Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats

Ventura, Daniele; Napoleone, Francesca; Cannucci, Silvia; Alleaume, Samuel; Valentini, Emiliana; Casoli, Edoardo; Burrascano, Sabina

doi:10.1016/j.jenvman.2022.115723

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Objectbased image analysis (OBIA), as an alternative to pixel-based classification, segments an image into meaningful objects and assigns a specific vegetation class to each object (Blaschke et al, 2014). A recent study by Ventura et al (2022) utilized OBIA classification and UAV imagery to monitor and map semi-natural grasslands. They successfully differentiated three grassland types, such as closed and open grasslands, achieving an overall classification accuracy of over 89%.…”

Section: Discussionmentioning

confidence: 99%

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning

Pranga,

Borra-Serrano,

Quataert

et al. 2024

Front. Plant Sci.

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IntroductionGrowing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA).MethodsThe OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a machine learning algorithm (Random Forest) to perform image segmentation and classification. Spatial k-fold cross-validation was employed to assess accuracy.Results and discussionResults demonstrated good performance, achieving an overall accuracy of approximately 70%, for both RGB and MS-based imagery, with grass and clover classes yielding similar F1 scores, exceeding 0.7 values. The effectiveness of the OBIA procedure and classification was examined by analyzing correlations between predicted clover fractions and dry matter yield (DMY) proportions. This quantification revealed a positive and strong relationship, with R2 values exceeding 0.8 for RGB and MS-based classification outcomes. This indicates the potential of estimating (relative) clover coverage, which could assist breeders but also farmers in a precision agriculture context.

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Section: Discussionmentioning

confidence: 99%

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning

Pranga,

Borra-Serrano,

Quataert

et al. 2024

Front. Plant Sci.

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“…Vegetation indices computed from RGB have been extensively used to segment vegetation from non-vegetation in imagery [31][32][33][34][35][36][37][38][39][40][41][42][43][44], and their development was often associated with commercial agriculture application(s) for evaluating crop or soil health when near-NIR or other bands were not available. However, these indices can be adapted by a broader remote-sensing community for more diverse applications such as segmenting vegetation from bare-Earth points in dense point clouds.…”

Section: Vegetation Classification and Indicesmentioning

confidence: 99%

Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds

Wernette

2024

Remote Sensing

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Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

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“…According to classification units in remote sensing imagery, classification methods can be categorized into two types: pixel-based and object-based [13]. Traditional pixel-based classification methods encounter challenges related to spectral heterogeneity and similarity in high-resolution remote sensing images, often resulting in the occurrence of salt-andpepper noise and suboptimal accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

Deng,

Zhang,

Zheng

et al. 2024

Agriculture

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The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.

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Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats

Cited by 9 publications

References 34 publications

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning

Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

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