Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain
"> Figure 1
<p>Study area: (<b>a</b>) location in the province of Malaga; (<b>b</b>) undulating relief and geometric planting framework; (<b>c</b>) areas with high VGC density; (<b>d</b>) areas with medium VGC density; and (<b>e</b>) no VGC (bare soil).</p> "> Figure 2
<p>Stages of the method used to quantify the density of vegetation ground cover (VGC).</p> "> Figure 3
<p>UAV quadcopter (<b>a</b>) and multispectral sensor (<b>b</b>).</p> "> Figure 4
<p>Quadrant-based method for quantifying VGC.</p> "> Figure 5
<p>Random distribution of the sampling plots within the NDVI image (centre), detail of the pixels constituting the average NDVI reflectance value for each SP (left), ground-truth (right) (* Coordinate System: ETRS 1989 UTM Zone 30N).</p> "> Figure 6
<p>Scatter diagrams and regression curves of the relation between vegetation cover (Ground-Truth) and VIs.</p> "> Figure 6 Cont.
<p>Scatter diagrams and regression curves of the relation between vegetation cover (Ground-Truth) and VIs.</p> "> Figure 7
<p>Quantification map of the VGC in Alozaina, made at a cover interval range of 30 % using IRVI index.</p> "> Figure 8
<p>Quantification map of the VGC in Casarabonela, made at a cover interval range of 30% using IRVI index.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Quantifying the VGC Density
2.2.1. UAV Flights and Image Mosaicking
2.2.2. Application of the Vegetation Indices (VIs)
2.2.3. Quantifying VGC Density by Means of Sampling Plots (Ground-Truth)
2.2.4. Calculating the Mean Reflectance Values of the Spectral Bands and the Vis in the Sampling Plots
2.2.5. Assessing the VIs to Quantify the VGC Density
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- FAO. Food and Agriculture Organization of the United Nations, FAOSTAT. 2016. Available online: http://www.fao.org/faostat/es/#data/QC (accessed on 6 September 2018).
- INE. National Institute of Statistics of Spain. Agrarian Census. 2009. Available online: https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176851&menu=resultados&secc=1254736194950&idp=1254735727106 (accessed on 6 September 2018).
- Infante, J. The ecology and history of the Mediterranean olive grove: The Spanish great expansion, 1750–2000. Rural Hist. 2012, 23, 161–184. [Google Scholar] [CrossRef]
- Lima-Cueto, F.J.; Gómez-Moreno, M.L.; Blanco-Sepúlveda, R. Mountain olive groves and soil preservation in the transition from an organic to an industrial economy: The case of Sierra de las Nieves (Málaga, Spain), 1940–1975. J. Depopulation Rural Dev. Stud. 2017, 23, 97–128. [Google Scholar]
- Moreno, B.; García-Rodriguez, S.; Cañizares, R.; Castro, J.; Benítez, E. Rainfed olive farming in south-eastern Spain: Long-term effect of soil management on biological indicators of soil quality. Agric. Ecosyst. Environ. 2009, 131, 333–339. [Google Scholar] [CrossRef]
- Brussaard, L.; Kuyper, T.W.; Didden, W.A.M.; de Goede, R.G.M.; Bloem, J. Biological soil quality from biomass to biodiversity–Importance and resilience to management stress and disturbance. In Managing Soil Quality: Challenges in Modern Agriculture; Schjønning, P., Elmholt, S., Christensen, B.T., Eds.; CAB International: Wallingford, UK, 2004; pp. 139–171. [Google Scholar]
- Sastre, B.; Marques, M.J.; García-Díaz, A.; Bienesa, R. Three years of management with cover crops protecting sloping olive groves soils, carbon and water effects on gypsiferous soil. Catena 2018, 171, 115–124. [Google Scholar] [CrossRef]
- Lima-Cueto, F.J.; Blanco-Sepúlveda, R.; Gómez-Moreno, M.L. Soil erosion and environmental regulations in the European Agrarian Policy for olive groves (Olea europea L.) of southern Spain. Agrociencia 2018, 52, 293–308. [Google Scholar]
- Fang, H.; Liang, S. Leaf Area Index Models. Reference Module in Earth Systems and Environmental Sciences. Encycl. Ecol. 2014, 2139–2148. Available online: https://www.sciencedirect.com/science/article/pii/B9780080454054001907 (accessed on 1 September 2018).
- Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 2000, 76, 156–172. [Google Scholar] [CrossRef]
- Baret, F.; Guyot, G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 1991, 35, 161–173. [Google Scholar] [CrossRef]
- Richardson, A.J.; Wiegand, C.L.; Wajura, D.F.; Dusek, D.; Steiner, J.L. Multisite analyses of spectral-biophysical data for sorghum. Remote Sens. Environ. 1992, 41, 71–82. [Google Scholar] [CrossRef]
- Song, W.; Mu, X.; Ruan, G.; Gao, Z.; Li, L.; Yan, G. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. Int. J. Appl. Earth Obs. Geoinf. 2017, 58, 168–176. [Google Scholar] [CrossRef]
- Xiao, J.; Moody, A. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sens. Environ. 2005, 98, 237–250. [Google Scholar] [CrossRef]
- Barati, S.; Rayegani, B.; Saati, M.; Sharifi, A.; Nasri, M. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egypt. J. Remote Sens. Space Sci. 2011, 14, 49–56. [Google Scholar] [CrossRef] [Green Version]
- Torres, J.; Peña, J.M.; De Castro, A.I.; Lopez, F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 2014, 103, 104–113. [Google Scholar] [CrossRef]
- Yun, H.S.; Park, S.H.; Kim, H.J.; Lee, W.D.; Lee, K.D.; Hong, S.Y.; Jung, G.H. Use of unmanned aerial vehicle for multi-temporal monitoring of soybean vegetation fraction. J. Biosyst. Eng. 2016, 41, 126–137. [Google Scholar] [CrossRef]
- Peña-Barragán, J.M.; Jurado-Expósito, M.; López-Granados, F.; Atenciano, S.; Sánchez-De la Orden, M.; García-Ferrer, A.; García-Torres, L. Assessing land-use in olive groves from aerial photographs. Agric. Ecosyst. Environ. 2004, 103, 117–122. [Google Scholar] [CrossRef]
- García, L.; Peña-Barragán, J.M.; López-Granados, F.; Jurado-Expósito, M.; Fernández-Escobar, R. Automatic assessment of agro-environmental indicator from remotely sensed images of tree orchards and its evaluation using olive plantions. Comput. Electron. Agric. 2008, 61, 179–191. [Google Scholar] [CrossRef]
- Torres-Sánchez, J.; López-Granados, F.; Serrano, N.; Arquero, O.; Peña, J.M. High-throughput 3-d monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE 2015, 10, e0130479. [Google Scholar] [CrossRef] [PubMed]
- Avola, G.; Di Gennaro, S.F.; Cantini, C.; Riggi Ezio Muratore, F.; Tornambe, C.; Matese, A. Remotely sensed vegetation indices to discriminate field-grown olive cultivars. Remote Sens. 2019, 11, 1242. [Google Scholar] [CrossRef]
- De Castro, A.I.; Six, J.; Plant, R.E.; Peña, J.M. Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California. Remote Sens. 2018, 10, 1745. [Google Scholar] [CrossRef]
- Pinilla, C.; Ariza, F.J.; Sánchez, M.; Tovar, J. Improvement of the reliability in the identification of the olive grove using a geometric reflectance model. J. Remote Sens. 2001, 16, 11–15. (In Spanish) [Google Scholar]
- Huete, A.R.; Jackson, R.D.; Post, D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 1985, 17, 37–53. [Google Scholar] [CrossRef]
- Iaquinta, J.; Fouilloux, A. Influence of the heterogeneity and topography of vegetated land surfaces for remote sensing applications. Int. J. Remote Sens. 1988, 19, 1711–1723. [Google Scholar] [CrossRef]
- Saberioon, M.M.; Amin, M.S.M.; Anuar, A.R.; Gholizadeh, A.; Wayayok, A.; Khairunniza-Bejo, S. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. Int. J. Appl. Earth Obs. Geoinf. 2014, 32, 35–45. [Google Scholar] [CrossRef]
- De Castro, A.I.; Jiménez-Brenes, F.M.; Torres-Sánchez, J.; Peña, J.M.; Borra-Serrano, I.; López-Granados, F. 3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications. Remote Sens. 2018, 10, 584. [Google Scholar] [CrossRef]
- Catur, A.R. The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environ. Sci. 2015, 24, 245–253. [Google Scholar]
- FAO. World Reference Base for Soil Resources 2014. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. Update 2015; Food and Agriculture Organization of the United Nations: Rome, Italy, 2014; ISSN 0532-0488. [Google Scholar]
- Foraster, L.; Gómez, E.J.; Iglesias, I.; Macías, F.J.; Ruiz, M. Study for the Delimitation of Experimental Zones. Olive Grove of the Sierra de Las Nieves Region. Characterization and Recommendations for Ecological Management; Mancomunidad de Municipios Sierra de las Nieves: Málaga, Spain, 2011; ISBN 978-84-930235-5-4. (In Spanish) [Google Scholar]
- Hunt, E.R.; Hively, W.D.; Fujikawa, S.J.; Linden, D.S.; Daughtry, C.S.T.; McCarty, G.W. Acquisition of NIR-green–blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2010, 2, 290–305. [Google Scholar] [CrossRef]
- Richardson, A.J.; Wiegand, C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 1977, 43, 1541–1552. [Google Scholar]
- Birth, G.S.; Birth, G.R. Measuring the color of growing turf with a reflectance spectrophotometer. Am. Soc. Agron. 1968, 60, 640–643. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite–1 Syposium; Freden, S.C., Mercanti, E.P., Becker, M., Eds.; Technical Presentations, NASA SP-351; NASA: Washington, DC, USA, 1973; Volume 1, pp. 309–317. [Google Scholar]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocatanum I and Hacer plantanoides I. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef] [Green Version]
- Falkowski, M.J.; Gessler, P.E.; Morgan, P.; Hudak, A.T.; Smith, A.M.S. Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. For. Ecol. Manag. 2005, 217, 129–146. [Google Scholar] [CrossRef] [Green Version]
- Cruden, B.A.; Prabhu, D.; Martinez, R. Absolute radiation measurement in Venus and Mars entry conditions. J. Spacecr. Rocket. 2012, 49, 1069–1079. [Google Scholar] [CrossRef]
- Huete, A.R. A soil vegetation adjusted index (Savi). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Vogelmann, B.N.; Rock, D.M. Moss Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563–1575. [Google Scholar] [CrossRef]
- Richardson, A.; Everitt, J. Using spectral vegetation indices to estimate rangeland productivity. Geocarto. Int. 1992, 1, 63–77. [Google Scholar] [CrossRef]
- Verbesselt, J.; Somers, B.; Van Aardt, J.; Jonckheere, I.; Coppin, P. Monitoring herbaceous biomass and water content with spot vegetation time-series to improve fire risk assessment in savanna ecosystems. Remote Sens. Environ. 2006, 101, 399–414. [Google Scholar] [CrossRef]
- Carlson, D.A.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62, 241–252. [Google Scholar] [CrossRef]
- Hassan, M.A.; Yang, M.; Rasheed, A.; Yang, G.; Reynolds, M.; Xia, X.; Xiao, Y.; He, Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci. 2019, 282, 95–103. [Google Scholar] [CrossRef]
- Tucker, C.J. A spectral method for determining the percentage of green herbage material in clipped samples. Remote Sens. Environ. 1980, 9, 175–181. [Google Scholar] [CrossRef]
- Khajeddin, S.J. A Survey of the Plant Communities of the Jazmorian, Iran, Using Landsat MSS Data; Reading University: Reading, UK, 1995. [Google Scholar]
- Dong, T.; Lui, J.; Shang, J.; Qian, B.; Ma, B.; Kovacs, J.M.; Walters, D.; Jiao, X.; Geng, X.; Shi, Y. Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sens. Environ. 2019, 222, 133–143. [Google Scholar] [CrossRef]
- Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999–4014. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Eitel, J.U.H.; Vierling, L.A.; Litvak, M.E.; Long, D.S.; Schulthess, U.; Ager, A.A.; Krofcheck, D.J.; Stoscheck, L. Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sens. Environ. 2011, 115, 3640–3646. [Google Scholar] [CrossRef]
- Marx, A. The Impact of the RapidEye Red Edge Band in Mapping Defoliation Symptoms; ESA Living Planet Symposium: Edinburgh, UK, 2013; Available online: http://seom.esa.int/LPS13/5405ddfb/ (accessed on 1 July 2017).
Index | Formula a | Reference |
---|---|---|
IRVI (Inverse Ratio Vegetation Index) | [32] | |
RVI (Ratio Vegetation Index) | [33] | |
DVI (Difference Vegetation Index) | [32] | |
NDVI (Normalised Difference Vegetation Index) | [34] | |
GNDVI (Green Normalised Difference Vegetation Index) | [35] | |
NDRE (Normalised Difference Red Edge) | [36] | |
GRVI (Green-Red Vegetation Index) | [37,38] | |
GVI (Green Vegetation Index) | [39] | |
NRVI (Normalised Ratio Vegetation Index) | [11] | |
SAVI (Soil-Adjusted Vegetation Index) b | [40] | |
VREI (Vogelmann Rededge Index) | [41] |
Band | Regression Model | R | R2 | Adjusted R2 | P-Value |
---|---|---|---|---|---|
R | y = 108.05 − 413R | 0.765 | 0.584 | 0.581 | p < 0.001 |
G | y = 135.63 − 659.93G | 0.710 | 0.504 | 0.500 | p < 0.001 |
NIR | y = − 14.46 + 217.92NIR | 0.571 | 0.326 | 0.320 | p < 0.001 |
RE | y = − 6.08 + 237.197RE | 0.409 | 0.167 | 0.160 | p < 0.001 |
R, NIR | y = 49.53 − 358.58R + 155.76NIR | 0.861 | 0.741 | 0.737 | p < 0.001 |
R, NIR, G | y = 60.41−161.04R + 173.28NIR−346.05G | 0.867 | 0.752 | 0.745 | p < 0.001 |
Cover Interval Range (%) | Significant Cover Intervals (Analysis Pairs) | VI |
---|---|---|
10 | 0–10 ↔ 10–20 ↔ 20–30 ↔ 30–40 ↔ 40–50 ↔ 50–60 ↔ 60–70 ↔ 70–80 ↔ 80–90 ↔ 90–100 p < 0.001 | RVI |
0–10 ↔ 10–20 ↔ 20–30 ↔ 30–40 ↔ 40–50 ↔ 50–60 ↔ 60–70 ↔ 70–80 ↔ 80–90 ↔ 90–100 p < 0.01 | DVI GVI SAVI | |
0–10 ↔ 10–20 ↔ 20–30 ↔ 30–40 ↔ 40–50 ↔ 50–60 ↔ 60–70 ↔ 70–80 ↔ 80–90 ↔ 90–100 p < 0.05 | NRVI NDVI | |
0–10 ↔ 10–20 ↔ 20–30 ↔ 30–40 ↔ 40–50 ↔ 50–60 ↔ 60–70 ↔ 70–80 ↔ 80–90 ↔ 90–100 | Other VIs | |
15 (10) | 0–15 ↔ 15–30↔ 30–45 ↔ 45–60 ↔ 60–75 ↔ 75–90 ↔ 90–100 p < 0.01 p < 0.01 | IRVI |
0–15 ↔ 15–30↔ 30–45 ↔ 45–60 ↔ 60–75 ↔ 75–90 ↔ 90–100 p < 0.05 p < 0.001 | NRVI NDVI GNDVI | |
0–15 ↔ 15–30 ↔ 30–45 ↔ 45–60 ↔ 60–75 ↔ 75–90 ↔ 90–100 p < 0.001 | RVI DVI GVI SAVI | |
0–15 ↔ 15–30 ↔ 30–45 ↔ 45–60 ↔ 60–75 ↔ 75–90 ↔ 90–100 p < 0.05 | VREI | |
0–15 ↔ 15–30 ↔ 30–45 ↔ 45–60 ↔ 60–75 ↔ 75–90 ↔ 90–100 | Other VIs | |
20 | 0–20 ↔ 20–40↔ 40–60 ↔ 60–80 ↔ 80–100 p < 0.001 p < 0.001 | IRVI NRVI NDVI GNDVI |
0–20 ↔ 20–40↔ 40–60 ↔ 60–80 ↔ 80–100 p < 0.01 p < 0.001 | SAVI | |
0–20 ↔ 20–40↔ 40–60 ↔ 60–80 ↔ 80–100 p < 0.05 p < 0.05 | NDRE | |
0–20 ↔ 20–40 ↔ 40–60 ↔ 60–80 ↔ 80–100 p < 0.001 | RVI DVI GVI | |
0–20 ↔ 20–40 ↔ 40–60 ↔ 60–80 ↔ 80–100 p < 0.05 | VREI | |
0–20 ↔ 20–40 ↔ 40–60 ↔ 60–80 ↔ 80–100 | Other VIs | |
25 | 0–25 ↔ 25–50 ↔ 50–75 ↔ 75–100 p < 0.001 p < 0.001 | IRVI NRVI NDVI GNDVI SAVI |
0–25 ↔ 25–50 ↔ 50–75 ↔ 75–100 p < 0.05 p < 0.001 | DVI NDRE | |
0–25 ↔ 25–50 ↔ 50–75 ↔ 75–100 p < 0.001 | RVI GVI GRVI VREI | |
0–25 ↔ 25–50 ↔ 50–75 ↔ 75–100 | Other VIs | |
30 (10) | 0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.001 p < 0.01 p < 0.001 | IRVI NRVI NDVI |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.001 p < 0.05 p < 0.001 | GNDVI SAVI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.001 p < 0.001 | DVI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.01 p < 0.001 | GVI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.05 p < 0.01 | NDRE | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.05 p < 0.001 | GRVI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.001 | RVI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 p < 0.01 | VREI | |
0–30 ↔ 30–60 ↔ 60–90 ↔ 90–100 | Other VIs |
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Lima-Cueto, F.J.; Blanco-Sepúlveda, R.; Gómez-Moreno, M.L.; Galacho-Jiménez, F.B. Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain. Remote Sens. 2019, 11, 2564. https://doi.org/10.3390/rs11212564
Lima-Cueto FJ, Blanco-Sepúlveda R, Gómez-Moreno ML, Galacho-Jiménez FB. Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain. Remote Sensing. 2019; 11(21):2564. https://doi.org/10.3390/rs11212564
Chicago/Turabian StyleLima-Cueto, Francisco J., Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, and Federico B. Galacho-Jiménez. 2019. "Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain" Remote Sensing 11, no. 21: 2564. https://doi.org/10.3390/rs11212564
APA StyleLima-Cueto, F. J., Blanco-Sepúlveda, R., Gómez-Moreno, M. L., & Galacho-Jiménez, F. B. (2019). Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain. Remote Sensing, 11(21), 2564. https://doi.org/10.3390/rs11212564