GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. WorldView-3 Satellite Image
- the positions of 20 Ground Control Points (GCPs) distributed across the scene were obtained through an NRTK (Network Real Time Kinematic) GNSS (Global Navigation Satellite System) survey. The GPSUmbria network [31] was used to determine the GCP positions using the Virtual Reference Station (VRS) mode (Figure 4);
- a Digital Elevation Model (DEM) was derived from the regional vectorial cartography [32] (scale 1:5000) with a resolution of 2 m.
2.2. Study Area and Surveying Methods
2.3. Geometric Model for Tree Height Measurement
- Sun’s azimuth (θS): the angle between the north direction and the Sun’s direction projected onto a horizontal plane (143.7° for this specific acquisition);
- Sun’s elevation (φS): the angle between the horizontal plane and the direction of the Sun (62.2° for this specific acquisition).
- The shadow length was estimated directly on the orthophoto along the Sun’s azimuth direction by creating a shape file containing a segment with an inclination of 143.7° from the shadow’s endpoint to the corresponding point on the canopy (Figure 9b);
- The segment length was measured using the “Add geometry attributes” tool;
- The two extreme points of the segment were then extracted using the “Extract vertices” tool of the vector menu (Figure 9c);
- The first point of the segment (ground point) was used to measure the terrain’s aspect and slope using the “Extract value by point” tool;
- The second point of the segment (canopy point) was used to identify the elevation of the corresponding point on the canopy from the UAV point cloud, which was later used to validate the tree height measured by the model.
- d represents the shadow length measured along the Sun’s azimuth direction;
- φS represents the Sun’s elevation angle;
- θS represents the Sun’s azimuth angle;
- α represents the terrain’s slope;
- β represents the terrain’s aspect.
3. Results and Validation of Tree Height Estimation
4. Discussion
- The satellite image was orthorectified using a photogrammetrically rigorous model [40], 20 ground control points (GCPs) measured with GNSS RTK receivers, and a digital elevation model (DEM) derived from 1:5000 scale vector cartography. The estimated planimetric accuracy of this orthorectification, based on independent check points (CPs), is approximately 20 cm. It should be noted that such planimetric accuracy cannot be achieved with raw satellite images or without the use of GCPs acquired via differential GPS/GNSS;
- The two drones used are equipped with GNSS/GPS RTK receivers, enabling a planimetric accuracy of a few centimeters and an altimetric accuracy of about one decimeter. Most drones currently in use do not employ RTK technology but rely solely on autonomous GNSS positioning, with accuracies of approximately 10 m planimetrically and 15 m in height. The accuracy of the drone-derived models was anyway further validated through comparisons with additional GCPs;
- The coordinates of the two surveys are compatible and comparable, as both are referenced to the same realization of the WGS84 datum (EPSG:6708 RDN2008/UTM zone 33N (N-E)) materialized by the same Continuously Operating Reference Station Network (CORSN) managed by the Umbria region [31];
- The respective planimetric accuracies of the two methods, with expected maximum deviations of a few decimeters, ensure the consistency of the comparative test, as they prevent the possibility of confusing one olive tree with another within the same row. The use of differential GNSS positioning technologies (here employed in RTK mode) is crucial for avoiding such misidentifications.
- The difference between measurements at the tree’s apex and those at the most easily recognizable point on its shadow;
- The influence of the recognizability of the measured points on the shadow and the corresponding olive tree canopy image;
- The impact of terrain slope, which, for simplicity, is considered constant over the length of the shadow. For this purpose, an average value derived from the reference DEM is used.
4.1. Statistical Results
4.2. Cost–Benefit Analysis
4.3. Limitations of the Proposed Methodology
5. Conclusions and Future Prospects
- The ease or difficulty of identifying specific points on the canopy and shadow;
- The choice of measuring from the canopy’s apex or the most recognizable point;
- The effect of terrain morphology by comparing flat and sloped areas.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Test 1a | H_UAV | H_Eq1 |
---|---|---|
Mean | 2.106232026 | 2.11813773 |
Variance | 0.206459206 | 0.184138669 |
Observations | 612 | 612 |
Total Variance | 0.195298938 | |
Hypothesized Difference for Means | 0 | |
Degrees of Freedom | 1222 | |
t Statistic | −0.471265718 | |
P(T ≤ t) One-Tailed | 0.318767557 | |
t Critical One-Tailed | 1.646101525 | |
P(T ≤ t) Two-Tailed | 0.637535113 | |
t Critical Two-Tailed | 1.961907178 |
Test 1b | H_UAV | H_Eq1 |
---|---|---|
Mean | 2.729218636 | 2.704842175 |
Variance | 0.218173587 | 0.21410717 |
Observations | 626 | 626 |
Total Variance | 0.216140378 | |
Hypothesized Difference for Means | 0 | |
Degrees of Freedom | 1250 | |
t Statistic | 0.927630189 | |
P(T ≤ t) One-Tailed | 0.176889254 | |
t Critical One-Tailed | 1.646073552 | |
P(T ≤ t) Two-Tailed | 0.353778508 | |
t Critical Two-Tailed | 1.961863609 |
Test 2 | H_UAV | H_Eq1 |
---|---|---|
Mean | 3,3134 | 3.351447175 |
Variance | 0.237838568 | 0.235708219 |
Observations | 20 | 20 |
Total Variance | 0.236773394 | |
Hypothesized Difference for Means | 0 | |
Degrees of Freedom | 38 | |
t Statistic | −0.247261197 | |
P(T ≤ t) One-Tailed | 0.403018587 | |
t Critical One-Tailed | 1.68595446 | |
P(T ≤ t) Two-Tailed | 0.806037174 | |
t Critical Two-Tailed | 2.024394164 |
Test 3 | H_UAV | H_Eq1 |
---|---|---|
Mean | 3.499627756 | 3.4565 |
Variance | 0.411250197 | 0.387079737 |
Observations | 20 | 20 |
Total Variance | 0.399164967 | |
Hypothesized Difference for Means | 0 | |
Degrees of Freedom | 38 | |
t Statistic | 0.215864214 | |
P(T ≤ t) One-Tailed | 0.415124071 | |
t Critical One-Tailed | 1.68595446 | |
P(T ≤ t) Two-Tailed | 0.830248143 | |
t Critical Two-Tailed | 2.024394164 |
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Type of Point | Residual Error—Ground (m) | Residual Error—Image (px) | |||
---|---|---|---|---|---|
East | North | Elevation | X | Y | |
GCPs | 0.099 | 0.103 | 0.025 | 0.33 | 0.36 |
CPs | 0.168 | 0.145 | 0.035 | 0.56 | 0.51 |
Test | Number of Trees | Mean Error (m) | Standard Deviation (m) | Mean Error Absolute Value (m) | |
---|---|---|---|---|---|
1a | Total | 612 | 0.015 | 0.190 | 0.140 |
“1” class | 494 (80.7%) | 0.014 | 0.195 | 0.144 | |
“0” class | 118 (19.3%) | 0.021 | 0.169 | 0.127 | |
1b | Total | 612 | −0.024 | 0.288 | 0.175 |
2 | Total | 20 | 0.043 | 0.135 | 0.115 |
3 | Total | 20 | 0.038 | 0.121 | 0.101 |
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Brigante, R.; Baiocchi, V.; Calisti, R.; Marconi, L.; Proietti, P.; Radicioni, F.; Regni, L.; Vinci, A. GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis. Appl. Sci. 2025, 15, 3066. https://doi.org/10.3390/app15063066
Brigante R, Baiocchi V, Calisti R, Marconi L, Proietti P, Radicioni F, Regni L, Vinci A. GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis. Applied Sciences. 2025; 15(6):3066. https://doi.org/10.3390/app15063066
Chicago/Turabian StyleBrigante, Raffaella, Valerio Baiocchi, Roberto Calisti, Laura Marconi, Primo Proietti, Fabio Radicioni, Luca Regni, and Alessandra Vinci. 2025. "GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis" Applied Sciences 15, no. 6: 3066. https://doi.org/10.3390/app15063066
APA StyleBrigante, R., Baiocchi, V., Calisti, R., Marconi, L., Proietti, P., Radicioni, F., Regni, L., & Vinci, A. (2025). GIS-Based Approach for Estimating Olive Tree Heights Using High-Resolution Satellite Imagery and Shadow Analysis. Applied Sciences, 15(6), 3066. https://doi.org/10.3390/app15063066