Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping
Received: 6 March 2024 | Revised: 19 March 2024 | Accepted: 21 March 2024 | Online: 1 June 2024
Corresponding author: Sheroz Khan
Abstract
Temporal-vegetation mapping bearing temporal-related features is important because it helps to understand the global climate changes that drive resource management and habitat conservation. This paper presents a Supervised Normalized Difference Vegetation Index (SNDVI) approach for mapping the vegetation cover in arid environment regions. The NDVI is used to extract features to classify land as a vegetation cover, water body, or bare soil. Through the use of Normalized Difference Vegetation Index (NDVI), regions can be categorized as dry or sandy, based on the soil reflectance values. NDVI is the most commonly deployed index for accurate vegetation cover estimates. The NDVI values lie in a range from -1 to +1, depending on the environmental region and vegetation conditions. It is difficult to assign a specific threshold value to distinguish between vegetation and non-vegetation for all the eco-regions under a specific landscape and ecological conditions. The proposed approach is based on the quantitative verification of the samples as well as the supervised classification method followed to categorize the images. The SNDVI approach has been applied to three different locations in three different seasons in arid ecoregions to extract features for vegetation mapping. The results disclose that SNDVI is a very reliable parameter in extracting true vegetation cover in arid regions. An accuracy evaluation matrix has been performed for each case study and the overall obtained accuracy value ranged from 82% to 100%, depending on the season of the area under investigation. The utility of the proposed method is determined by bench-marking the results with those of the techniques recently utilized by contemporary researchers.
Keywords:
crop land mapping, vegetation indices, remote sensingDownloads
References
Y. Xie, Z. Sha, and M. Yu, "Remote sensing imagery in vegetation mapping: a review," Journal of Plant Ecology, vol. 1, no. 1, pp. 9–23, Mar. 2008.
C. He, Q. Zhang, Y. Li, X. Li, and P. Shi, "Zoning grassland protection area using remote sensing and cellular automata modeling—A case study in Xilingol steppe grassland in northern China," Journal of Arid Environments, vol. 63, no. 4, pp. 814–826, Dec. 2005.
P. S. Nagendram, P. Satyanarayana, and P. R. Teja, "Mapping Paddy Cropland in Guntur District using Machine Learning and Google Earth Engine utilizing Images from Sentinel-1 and Sentinel-2," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12427–12432, Dec. 2023.
M. K. Villareal and A. F. Tongco, "Remote Sensing Techniques for Classification and Mapping of Sugarcane Growth," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6041–6046, Aug. 2020.
H. Tahir and A. H. M. Din, "The Potential of Landsat 8 OLI Images in Coastline Identification: The Case Study of Basra, Iraq," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 13041–13046, Feb. 2024.
C. Eisfelder, C. Kuenzer, and S. Dech, "Derivation of biomass information for semi-arid areas using remote-sensing data," International Journal of Remote Sensing, vol. 33, no. 9, pp. 2937–2984, May 2012.
D. Moravec, J. Komarek, S. Lopez-Cuervo Medina, and I. Molina, "Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors," Remote Sensing, vol. 13, no. 18, Jan. 2021, Art. no. 3550.
A. Karnieli et al., "Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations," Journal of Climate, vol. 23, no. 3, pp. 618–633, Feb. 2010.
A. L. Yagci, L. Di, and M. Deng, "The influence of land cover-related changes on the NDVI-based satellite agricultural drought indices," in IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, Jul. 2014, pp. 2054–2057.
S. S. Panda, D. P. Ames, and S. Panigrahi, "Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques," Remote Sensing, vol. 2, no. 3, pp. 673–696, Mar. 2010.
M. S. Mkhabela, P. Bullock, S. Raj, S. Wang, and Y. Yang, "Crop yield forecasting on the Canadian Prairies using MODIS NDVI data," Agricultural and Forest Meteorology, vol. 151, no. 3, pp. 385–393, Mar. 2011.
J. White, A. A. Berg, C. Champagne, Y. Zhang, A. Chipanshi, and B. Daneshfar, "Improving crop yield forecasts with satellite-based soil moisture estimates: An example for township level canola yield forecasts over the Canadian Prairies," International Journal of Applied Earth Observation and Geoinformation, vol. 89, Jul. 2020, Art. no. 102092.
T. L. Nordblom, T. R. Hutchings, S. S. Godfrey, and C. R. Schefe, "Precision variable rate nitrogen for dryland farming on waterlogging Riverine Plains of Southeast Australia?," Agricultural Systems, vol. 186, Jan. 2021, Art. no. 102962.
R. M. Fokeng and Z. N. Fogwe, "Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon," Geosystems and Geoenvironment, vol. 1, no. 3, Aug. 2022, Art. no. 100075.
M. Aboelghar, S. Arafat, A. Saleh, S. Naeem, M. Shirbeny, and A. Belal, "Retrieving leaf area index from SPOT4 satellite data," The Egyptian Journal of Remote Sensing and Space Science, vol. 13, no. 2, pp. 121–127, Dec. 2010.
D. Dutta, A. Kundu, and N. R. Patel, "Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index," Geocarto International, vol. 28, no. 3, pp. 192–209, Jun. 2013.
S. Mondal, C. Jeganathan, N. K. Sinha, H. Rajan, T. Roy, and P. Kumar, "Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India," The Egyptian Journal of Remote Sensing and Space Science, vol. 17, no. 2, pp. 123–134, Dec. 2014.
M. Roznik, M. Boyd, and L. Porth, "Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks," Remote Sensing Applications: Society and Environment, vol. 25, Jan. 2022, Art. no. 100693.
P. Fabijanczyk and J. Zawadzki, "Spatial correlations of NDVI and MSAVI2 indices of green and forested areas of urban agglomeration, case study Warsaw, Poland," Remote Sensing Applications: Society and Environment, vol. 26, Apr. 2022, Art. no. 100721.
H. Demirel, C. Ozcinar, and G. Anbarjafari, "Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition," IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 2, pp. 333–337, Apr. 2010.
X. Zhang, Y. Hu, D. Zhuang, Y. Qi, and X. Ma, "NDVI spatial pattern and its differentiation on the Mongolian Plateau," Journal of Geographical Sciences, vol. 19, no. 4, pp. 403–415, Aug. 2009.
M. S. Omar and H. Kawamukai, "Prediction of NDVI using the Holt-Winters model in high and low vegetation regions: A case study of East Africa," Scientific African, vol. 14, Nov. 2021, Art. no. e01020.
D. Dutta, A. Kundu, N. R. Patel, S. K. Saha, and A. R. Siddiqui, "Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI)," The Egyptian Journal of Remote Sensing and Space Science, vol. 18, no. 1, pp. 53–63, Jun. 2015.
R. Chouhan and N. Rao, "Vegetation Detection in Multispectral Remote Sensing Images: Protective Role-Analysis of Vegetation in 2004 Indian Ocean Tsunami," in Gi4DM 2011: GeoInformation for disaster management, 2011.
A. Zaitunah, Samsuri, and F. Sahara, "Mapping and assessment of vegetation cover change and species variation in Medan, North Sumatra," Heliyon, vol. 7, no. 7, Jul. 2021, Art. no. e07637.
A. Fusami, O. Nweze, and R. Hassan, "Comparing the Effect of Deforestation Result by NDVI and SAVI," International Journal of Scientific and Research Publications, vol. 10, no. 6, pp. 918–925, Jun. 2020.
"Saudi Arabia," World Bank Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/saudi-arabia/climate-data-historical.
G. Gutman and A. Ignatov, "The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models," International Journal of Remote Sensing, vol. 19, no. 8, pp. 1533–1543, Jan. 1998.
S. Shastri, P. Singh, P. Verma, P. K. Rai, and A. P. Singh, "Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India," Journal of Landscape Ecology, vol. 13, no. 2, pp. 1–13, Sep. 2020.
X. Gong, S. Du, F. Li, and Y. Ding, "Study of mesoscale NDVI prediction models in arid and semiarid regions of China under changing environments," Ecological Indicators, vol. 131, Nov. 2021, Art. no. 108198.
J. Aryal, C. Sitaula, and S. Aryal, "NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia," Land, vol. 11, no. 3, Mar. 2022, Art. no. 351.
I. Beveridge, "Mammal parasites in arid Australia," International Journal for Parasitology: Parasites and Wildlife, vol. 12, pp. 265–274, Aug. 2020.
Downloads
How to Cite
License
Copyright (c) 2024 Ragab Khalil, Mohammad Shahiq Khan, Yassin Hasan, Nacer Nacer, Sheroz Khan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
