density Maps were generated, where the overall
accuracy for year 2007 was 84.25% and the overall accuracy for year 2017 was
80.06%, tested using the Kappa statistical methods generated from Erdas Imagine
10.3 tools (Figures 5 and 6).
Chlorophyll estimation is the method of estimating or measuring the
chlorophyll content of leaves of a tree or plant. In this study the chlorophyll
estimation was done by Off-field Chlorophyll Estimation.
Off-field chlorophyll
estimation
The off-field chlorophyll estimation was performed on the satellite
imagery by using empirical equation produced by Carmona. The empirical equation
also involved the calculation of NAVI. The result assessed from that equation
is shown on Figure 7.
Field observation and
validations
The field observation was done by measuring the chlorophyll content on the field by the help of DUOLEX SCIENTIF+ a proximal sensor for the measuring chlorophyll content from the tress of Shorea robusta. The table shown below displays species with its chlorophyll content first measured from the chlorophyll meter and next estimated from the satellite imagery (Figure 8). A significant deviation can easily be observed from the above table. Some deviation are like chlorophyll content of Sample 15 of Jagarnathpur forest region was found to be 20.569 μg/cm2 when measured from chlorophyll meter and was 16.326 μg/cm2 when estimated from satellite image. In the case of Noamundi when measured by chlorophyll meter it was 8.968 μg/cm2 and it was 6.763 μg/cm2 when estimated from the imagery. Similar trends are observed in all species. The trend of deviation which was being observed because the chlorophyll meter provided precise measurement for the chlorophyll species. On the hand the chlorophyll estimation from the satellite image gave result of 30 m * 30 m dimension of real ground plot, that plot contained several trees (Figure 9).
In the present study the graph has been plotted using regression
analysis techniques between satellite image and ground chlorophyll data in
MS-Excel. Regression analysis has a great technique for estimating the
satellite image and ground chlorophyll data The R² between the Satellite image
and ground chlorophyll data is 0.755 which is good (Figure 10). Anthocyanins belong to a parent class of molecules called flavonoids
synthesized via the phenyl propanoid pathway. They occur in all tissues of
higher plants, including leaves, stems, roots, flowers, and fruits.
Anthocyanins may have a protective role in plants against extreme temperatures.
Anthocyanins were observed in almost all combinations of every leaf tissue, but
were most commonly located in the vacuoles of photosynthetic cells. As during
the field based observation anthocyanins values was varying from one sample
plot to another sample plot. By doing interpolation in my study area the value
ranges from 0.05 to 0.99. These data indicate that anthocyanins are associated
with photosynthesis, but do not serve an auxiliary phytoprotective role. They
may serve to protect shade‐adapted chloroplasts from brief exposure to high
intensity sun flecks (Figure 11).
Flavonoids are a large group of polyphenolic compounds ubiquitous in
fruits, vegetables and herbs; they have attracted much attention due to their
potential antioxidant properties and probable role in the prevention of
oxidative stress-associated diseases including atherosclerosis. Flavonoid is
widely distributed in plants, fulfilling many functions. Flavonoid is the most
important plant pigments for flower coloration, producing yellow or red or blue
pigmentation in petals designed to attract pollinator animals. In higher
plants, flavonoids are involved in UV filtration, symbiotic nitrogen fixation
and floral pigmentation. As during the field based observation flavonol values
was varying from one sample plot to another sample plot .By doing interpolation
in my study area the value ranges from 0.07 to 1.67.
CONCLUSION
Conventional RS methodology, as generally applied in forestry is based
on qualitative analysis of information derived from “training areas” (i.e.,
ground-truth). This has certain disadvantages in terms of the time and cost
required for training area establishment, as well as to ensure a high accuracy.
Unlike the conventional qualitative method, the FCD model indicates the growth
phenomena of forests by means of qualitative analysis. The accuracy of
methodology is checked in field test. FCD model is very useful for monitoring
and management with less ground truth survey. The management and protection of
forest resources play an extremely important role for the study area. Using GIS
and remote sensing data, the achieved results have high reliability. Based on
the physical properties and spectral reflection of the objects on the surface,
the remote sensing images can provide much useful information in research.
Geographic problems often require the analysis of many different factors. For
instances, delineating the canopy density map, it needs different factors like
Advanced vegetation index, Bare soil index, Canopy Shadow Index, and Thermal
Index etc. It is possible to measure canopy density more accurately but it
needs more parameters like DEM, Slope, Soil type and others values depending
upon the environment of the study area. To fulfil the needs of these parameters
Advance Indexes has been taken into consideration. The more parameters used the
result will be more accurate. So it can be concluded that canopy density is the
integrated result of the various parameters and thus can be found more accurate
than normal classification scheme. Although higher Dense Forest regions can be
located easily from FCD Model using satellite data further analysis could be
carried out only when these data merged into Land use/land cover data and also
more detailed analysis could be achieved. Chlorophyll estimation was conducted
using on field chlorophyll estimation and off field chlorophyll estimation .On
field chlorophyll estimation was performed by using Chlorophyll meter, which
has inbuilt GPS present in it and provides Nitrogen status, Chlorophyll
content, Flavonols and Anthocyanin. Off field chlorophyll estimation was
conducted using the satellite imagery. From the imagery NAVI, i.e., Normalized
Area Vegetation Index was calculated and thereafter by the NAVI value,
Chlorophyll content is estimated. Hence from the on field and off field
calculation, comparison study is performed. From the comparison study a
significant deviation between the chlorophyll meters provided precise measurement
for the chlorophyll species wise. On the other hand the chlorophyll estimation
from the imagery gave result in a pixel of 30 m * 30 m dimensional of real
ground plot, the plot contained same species and the result is generated by
integrating the chlorophyll 56 content of the species. The variation is
represented by plotting a line chart with both the values, chlorophyll content
estimated by the satellite imagery and measured by the chlorophyll meter.
LIMITATIONS
In spite of using high resolution imagery, the chlorophyll was not
satisfying as it provided result in plot of 30 m * 30 m which was proved to be
coarser for this purpose. Performing field observation is really a time, labor
and money consuming thing. As the study was performed in buffer region of the
forest reaching and collecting observation from the core region could be more
hazardous due to the dense vegetation of Sal trees.
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