Established remote sensing systems provide opportunities to develop and apply new measurements of ecosystem function across landscapes, regions and continents. New efforts to predict the consequences of ecosystem function change, both natural and human- induced, on the regional and global distributions and abundances of species should be a high research priority

1. Seminar Topic ; Applications of Remote Sensing in Forest Ecology
Seminar Incharge ; Dr.H.P.Sankhyan
Student Name ; Bibi Nagaar
Adm.No. ; F-15-23-M
Course No ; FGR-591

2. Introduction to Remote sensing
Introduction to Forest ecology
Relationship between Remote Sensing and Forest Ecology
Applications of Remote Sensing in Forest Ecology.
Case Studies
Conclusions

3. REMOTE SENSING. The science of acquiring
information about an object, without entering in contact
with it, by sensing and recording reflected or emitted
energy and processing, analyzing, and applying that
information.
The processes of collecting information
about Earth surfaces and phenomena
using sensors not in physical contact with
the surfaces and phenomena of interest.
There is a medium of
transmission involved i.e.
Earth’s Atmosphere.

4. PRINCIPLES OF REMOTE SENSING
Detection and discrimination of objects or surface features means
detecting and recording of radiant energy reflected or emitted by objects
or surface material. Different objects return different amount of energy
in different bands of the electromagnetic spectrum, incident upon it. This
depends on the
Property of material (structural, chemical, and physical)
Surface roughness
Angle of incidence
Intensity
Wavelength of radiant energy.

5. Remote sensing
Aero-plane
Satellite

6. Ground-based Airplane-based Satellite-based

7. Active Remote Sensors
Laser altimeter
Lidar
Radar
Passive Remote Sensors
Accelometer
Radiometer
Spectrometer

8. Three main types of sensors used
Optical (Visible/IR)
Radar (Microwave)
LiDAR (Mostly NIR)

9. Remote sensing involves the use of aircraft or satellites to
collect photographs or scanned images of the Earth’s surface.
The origins of remote sensing date back to a photograph
taken from a balloon in 1858. By World War I, the aeroplane
had become the main platform from which aerial
photography was collected.

10. Balloon photography (1858)
Pigeon cameras (1903)
Kite photography (1890)
Aircraft (WWI and WWII)Space
(1947)
Remote Sensing & GIS Applications Directorate

11. Remote Sensing & GIS Applications Directorate

12. Energy Source or Illumination (A)
Radiation and the Atmosphere (B)
Interaction with the Target (C)
Recording of Enery by the Sensor (D) g
Transmission, Reception, and Processing (E)
Interpretation ad Annalysis (F)
Application (G)
Source: Canadian Centre for Remote Sensing
Remote Sensing Process Components

13. Data acquisition
Processing
Analysis
Accuracy assessment
Information distribution to users

14. NOAA-AVHRR
GOES
MODIS
Landsat TM and ETM
SPOT
IKONOS
Quick bird
Remote Sensing & GIS Applications Directorate

15. The study of living organisms in the
natural environment
How they interact with one another
How they interact with their
environment
© 2008 Paul Billiet ODWS

17. Physical hierarchy
Biological
hierarchy
Ecological
hierarchy

18. Cellular level
Tissue level
Organ level
Organ system level
Individual level

19. Population level
Community level
Ecosystem level
Biosphere level

25. A variety of ecological applications require data from broad spatial extents that cannot be collected
using field based methods.
Remote sensing data and techniques address these needs, which include identifying and detailing the
characteristics of species habitats
Predicting the distribution of species
Spatial variability in species richness
Detecting natural and human-caused change at scales ranging from individual landscapes to the
entire world.
Ecologists and conservation biologists are finding new ways to approach their research with the
powerful tools and data from remote sensing.

28. 1.Land cover classification .Satellite remote sensing is
used to estimate the
Variety
Type
Extent of land cover throughout a study region, meeting a fundamental
need that is common to many ecological applications.
Land cover data describe the physiographical characteristics of the
surface environ- ment, which can range from bare rock to tropical forest
and that are usually derived by applying statistical clustering methods to
multispectral remote sensing data .
Remote sensing also assist in the development of land use data that
reflect human interactions with the physical environment.

35. Integrated Ecosystem Measurements
Unlike field-based measurements of ecosystem
function, which cannot easily be converted to
estimates of function across entire ecosystems,
remote sensing can provide simultaneous
estimates of ecosystem functions
Net primary productivity (NPP) represents one aspect
of inte grated ecosystem function.

36. Recently,the Moderate Resolution Imaging Spectro Radiometer
(MODIS) NPP, based on a micrometeorological approach was developed
by Rahman et al. (2004) to provide a consistent, continuous estimate of
photosynthetic production (Heinsh et al., 2006) hereinafter referred to as
the MODNPP model.
MODNPP = 0.5139(MODPRI × APAR) −
1.9818
where, MOD PRI is MODIS-derived
photochemical reflectance index, APAR refers
to absorbed photosynthetically active
radiation by vegetation and 0.5139 and 1.9818
are constants

38. The landscape parameters-patch shape, patch size, number of
patches, porosity, fragmentation and juxtaposition, and have been
analyzed to delineate the disturbance regimes.
Roy et al. (2005) using vegetation type map of Andaman & Nicobar
islands studied the patch characteristics in terms of patch size,
number, shape, porosity and land cover diversity (IIRS, 2003).
Prasad et al. (2009) analyzed the levels of forest fragmentation and
its effect on species diversity in north Andaman forest using satellite
data and GIS-based fragmentation model.
Analysis of patch characteristics and species distribution showed
high species richness in less fragmented evergreen forests.

39. Assessment of Forest Productivity
Satellite sensors accurately detect forest productivity, they provide cost and
effort advantages over traditional field survey methods.
Productivity estimates based on satellite data have been produced with some success for
agronomic ecosystems (Olang 1983)
Wetlands (Butera et al. 1984; Hardisky et al. 1984), and Shrublands (Strong et al.
1985).

40. In a study, predictive models of wood mean annual
increment of volume in three regions of the United States
(southern Illinois, eastern Ten- nessee, and northeast New
York) were developed using CIS, TM data, and digital bio
geographical data on forest productivity and soils, slope,
solar radiation, and/or vegetation type (Cook et al. 1987;
Cook et al. 1989).
In general, forest produc- tivity was more accurately
predicted with a combi- nation of TM and biogeographical
variables than with either data type alone.

42. Vegetation type maps in conjunction with phytosociology,
topography and soil are used in identifying areas of high economic
value (Reddy et al., 2008).
The direct identification of economically important of species
depends on the extent of distribution and resolution of satellite data.
Vegetation formations like pines, sal, teak, which grow gregariously
under unique climatic and geological formations are easily
identified using remote sensing and the quantitative estimates of
timber and biomass are obtained using optimized ground
inventories.

44. Monitoring - Changing Biodiversity
Levin (1999) emphasized the problem of biodiversity prediction and
mentioned the need to understand processes that add to or remove the species
from an ecosystem by changing the surrounding environment of the
ecosystem under study.
Predictions of biodiversity change is quite challenging because of
large uncertainties associated with the complex dynamics of
ecological systems.
By using remotely sensed data to describe the changes brought
about in vegetated areas of Vindhyan hills over a period of 10
years as a result of fragmentation and its impact on biodiversity
have been successfully described (Jha et al. 2005).
A high degree of influence of land-use history, fire regimes and other
disturbances impacts the vegetation and the biogeochemical characteristics of
currently existing ecosystems (Compton and Boone, 2000; Goodale and Aber,
2001)

45. Assessment of Forest Damage
The assessment of forest damage is an important use of remote sensing
data.
Many of the changes in tree or foliage morphology resulting from stress
can be detected with remote sensors (Jackson 1986).
Furthermore, the spectral signature of stressed trees indicate not only
the degree of stress but also the type of stress. For example, TMS
imagery of damaged red spruce (Picea rubens) stands in Vermont shows
a large reduction in the shortwave-infrared reflectance (Rock et al.
1986).
Field verification of the image revealed that the foliage of the
highly damaged spruce stands was drier and less dense than
that of undamaged stands (Rock et al. 1986; Vogelmann and
Rock 1986).

48. Deforestation in the Amazon basin of Brazil has been quantified by using
AVHRR band 3 thermal data which, unlike the visible bands, can penetrate the
ubiquitous cloudcover of the region .
Estimates of deforestation were obtained by using band 3 to
detect both the fires associated with lines of active deforestation
and the devegetated areas, which are warmer. The studies of
Rondonia, Brazil, indicate that the deforested area increased
from 4200 km’ in 1978 to 10,000 km’ in 1982 to 27,000 km’ in
1985 to over 35,000 km’ in 1987 (Malingreau and Tucker 1987;
Malingreau and Tucker 1988).

49. Habitat loss
Satellite measurements of broad-scale trends in vege- tation provide direct
estimates of habitat loss, increasing the power of applied ecological studies to
detect changes in species distributions or extinction rates.
Defore- station in humid tropical forests, which house many terrestrial
biodiversity hotspots, is a globally leading cause of species loss.
It has proven very difficult to estimate accurately the extent of humid
tropical deforestation because of poor monitoring infrastructure in many
countries and inconsistencies among existing monitoring regimes.

50. Satellite data from the 1990s, based on AVHRR and SPOT4/Vegetation
and supplemented by high-resolution Landsat and SPOT4/HRVIR (high
resol- ution visible and infrared) data, have been integrated to generate
the best estimates of rates of deforestation among remaining humid
tropical forests .

51. Deforestation ‘hotspots’ could also be detected. Fire,
another leading source of change, especially extensive in
areas that have previously been damaged by
deforestation.
A combination of AVHRR, Landsat TM (Thematic
Mapper) and radar data were used to detect the impact
of deforestation on the burn like- lihood of forests in East
Kalimantan, Indonesia .

52. Satellite remote sensing data has been extensively used to
map forests of tropics whereas up to date data about spatial
distribution are absent or lacking. In India, the initial
attempt at national level has been on 1: 250,000 scale using
visual interpretation of false color images.
National Remote Sensing Agency for the first time studied 1: 1
million images for the periods 1972-75 and 1980-82 and forests
were classified into three categories .

64. Study on mapping and sustainable forest management in Rewari district
was carried out to map the forest cover areas, crown density analysis of
reserved forests and potential afforestation sites.
IRS data was used and visual image analysis techniques were employed.
The study area covers 1559 sq. km and consists of tropical thorn forest
with some tropical dry deciduous species.

67. Important tree species recommended are
Prosopis
cineraria,
Acacia
arabica
Acacia
tortilis
Zizyphus
numulleria,
Lasirus
sindicus
Cencherus
ciliaris

68. S. No. Category Area (ha) % to total Forest cover
1 Closed Forest 424 14.21
2 Block Plantation 3163 85.79
Total 3587 100.00

69. S. No. Category Area (ha) % to total Potential land
1 Degraded block plantation 1421 10.87
2 Scrub land 4993 38.20
3 Degraded pasture/ grazing land 3595 27.50
4. Sand 1691 12.93
5. Rocky land 1369 10.47
Total 13069 100.00

73. Remote sensing is indispensable for ecological and con-
servation biological applications and will play an increas-
ingly important role in the future.
For many purposes, it provides the only means of measuring
the characteristics of habitats across broad areas and
detecting environ- mental changes that occur as a result of
human or natural processes.
These data areincreasingly easy to find and use. Although
field and remote sensing data are often collected at divergent
spatial scales, ecologists have begun to recog- nize both the
potential and the pitfalls of satellite information.

74. Established remote sensing systems provide opportunities to
develop and apply new measurements of ecosystem function
across landscapes, regions and continents.
New efforts to predict the consequences of ecosystem
function change, both natural and human- induced, on the
regional and global distributions and abundances of species
should be a high research priority.
The full range of remote sensing techniques for identifying
land covers, measuring the biophysical properties of
ecosystems and detecting environmental change will need to
be integrated with existing and new ecological data to meet
this ambitious challenge.

75. THANK YOU FOR YOUR
KIND ATTENTION