Monitoring Built-up areas using DMSP-OLS nighttime lights data: A - PowerPoint PPT Presentation

Monitoring Built-up areas using DMSP-OLS nighttime lights data: A study from Indo Gangetic Plain Region lights data: A study from Indo-Gangetic Plain Region, India Pranab Kanti Roy Chowdhury S i Scientist ti t Government of India DMSP Nighttime Lights of India Workshop APAN 32 nd New Delhi Meeting 26 th August, 2011, New Delhi g , ,

Need of monitoring built-up areas In developing countries like India, the urban built-up areas are expanding in an unplanned manner on their peripheries leading to an irreversible transformation of contiguous agricultural and forest lands into built-up areas. g p This affects hydrological and ecological cycles at regional and global scale and has become a matter of concern for climate change, natural resource utilization, biological and become a matter of concern for climate change, natural resource utilization, biological and ecological sustainability etc. Thus monitoring the growth of built up areas at regional and global scale has become an Thus, monitoring the growth of built-up areas at regional and global scale has become an urgent task for taking preventive measures in order to reduce the negative effects associated with these rapid expansion.

Monitoring urban areas at regional scale using Remote Sensing data Remote Sensing techniques provide an ideal means of monitoring built-up areas. But high and medium resolution datasets are often less popular for regional level studies and medium resolution datasets are often less popular for regional level studies. Firstly, using high and medium resolution satellite datasets for regional level studies involves high cost for acquiring the datasets, time and labour required for processing and i l hi h t f i i th d t t ti d l b i d f i d interpreting images, huge database size and these act as prohibitive factors. Secondly, as the swath of the high and medium resolution images are not wide, frequent cloud conditions make it difficult to collect a large number of good quality datasets within a specific time frame for the entire study area. p y These problems may be overcome using coarse resolution datasets, having wide swath, less data volume and no or very less cost. less data volume and no or very less cost.

Monitoring urban areas at regional scale using Remote Sensing data However, the main problem in monitoring built-up areas using coarse resolution datasets is that of mixed pixels which makes the estimation of built up areas less accurate that of mixed pixels which makes the estimation of built up areas less accurate. Techniques such as spectral mixture analysis, artificial neural network, support vector machine etc may be used for this purpose but they are quite complex and have their own hi t b d f thi b t th it l d h th i constraints. Thus, there is a need to develop new methods which will help in monitoring the growth of built-up areas within limited time along with minimal labour and cost. In this study an attempt has been made to implement a new method for monitoring built-up areas using DMSP-OLS datasets. Indo-Gangetic Plain region of India has been chosen as the study area for this purpose. the study area for this purpose.

Defense Meteorological Satellite Program Defense Meteorological Satellite Program run by the United States Air Force operates Defense Meteorological Satellite Program, run by the United States Air Force, operates mainly for meteorological, oceanographic and solar-physics environments monitoring since 1970s. Operational Linescan System OLS, onboard the DMSP satellites is an oscillating scan radiometer capable of detecting even very faint VNIR emission using a Photo Multiplier Tube (PMT) in two resolutions of 0 55 km and 2 7 km 0.55 km and 2.7 km. This unique capability results in detection of lights from human settlements and ephemeral events on cloud less nights under low lunar illuminance, which has been used in urban g areas and population related studies by researchers globally.

DMSP-OLS nighttime lights data • NOAA NGDC has developed yearly composites of DMSP OLS datasets captured in • NOAA-NGDC has developed yearly composites of DMSP-OLS datasets captured in cloudless nights under very low or no lunar illuminance. • Ephemeral events have been removed from datasets using local threshold levels leaving Ephemeral events have been removed from datasets using local threshold levels, leaving lights from only from urban areas, which may be used as a surrogate of urban areas. • The digital datasets, having 6 bit radiometric resolution (1992 onwards), are available at g g ( ) 1km spatial resolution and may freely be downloaded from the NOAA-NGDC website http://www.ngdc.noaa.gov/dmsp/global_composites_v4.html In this study the DMSP-OLS nighttime stable lights datasets have been used in monitoring y g g g the built-up areas and their growth.

Indo-Gangetic Plain, India • The study area comprises of 9 Indian states and Union Territories and accounts for about • The study area comprises of 9 Indian states and Union Territories and accounts for about 19.87% (653,211 sq. km.) of the total land area of India (Census of India, 2001) • The area is bestowed with extensive expanse of uninterrupted alluvium, rich soil, Th i b t d ith t i f i t t d ll i i h il sufficient ground water sources and mild climate, resulting in it being one of the world's most intensely cultivated and populated regions. • The area is inhabited by about 41.32% of total Indian population (Census of India, 2001). • In order to accommodate this huge and ever increasing population, the urban areas are undergoing a rapid expansion and as a consequence most of the rich agricultural land is being converted into built-up areas. being converted into built up areas. • This not only affects the agricultural productivity but also affects the ecological and hydrological cycles and sustainability hydrological cycles and sustainability.

Map not to scale

DMSP/OLS nighttime lights data in built-up area monitoring DMSP OLS nighttime lights datasets have been extensively used for urban areas and DMSP-OLS nighttime lights datasets have been extensively used for urban areas and population related studies, however direct identification of urban areas made by OLS is not free from errors • Low radiometric resolution of 6 bits results in data saturation over brightly lit built-up areas. Due to this, the pixels having significantly different fraction of built-up areas may have similar DN values in DMSP-OLS datasets. similar DN values in DMSP OLS datasets. • Data saturation also results in over-estimation of urban areas. To address this many researchers have used value thresholding in such datasets. But using a single threshold in a large region may result in smaller urban areas being lost in the process. • Factors along with the impacts of backgrounds such as sporadic ephemeral lights, glint of light into adjacent water bodies etc lead to uncertainty in motoring built-up areas light into adjacent water bodies etc. lead to uncertainty in motoring built-up areas. In this study, an effort has been made to integrate data from multiple sensors for accurately mapping the growth of urban areas (in terms of built-up area) on a per pixel basis. pp g g ( p ) p p

DMSP-OLS and NDVI datasets Vegetation indices like NDVI are negatively correlated with impervious surfaces and may Vegetation indices like NDVI are negatively correlated with impervious surfaces and may be used for estimation of built-up areas as demonstrated by past researchers. OLS and NDVI datasets are complementary in the sense that higher fraction of built-up OLS d NDVI d t t l t i th th t hi h f ti f b ilt areas in a pixel will be associated with less vegetation cover, resulting in higher DN values in OLS data and lower DN value in corresponding NDVI data and the vice versa. Combined use of both datasets may provide new insights in estimating the fraction of built- up areas on a per pixel basis through bringing out more information in the resultant p p p g g g datasets than contained by the constituent datasets individually. This approach also helps in minimizing the errors resulting from external sources in DMSP- This approach also helps in minimizing the errors resulting from external sources in DMSP OLS data.

Human Settlement Index The strong negative relationship between the NDVI and the built-up surface has been Th t ti l ti hi b t th NDVI d th b ilt f h b utilized by Lu et al , 2008 for developing Human Settlement Index (HSI) that represents fraction of built-up area of a per pixel basis. A higher value in the HSI is related to higher proportion of built-up area in a pixel. (1-NDVI max ) + OLS nor ( max ) nor Human Settlement Index = (1-OLS nor ) + NDVI max + (OLS nor * NDVI max ) (After Lu et al., 2008) Where, OLS nor = (OLS - OLS min ) / (OLS max - OLS min ) OLS = (OLS - OLS ) / (OLS - OLS ) and NDVI NDVI max = MAX (NDVI 1 , NDVI 2 , NDVI 3 ,..., NDVI 12 ) = MAX (NDVI NDVI NDVI NDVI )