Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of Southern Nigeria

Sehinde, Akinbiola; Salami, Ayobami T.; Awotoye, O. O.; Popoola, Samuel .O.; Olusola, A. Johnson

doi:10.1080/10106049.2023.2190621

Cited by 3 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assessments of forest structural parameters [10,11] involve various terminologies, such as stand parameters [12,13], forest structural attributes [11,[14][15][16][17], forest biophysical properties [18][19][20], and tree attributes [7,21]. Previous studies have explored diverse forest structural parameters, including stand top height, mean H, max H, Lorey's mean height (HL), stand mean DBH, number of trees, stem density (Sden), BA, stand V, and aboveground biomass (AGB), canopy cover (CC), crown area (CA), crown width, crown length, and crown volume [7,11,12,14,[18][19][20][22][23][24]. In our study, we considered Hd, DBH, BA, V, CST, Sden, and the broadleaf ratio (BLr) as forest structural parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In remote sensing studies, variables used to predict forest structural parameters are height-related metrics (maximum, minimum, mean, percentile height, standard deviation, covariance, kurtosis, and skewness) and canopy attributes (canopy gap, canopy density, CC, canopy area, crown diameter, crown height, canopy V, and canopy profile metrics) [22,[26][27][28][29][30]. Previous studies have estimated forest structural parameters using area-based approaches (plot, stand, or landscape level) [18,19,31], an individual-based approach [7,10,13,15,20,21], or both [14,17]. In this study, forest structural parameters were estimated using an area-based approach suitable for explaining forest complexity and spatial distribution patterns [32,33].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Karthigesu,

Owari,

Tsuyuki

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Karthigesu,

Owari,

Tsuyuki

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Furthermore, while ALS data depend on a dedicated aircraft campaign and may involve higher costs, operational testing of our hypothesis on Unoccupied Aerial Vehicles (UAVs) data might provide a practical and scalable approach. The recently developed technology centered around these new vectors, specifically photogrammetry that employs structure-from-motion algorithms, has resulted in the creation of highly precise orthomosaics and 3D information across vast areas at a relatively low expense, with spatial resolutions ranging from centimeters to millimeters suitable to derive information on vegetation structure 1 . Previous researches 9 , 10 , 33 , 38 , 77 has demonstrated that UAV imagery can be utilized to gauge vegetation attributes, including diversity, species, and plant species distribution, as well as to map and track invasive species.…”

Section: Introductionmentioning

confidence: 99%

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

Torresani,

Rocchini,

Ceola

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique “Structure from Motion” (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an $$R^2$$ R 2 of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao’s Q, Coefficient of Variation, Berger–Parker index, and Simpson’s D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao’s Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

show abstract

Influence of Main Flight Parameters on the Performance of Stand-Level Growing Stock Volume Inventories Using Budget Unmanned Aerial Vehicles

Lisańczuk,

Krok,

Mitelsztedt

et al. 2024

Forests

View full text Add to dashboard Cite

Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys.

show abstract

Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of Southern Nigeria

Cited by 3 publications

References 37 publications

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

Influence of Main Flight Parameters on the Performance of Stand-Level Growing Stock Volume Inventories Using Budget Unmanned Aerial Vehicles

Contact Info

Product

Resources

About