Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Pervez, Wasim; Uddin, Vali; Khan, Shoab A.; Khan, Junaid Aziz

doi:10.1117/1.jrs.10.026004

Cited by 32 publications

(19 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RBF kernel is frequently used for its advantage in the classification of remotely sensed images to achieve better results than other kernels (polynomial, linear, and sigmoid) [68,80]. The penalty parameter maximum value 100 was assigned and the land-cover classification scheme was adopted as recommended by Anderson et al [81] and Thapa and Murayama [25].…”

Section: Pre-processing Of Images and Design Of Image Classificationmentioning

confidence: 99%

Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

Rimal

Zhang

Keshtkar

et al. 2018

IJGI

201

View full text Add to dashboard Cite

This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven time-periods (1988-1992, 1992-1996, 1996-2000, 2000-2004, 2004-2008, 2008-2013, and 2013-2016). The classification was conducted using the support vector machines (SVM) technique. A hybrid simulation model that combined the Markov-Chain and Cellular Automata (MC-CA) was used to predict the future urban sprawl existing by 2024 and 2032. Research analysis explored the significant expansion in urban cover which was manifested at the cost of cultivated land. The urban area totaled 40.53 km 2 in 1988, which increased to 144.35 km 2 in 2016 with an average annual growth rate of 9.15%, an overall increase of 346.85%. Cultivated land was the most affected land-use from this expansion. A total of 91% to 98% of the expanded urban area was sourced from cultivated land alone. Future urban sprawl is likely to continue, which will be outweighed by the loss of cultivated land as in the previous decades. The urban area will be expanded to 200 km 2 and 238 km 2 and cultivated land will decline to 587 km 2 and 555 km 2 by 2024 and 2032. Currently, urban expansion is occurring towards the west and south directions; however, future urban growth is expected to rise in the southern and eastern part of the study area, dismantling the equilibrium of environmental and anthropogenic avenues. Since the study area is a cultural landscape and UNESCO heritage site, balance must be found not only in developing a city, but also in preserving the natural environment and maintaining cultural artifacts.

show abstract

Section: Pre-processing Of Images and Design Of Image Classificationmentioning

confidence: 99%

Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

Rimal

Zhang

Keshtkar

et al. 2018

IJGI

201

View full text Add to dashboard Cite

show abstract

“…A., 2012;Markham, B. L. et al, 2010;Pehlevan, N., Schott, J. R., 2011;U.S Geological Survey, 2012). Landsat-8 OLI is appropriate for land use/cover mapping (Czapla-Myers, et al, 2015;Flood, N., 2014;Jiag, P., Li, Feng, Z., 2014;Knight, E., Kvaran, G., 2014;Ke, Y., et al, 2015;Pervez, W., 2016;Morfitt., R., 2015;Markham, B., 2014;Roy D., et al, 2014. This paper presents a change detection study of Landsat-8 Operational Land Imager (OLI) data of the study area for the four seasons and for six different cases.…”

Section: Introductionmentioning

confidence: 99%

Landsat-8 Operational Land Imager Change Detection Analysis

Pervez

Khan

Hussain

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission VI, WG VI/4KEY WORDS: Change Detection Analysis, Satellite Image Processing, Remote Sensing, Imaging Sciences, Operational Land Imager ABSTRACT:This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI) for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water). OLI support vector machine (SVM) classified data for the four seasons (i.e., spring, autumn, winter, and summer) was used to change detection results of six cases: (1) winter to spring which resulted reduction in dam water mapping and increases of bushes; (2) winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3) winter to autumn which resulted increase in dam water mapping; (4) spring to summer which resulted reduction of vegetation and shallow water; (5) spring to autumn which resulted decrease of vegetation; and (6) summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

show abstract

“…In OLI, the new Cirrus band detects thin clouds more accurately compared to previous satellite-based sensors (i.e., ALI and Landsat-7) [6]. Landsat 8 provides regional monitoring through remote sensing with substantial improvements in data quality due to advances in its noise reduction and spectral coverage designs [7,8]. Studying different satellite sensors and their parameters (i.e., scanning systems, sensor characteristics, data systems, resolution, and so on) is important.…”

mentioning

confidence: 99%

Satellite based seasonal land use classification and change detection analysis of landsat-8 operational land imager

et al. 2017

Self Cite

View full text Add to dashboard Cite

This study investigated the Landsat-8 Operational Land Imager (OLI) to determine its suitability for change detection analysis and mapping applications due to its enhanced signal-to-noise ratio (SNR), spectral band configuration, technical superiority, improved system design and high radiometric resolution. Earlier Landsat series had a lower SNR, comparatively smaller radiometric resolution and limitations in spectral band configurations. Pre-classification methods e.g., image differencing; image rationing, vegetation indices and principal components used for change detection analysis do not indicate type of the change due to its limitations. Therefore, better technique of post classification change detection was used on OLI data in the study area which provided information of the type of the change i.e., from-to change detection. This paper evaluated the OLI support vector machines (SVM) classification suitability using data covering four different seasons (i.e., spring, autumn, winter, and summer) after pre-processing and atmospheric correction. After classification, the major change detection results of OLI SVM-classified data for the four seasons were compared to change detection results of six cases: (1) winter to spring; (2) winter to summer; (3) winter to autumn; (4) spring to summer; (5) spring to autumn; and (6) summer to autumn. Seasonal change in the shoreline resulted with the corresponding change in categories. The OLI data classifications were made by applying SVM classifier and due to its improved features, OLI data is found suitable for seasonal land cover classification and post classification change detection analysis. , compared to ALI and Landsat 7 [3][4][5]. The width of OLI band 5 is refined to exclude atmospheric absorption features at 825 nanometers (nm) and its panchromatic bandwidth is reduced to improve detection of vegetation vs. non-vegetation compared with previous Landsat series. In OLI, the new Cirrus band detects thin clouds more accurately compared to previous satellite-based sensors (i.e., ALI and Landsat-7) [6]. Landsat 8 provides regional monitoring through remote sensing with substantial improvements in data quality due to advances in its noise reduction and spectral coverage designs [7,8]. Studying different satellite sensors and their parameters (i.e., scanning systems, sensor characteristics, data systems, resolution, and so on) is important. However, studies examining the characteristics of Landsat-8 and its pre-and post-flight calibrations are rare in the literature [9][10][11][12][13][14][15].Change detection remains a challenging problem and is used different for classification and change detection analysis [16][17][18]. Different satellite based change detection methods have different advantages and disadvantages [19,20]. In some change detection methods, no training data is required [21] e.g., in image differences [22] and clustering based methods [23] provide only change vs no change. If training data is available, then post classification change detection analysis ...

show abstract

Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery

Cited by 32 publications

References 52 publications

Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

Landsat-8 Operational Land Imager Change Detection Analysis

Satellite based seasonal land use classification and change detection analysis of landsat-8 operational land imager

Contact Info

Product

Resources

About