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Dr Kasper Johansen & Tri RaharjoRemote Sensing Research CentreThe University of Queensland
Pre- and Post-Pruning Assessment of Lychee Tree Crop Structure Using
Multi-Spectral RPAS Imagery
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Outline
1. Introduction and Objectives
2. Study Area
3. Data
4. Methods
5. Results
6. Conclusions and Future Work
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1. Introduction
Tropical tree native to China, producing small fleshy fruit.
China and India account for > 80% of total production.
Lychee production in Australia worth > $20m annually.
Australian lychees in northern hemisphere off-season.
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1. Introduction
Pruning of trees:
encourages new growth;
has a positive effect on fruiting of lychee;
makes fruit-picking easier; and
increases yield, as it increases light interception and tree crown surface area.
Objectives:
to assess changes in tree structure (tree crown height, area, circumference and width, and plant projective cover) using multi-spectral RPAS imagery collected before and after pruning of a lychee plantation; and
to assess any variations in the results as a function of three different flying heights.
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2. Study Area – Shailer Park, QLD
Shailer Park located 25 km southeast of Brisbane
Site context
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2. Study Area – Shailer Park, QLD
Shailer Park located 25 km southeast of Brisbane
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3. RPAS Data
Green, Red, Red Edge, NIR bands
11 Feb 2017 (pre-pruning
4 March 2017 (post-pruning)
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3. RPAS Data
Tower Beta app, 80% overlap, 1 photo / sec, 5 m/s
30 m = 4.1 cm pixels, 50 m = 6.5 cm pixels, 70 m = 8.8 cm pixels
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3. Field Data
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3. Field Data
89 trees sampled, 4 March
Tree height
Tree crown circumference
Tree crown width
Plant projective crown cover
78.25%
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4. Methods – Image Processing
Generation of geometrically corrected orthomosaics, DSM and DTM in Pix4D Mapper
40 0 4020 Meters
11 February 2017 4 March 2017 DSM DTM
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4. Methods – Radiometric Correction
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4. Methods – Radiometric Correction
Green Red
Red Edge NIR
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4. Methods – Radiometric Correction
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4. Methods – GEOBIA
Map lychee tree extent –CHM and spectral info
Identify tree crown centres
Grow tree crown centres based on CHM
Adjust shape and tree crown dimensions50 m 50 m
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4. Methods – RPAS Derived Tree Crown Parameter Extraction
Spectral Information:
Green, Red, Red Edge, NIR;
Brightness, NIR+Red Edge, NDRE, NDVI
Height Information:
Average CHM, Max CHM, 90 Percentile per tree crown
Geometry:
Area, Perimeter length, Length, Width
Texture:
GLCM Homogeneity, Contrast, Dissimilarity, Standard deviation using the Green, Red, Red Edge and NIR bands
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5. Results – Tree Crown Delineation
50 m
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5. Results – Tree Crown Perimeter
RMSE = 4.57 m RMSE = 3.63 m RMSE = 3.42 m
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5. Results – Tree Crown Width
Similar results for all flying heights
Measurements of tree crown width not affected by pixel size
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5. Results – Tree Crown Height
The DTM had higher elevation (up to 60 cm) in some locations for the data set collected at 70 m
The DSM showed a lowering of relative height of features above ground with increasing flying height.
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5. Results – Plant Projective Cover
RedEdge and NIR bands produced highest correlation
Very low correlation between red band and PPC
Texture explains some variation in PPC
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5. Results – Plant Projective Cover
NDVI insensitive to PPC variation due to red band
Brightness under- and Red Edge over-estimated PPC
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5. Results – Pre- and Post Pruning
NIR used to predict PPC for 11 Feb 2017 (pre-pruning)
An average of 14.8% decrease in PPC, using the two data sets collected at 30 m height.
Smaller trees not pruned
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5. Results – Pre- and Post Pruning
Height differences based DTM and DSM quality
An average of 61.6 cm decrease in height, using the two data sets collected at 30 m height.
Smaller trees not pruned
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5. Results – Pre- and Post Pruning
Area differences based on quality of tree crown delineation
An average of 3.5 m2 decrease in area, using the two data sets collected at 30 m height
Smaller trees not pruned and showed slight area increase
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5. Results – Pre- and Post Pruning
Perimeter differences based on quality of tree crown delineation
An average of 1.94 m decrease in perimeter, using the two data sets collected at 30 m height
Smaller trees not pruned and showed slight increase
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5. Results – Pre- and Post Pruning
Tree crown dimension differences based on quality of tree crown delineation
An average of 65.3 cm decrease in length, using the two data sets collected at 30 m height
Smaller trees not pruned and showed slight increase
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5. Results – Pre- and Post Pruning
Tree crown dimension differences based on quality of tree crown delineation
An average of 56.7 cm decrease in width, using the two data sets collected at 30 m height
Smaller trees not pruned and showed slight increase
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5. Results – Pre- and Post Pruning
Difference between 30 m, 50 m and 70 m flying height Plant Projective Cover, n = 89
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5. Results – Pre- and Post Pruning
Average differences between 30 m, 50 m and 70 m flying height Height, Area, Perimeter, Length and Width, n = 89
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6. Conclusions
eCognition Developer could be used to map individual lychee tree crowns
The Sequoia image data were found suitable for assessing pre- and post-pruned tree crown structure: Plant projective cover (best predicted with NIR band)
Tree height (most accurately mapped at 30 m height)
Tree crown perimeter, area, and dimensions
Tree crown perimeter most accurately mapped at 70 m
Tree crown width and length similar for all flying heights
PPC accurately predicted at all three flying heights, although data collected at 70 m produced slightly higher correlation for most predictor variables.
The developed approach may be used to assess pruning efforts undertaken by contractors
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Dr Kasper Johansen & Tri RaharjoRemote Sensing Research CentreThe University of Queensland
Pre- and Post-Pruning Assessment of Lychee Tree Crop Structure Using
Multi-Spectral RPAS Imagery
