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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE 
DETECTION USING SATELLITE IMAGERY 
A. K. Sah* *, B. P. Sah*, K. Honji?, N. Kubo*, S. Senthil? 
*PASCO Corporation, 1-1-2 Higashiyama, Meguro-ku, Tokyo 153-0043, Japan - (ahwaas9539, bhpa s5512, kiojin6937, 
noabmu3604) (a)pasco.co.jp, selvaraj.senthilz gmail.com 
Commission VII, WG VII/5 
KEY WORDS: Satellite, Forestry, Land Cover, Classification, Change Detection, Semi-automation 
ABSTRACT: 
Multi-platform/sensor and multi-temporal satellite data facilitates analysis of successive change/monitoring over the longer period 
and there by forest biomass helping REDD mechanism. The historical archive satellite imagery, specifically Landsat, can play an 
important role for historical trend analysis of forest cover change at national level. Whereas the fresh high resolution satellite, such 
as ALOS, imagery can be used for detailed analysis of present forest cover status. ALOS satellite imagery is most suitable as it 
offers data with optical (AVNIR-2) as well as SAR (PALSAR) sensors. AVNIR-2 providing data in multispectral modes play due 
role in extracting forest information. 
In this study, a semi-automated approach has been devised for cloud/shadow and haze removal and land cover change detection. 
Cloud/shadow pixels are replaced by free pixels of same image with the help of PALSAR image. The tracking of pixel based land 
cover change for the 1995-2009 period in combination of Landsat and latest ALOS data from its AVNIR-2 for the tropical rain 
forest area has been carried out using Decision Tree Classifiers followed by un-supervised classification. As threshold for tree 
classifier, criteria of NDVI refined by reflectance value has been employed. The result shows all pixels have been successfully 
registered to the pre-defined 6 categories; in accordance with IPCC definition; of land cover types with an overall accuracy 80 
percent. 
1. INTRODUCTION 
Historical archive satellite imagery together with fresh one 
gives potentiality classifying time series Land cover (LC) of an 
area. LC is being used for national land planning since long and 
time series LC opens new applications, such as Reducing 
Emissions form Deforestation and forest Degradation (REDD 
or REDD+). Satellite Remote Sensing is a primary information 
source for LC and forest assessment as it provides images of 
wider areas relatively in a faster and cost-efficient manner. 
After the launch of Landsat 1 Satellite in 1972, several satellites 
(with both optical and Synthetic Aperture Radar (SAR) sensors) 
have been launched and trend is continuing at present as well as 
several planned to be launched in future and in due course 
spatial resolution has improved to a large extent. High 
resolution satellite, such as Advanced Land Observing Satellite 
(ALOS) provides Advanced Visible and Near Infrared 
Radiometer Type 2 (AVNIR-2) imagery (10m resolution) can 
be used for analysis of present forest cover status (Nonomura et 
al., 2010). 
However, presence of cloud, shadow, and haze in satellite 
imagery hamper LC classification and need to be treated. 
Treatment of all of them (cloud, shadow, and haze) together in 
different landscape including forest land is the big issue. 
There are some programs currently available for cloud, shadow, 
and haze but these are fragmented and lack of holistic approach 
that can treat all of them. Experimenting on simulated ALOS 
data, Hoan and Tateishi, 2008 have used Total Reflectance 
Radiance Index (TRRI) to separate cloud area. Further for 
  
* Corresponding author. 
separating thin cloud ‘Cloud Soil Index (CSI)' criterion has 
been mentioned as second step. 
Also, satellite based imageries from various platforms and 
optical sensors have been providing LC information since long, 
but extracting them correctly is challenging tasks. Employing 
decision tree classification scheme, Hansen et al, 2000 
produced global land cover for which multi-temporal Advanced 
Very High Resolution Radiometer (AVHRR) metrics were used. 
Use of multi-temporal satellite data to produce single time land 
cover becomes complicated for REDD (or REDD+) which 
needs LC classifications from past, present, and future satellite 
imagery with minimum human intervention so that it can fulfil 
the required MRV (Measuring, Reporting and Verification) 
transparency. 
Considering the above issues, in this study we present a semi- 
automated approach for cloud/shadow and haze removal and 
LC change detection as a whole. The concept of LC used in this 
study is equivalent to Land Use (LU) mentioned in IPCC. Most 
of the image processing steps have been carried out using 
PASCO Tool™. This has been developed using Erdas Macro 
Language (EML) and thus works with Erdas ImagineO 1991- 
2009 ERDAS, Inc., software environment. The processing steps 
comparatively require less operator inputs and can classify large 
number of satellite imageries with desired accuracy for pixel 
based change detection including defined number of LC classes 
as per IPCC (Bickel et al., 2006).