presentation remote sensing
TRANSCRIPT
Landcover Mapping using Landsat Image
Agus ArisG051110011
INFORMATION TECHNOLOGY FOR NATURAL RESOURCE MANAGEMENT
SEAMEO – BIOTROPBOGOR AGRICULTURAL UNIVERSITY
2011
Introduction Remote sensing is the science and art of obtaining information about an
object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation
Remote sensing is one method of observation or measurement of the spatial elements of the earth's surface. method is very effective and efficient has many variants in providing spatial data records, then to improve quality and enhance sensors recording data related to the production end of the spatial data (hardcopy), carried out the processes of digital image processing. In this connection we also use the device ErDas or Er Mapper
Erdas is one of the benefits for image processing in the process of classifying the coastal area or an area of land cover
Objective
Make a land cover using Software Remote sensing
Measure land cover area in Central Java
Data Requirement
Sensor Name Spatial Resolusi
(m)
SpectralResolusiNumber of Band & wavelength (µm)
Radiometrik Resolution (bit)
Temporal Resolution
(day)
launch time
LANDSAT 7 ETMMultispectral-Reflective-ThermalPan
30 m90 m15 m
Band 1-5&7 : 0.4 – 2.35Band 6 : 10.4– 12.5Band 8 : 0.52 – 0.90
888
16 days 1998
Image Processing Composit Georeference ( Geometric
Correction ) Masking Ground check using Googel Earth Make AOI (Area Of Interest) Classification Supervised
Composit
Before After
Composit is how do we combine all the bands
Georeference
Before After
Georeference is how to make same the coordinate of the image with the actual conditions on the ground
MaskingMasking is a step of being done to eliminate some areas that are not needed
Ground Check
Criteria of visual interpretation
Color Tone Size Pattern Texture Shape Shadow Association
AOI Porpose AOI using to make the area
for classification
Accuracy assesment
2000 Exelent1900 - < 2000 Good1800 - < 1900 Fair1600 - < 1800 Poor< 1600 Inseparable
overall accuracy 88.23895457
kappa accuracy 81.12461401
Evaluate Contingency Evaluate Separability
Classification SupervisedA method used to group classes defined by the user by using the Training Area
Area
Area = Number of Pixel / Resolusi