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Journal of Geographical Sciences    2015, Vol. 25 Issue (12) : 1479-1506     DOI: 10.1007/s11442-015-1247-y
Research Articles |
Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data
M USMAN1,R LIEDL1,M A SHAHID2,A ABBAS3,2
1. Institute for Groundwater Management, TU Dresden, 01069 Dresden, Germany
2. University of Agriculture, Faisalabad, Pakistan
3. Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany;
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Abstract  

Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three different techniques: error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coefficients (K). The average overall accuracies for rabi and kharif are 82.83% and 78.21%, respectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation between cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.
Keywords land use/land cover      remote sensing      normalized difference vegetation index      accuracy assessment      change detection      hydrological modeling     
Issue Date: 05 January 2016
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M USMAN
R LIEDL
M A SHAHID
A ABBAS
Cite this article:   
M USMAN,R LIEDL,M A SHAHID, et al. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data[J]. Journal of Geographical Sciences, 2015, 25(12): 1479-1506.
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http://www.geogsci.com/EN/10.1007/s11442-015-1247-y     OR     http://www.geogsci.com/EN/Y2015/V25/I12/1479
Sr. No Name of the dataset Data description Source Coverage/
Spatial scale
1 FAOSTAT Agricultural lands http://faostat3.fao.org/home/E Country level
2 FORIS Inland waters, forest and woodland http://www.fao.org/forestry/site/fra/en Country level
3 GLCC Built-up areas, water resources, barren or sparsely vegetated areas, grasslands, open shrub lands, forests http://edc2.usgs.gov/glcc/glcc.php 1 km × I km
4 GLCC-2000 Forests, cultivated and managed areas, bare areas, water bodies, urban and built-up areas http://forobs.jrc.ec.europa.eu/ 1 km × 1 km
5 MOD12Q1 Land Cover and Land Cover Dynamics products Land cover change vectors http://modis.gsfc.nasa.gov/about/ 500 m × 500 m
6 GISS Forests, cultivated land and grasslands http://data.giss.nasa.gov/landuse/ 1o (≈ 111 km)
7 GLCF Land tree cover, forest cover change, Geo cover http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp 500 m × 500 m
8 PELCOM Coniferous, deciduous and mixed forests, grassland,
rainfed and irrigated arable land, perennial crops, shrub, barren land, ice and snow cover, wetlands, inland waters, sea and urban area		 www.geo-informatie.nl/projects/pelcom/public/index.htm 1 km × 1 km
(Covers only European countries)
9 Global land cover map Cultivated areas, built-up lands, forests, barren lands, etc. Yu et al. (2013); Gong et al. (2013) 30 m × 30 m
Table 1  Summary of selected regional to country level Land Use/Land Cover datasets
Figure 1  Map of LCC (East), Rechna Doab, Punjab, Pakistan and ground truthing points
Figure 2  Crop calendar adopted in LCC
Figure 3  Flow diagram showing methodological and analytical steps
Figure 4  Highlights of the field visit in the study area
Figure 5  Mean NDVI temporal trends for major crops: rabi 2005-06 to rabi 2011-12
Year &
Accuracy		 Season & Class
Producer’s accuracy (%) User’s accuracy (%)
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 Avg. 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 Avg.
Rabi 1 75 83.3 77.8 71.4 81.8 75 75 77.0 64.3 62.5 77.8 62.5 81.8 66.7 75.0 70.1
2 81.3 86.4 85.8 90.5 90.4 91.3 89.5 87.9 77.4 82.1 83.7 83.5 91.2 84.1 88.0 84.3
3 70.8 71.1 81.3 82.5 71.7 76.0 86.2 77.1 75.6 76.2 77.6 81.3 73.3 80.9 82.4 78.2
4 71.4 70.8 73.1 72.0 78.2 69.6 72.3 72.5 76.4 78.0 80.3 84.4 75.4 80.0 78.3 79.0
Kharif 5 88.0 94.7 90.0 86.4 88.2 94.1 90.2 68.8 66.7 69.2 76.0 71.4 80.0 72.0
6 75.6 93.0 74.5 74.5 71.4 74.0 77.2 70.8 72.7 74.5 76.1 83.3 72.5 75.0
7 72.2 72.7 83.3 84.9 84.8 75.7 79.0 76.5 78.4 74.1 71.4 77.8 77.9 76.0
8 76.7 74.5 75.2 73.1 75.0 86.0 76.8 81.2 90.5 86.4 82.9 80.5 87.1 84.7
9 73.7 78.1 77.8 73.0 81.3 75.8 76.6 77.8 73.5 75.7 75.0 74.3 78.1 75.7
Table 2  Summary of producer’s and user’s accuracies for different classes of rabi and kharif seasons

Sr. No.
Year		 Rabi Season Kharif Season
Avg. Prod. Accur. Avg. User Accur. Overall accuracy K Avg. Prod. Accur. Avg. User Accur. Overall accuracy K
1 2005-06 74.7 73.4 87.4 0.77
2 2006-07 77.9 74.7 79.5 0.66 77.2 75.0 76.2 0.69
3 2007-08 80.6 80.4 83.6 0.74 82.6 76.4 79.3 0.73
4 2008-09 78.0 77.9 81.9 0.71 80.2 76.0 78.1 0.71
5 2009-10 79.5 79.9 81.2 0.71 78.4 76.3 77.0 0.70
6 2010-11 80.8 80.9 83.8 0.75 80.2 77.5 78.6 0.72
7 2011-12 79.0 77.9 82.5 0.74 81.1 79.1 80.1 0.74
Average 78.6 77.9 82.8 0.73 79.9 76.7 78.2 0.71
Table 3  Summary of seasonal accuracies and K (Kappa coefficient)
Sr. No. Classification
accuracy		 K Type of data Reference
1 91.5 0.89 RADARSAT Shao et al., 2001
2 77.2 0.736 MODIS Giri et al., 2005
3 84.4-87.1 82.3-83.6 Landsat MSS. ETM+ Reis, 2008
4 77 0.73 SPOT Cheema et al., 2010
5 94 0.93 SPOT Thi et al., 2012
6 78-99 - Landsat Ding et al., 2013
7 84-93 0.78-0.92 Landsat Lu et al., 2013
8 78.2-82.8 0.71-0.73 MODIS Present study
Table 4  Comparison of accuracy values and K from earlier studies with present study
Figure 6  Relationship between reported cropped-area fraction and remotely-sensed cropped-areas fraction for wheat, cotton, sugarcane and rice crops (1:1 Plot)
Figure 7  Comparison of crop area estimates with Cheema and Bastiannssen (2010)
Class Name Texture Class Texture Elevation (m) Slope (%)
Wheat Moderately fine/ Moderately-coarse Sandy-clay-loam, clay-loam, silty-clay-loam/sandy-loam,
fine sandy-loam		 187 1.98
Rice Moderately-fine Sandy-clay-loam, clay-loam, silty-clay-loam 192 2.40
Cotton Moderately-coarse Sandy-loam, fine sandy-loam 176 1.14
Sugarcane Moderately fine/Moderately-coarse Sandy-clay-loam, clay-loam, silty-clay-loam/sandy-loam,
fine sandy-loam		 169 2.00
Rabi fodder Moderately-coarse Sandy-loam, fine sandy-loam 175 1.41
Kharif fodder Moderately-coarse Sandy-loam, fine sandy-loam 179 1.91
Table 5  Summary of soil texture, elevation and slope for each LULC
Figure 8  Relationship of NDVI to temperature and precipitation for wheat, cotton, sugarcane and rice
Table 6  Areal distribution of LULC classes during rabi seasons in LCC
Table 7  Areal distribution of LULC classes during kharif seasons in LCC
Table 8  Pixel-by-pixel LULC change detection between maximum and minimum cropped areas for rabi seasons
Crop class Change matrix for area (ha) Spatial change detection
Rice 2008
2009 Fallow* Cotton S. cane K. fodder Rice Total
Fallow* 18388 0 0 16913 1118
Cotton 0 75533 8266 32982 184
S. cane 0 23966 61450 928 821
K. fodder 3937 17799 672 275465 25388
Rice 131 13726 35831 88378 224125 362218
Total 251654
Cotton 2007
2011 Fallow Cotton S. cane K. fodder Rice Total
Fallow 12566 0 0 4395 190
Cotton 12 8623 81034 143959 25881 259529
S. cane 0 23407 102895 7993 3485
K. fodder 8480 59 1701 154152 5156
Rice 1392 44662 12435 65239 218285
Total 76758
Kharif fodder 2011
2008 Fallow Cotton S. cane K. fodder Rice Total
Fallow 14088 0 0 7826 541
Cotton 0 83704 40660 1290 4775
S. cane 0 13006 81129 36 12049
K. fodder 2866 154378 10104 152249 95069 414699
Rice 196 8421 5888 7553 229578
Total 169562
Table 9  Pixel-by-pixel LULC change detection between maximum and minimum cropped areas for kharif seasons
LULC Sagar Chuharkana Paccadala Mohlan Buchiana Tandla Tarkhani Kanya Bhagat Sultanpur
Cotton Evapotranspiration 563.4 537.9 550.9 557.4 579.3 545.1 525.0 520.6 518.4 539.8
% area 1.1 2.9 5.8 11.8 15.4 21.9 13.7 14.1 9.6 3.6
Sugarcane Evapotranspiration 602.1 578.4 576.1 587.5 595.0 595.1 571.9 560.8 583.5 595.3
% area 0.03 0.10 0.61 7.35 7.44 19.17 19.26 16.40 8.16 21.48
Kharif fodder Evapotranspiration 529.3 525.6 513.1 534.3 544.7 516.5 482.9 505.1 460.7 530.5
% area 4.1 4.6 16.7 9.9 16.6 10.2 11.7 6.9 17.8 1.6
Rice Evapotranspiration 589.9 581.4 576.9 604.4 619.4 604.6 555.6 553.1 565.6 600.1
% area 30.5 24.4 9.6 16.2 0.6 2.0 2.0 1.9 7.5 5.4
Table 10  Irrigation-subdivision wise seasonal average evapotranspiration (mm) and percent of total cultivated area for each LULC class in the study area
Sr. No. Scenario Type Sagar Chuharkana Paccadala Mohlan Buchiana Tandla Tarkhani Kanya Bhagat Sultanpur CWU*
1 25% decrease in Rice area by replacing it with K.fodder & S.cane Type-I 100% Rice to K.fodder 50% each to K.fodder & S.cane (600.7) 100% Rice to K.fodder (37.5) 100% Rice to S.cane
(15.9)		 50% Rice each to K.fodder & S.cane 4452.4
1579.6 1163.8 524.3 47.1 32.7 280.2 170.5
2 25% decrease in Rice area by its major conversion to K.fodder 100% Rice to K.fodder 100% to S.cane
(15.9)		 100% Rice to K.fodder 4402.7
1579.6 1163.8 524.3 484.2 37.5 60.7 39.1 337.8 159.7
3 25% decrease in Rice area by replacing it with S.cane 100% Rice to K.fodder 100% to S.cane
(116.4)		 100% to K.fodder
(37.5)		 100% to S.cane
(15.9)		 100% Rice to S.cane 3370.8
1579.6 1163.8 524.3 -13.6 -6.4 -57.6 10.8
4 50% decrease in Cotton area by its conversion to S.cane & K.fodder 100% Cotton to S.cane 50% Cotton each to K.fodder and S.cane 100% to K.fodder
(722.8)		 100% to S.cane
(-263.0)		 -139.7
-56.4 -151.7 47.9 -53.8 189.0 -305.1 -43.5 -225.9
5 50% decrease in Cotton area by its major conversion to S.cane Type II 100% Cotton to S.cane 100% to K.fodder
(722.8)		 100% to S.cane
(-263.0)		 -1729.7
-56.4 -151.7 -94.9 -230.9 -157.6 -711.6 -418.2 -368.3
6 50% decrease in Cotton area by its major conversion to K.fodder Type-I 100% to S.cane 100% Cotton to K.fodder 100% to S.cane
(-263.0)		 1841.8
-56.4 -151.7 142.8 177.1 346.6 406.4 374.7 142.4 722.8
7 50% increase in K.fodder by replacing Rice, Cotton and S.cane 100% K.fodder from Rice 50% each from Rice and Cotton
(718.3)		 50% K.fodder each from S.cane and Cotton 50% each from Cotton & Rice
(1225.7)		 50% K.fodder each from Cotton and Rice
55.2		 4662.4
208.3 217.5 320.6 595.1 462.1 650.5 209.2
8 50% increase in K.fodder by major replacement of S.cane & Cotton 100% K.fodder from Rice 100% K.fodder from S.cane 100% from K. fodder Cotton 2838.6
208.3 217.5 450.9 223.6 352.7 338.9 441.7 163.6 435.0 6.5
9 50% increase in K.fodder area by major replacement of Cotton & Rice 100% K.fodder from Rice 100% K.fodder from Cotton 100% from Rice
(790.7)		 100% from Rice
(48.6)		 2249.6
208.3 217.5 267.4 97.0 242.4 123.2 208.7 45.6
Table 11  LULC change scenarios and water utilization (ha-m)
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