Developing object-based image procedures for classifying and characterising different protected agriculture structures using LiDAR and orthophoto

Tiwari, Arti; Silver, Micha; Karnieli, Arnon

doi:10.1016/j.biosystemseng.2020.07.017

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Due to the complexity of urban environments and mixed pixel issues, studies often favor OBIA for mapping urban vegetation [32,57,64]. Integrating LiDAR and high spatial resolution imagery with OBIA further enhances classification accuracy [40,65,66].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

Tiwari,

Kira,

Bamah

et al. 2024

Remote Sensing

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Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities.

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Section: Introductionmentioning

confidence: 99%

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

Tiwari,

Kira,

Bamah

et al. 2024

Remote Sensing

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“…The high-resolution aerial photographs contribute to recognizing vegetation features in challenging regions ( Silver et al, 2019 ). However, combining LiDAR-derived surfaces and applying OBIA methods enhances the skill of distinguishing complex structural features ( Tiwari et al, 2020a , Tiwari et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for automatic identification of ancient agricultural water harvesting systems

Tiwari

Silver

Karnieli

2023

International Journal of Applied Earth Observation and Geoinfor

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LiDAR applications in precision agriculture for cultivating crops: A review of recent advances

Rivera

Porras

Florencia

et al. 2023

Computers and Electronics in Agriculture

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Developing object-based image procedures for classifying and characterising different protected agriculture structures using LiDAR and orthophoto

Cited by 4 publications

References 27 publications

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

A deep learning approach for automatic identification of ancient agricultural water harvesting systems

LiDAR applications in precision agriculture for cultivating crops: A review of recent advances

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