Structural complexity biases vegetation greenness measures

Zeng, Yelu; Hao, Dalei; Park, Taejin; Zhu, Peng; Huete, Alfredo; Myneni, Ranga; Knyazikhin, Yuri; Qi, Jianbo; Nemani, Ramakrishna R.; Li, Fa; Huang, Jianxi; Gao, Yongyuan; Li, Baoguo; Ji, Fujiang; Köhler, Philipp; Frankenberg, Christian; Berry, Joseph A.; Chen, Min

doi:10.1038/s41559-023-02187-6

Cited by 34 publications

(8 citation statements)

References 57 publications

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“…This discrepancy could be attributed to local environmental factors and the complexity of forest structures, which impact surface reflectance. The influence of shadows, caused by complex structures, introduces uncertainty, highlighting the importance of biome-specific calibration, as [ 33 ] has suggested. Furthermore, atmospheric effects, such as Rayleigh and aerosol scattering [ 34 ], along with the scattering ratio of the red band being approximately three times higher than that of the NIR band [ 8 ], may also contribute to errors.…”

Section: Discussionmentioning

confidence: 99%

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

Lee,

Lim,

Lee

et al. 2024

Sensors

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As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

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Section: Discussionmentioning

confidence: 99%

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

Lee,

Lim,

Lee

et al. 2024

Sensors

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“…Guo et al [8] and Abdollahnejad et al [36] have similarly emphasized the significance of the red-edge band in plant identification. This is attributed to the sharp increase in plant reflectivity in the near-infrared band, resulting in a distinctive reflection peak at the red edge [4,37]. Consequently, plants exhibit sensitivity to the near-infrared and red-edge bands, making the addition of the INDE features with these two bands highly beneficial for crop species identification.…”

Section: Discussionmentioning

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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“…Compared to LULC, VIs are of various types; however, there are three types that are commonly used to reflect vegetated greenness, namely, the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIR v ) [45]. The NDVI is calculated based on near-infrared reflectance (841-876 nm) and red reflectance (620-670 nm), which is recognized as one of the most effective indicators for vegetation changes [46].…”

Section: Indicator Selectionmentioning

confidence: 99%

Spatio-Temporal Pattern of Urban Green Space in Chengdu Urban Center under Rapid Urbanization: From the Policy-Oriented Perspective

Li,

Du,

Yang

et al. 2024

Land

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Urban green space (UGS) is increasingly recognized as a nature-based solution to achieving urban sustainable development. Under rapid urbanization, greening policies are often the main driving factor behind the restoration or even growth of UGS. In this study, Chengdu, the pioneering “park city” in China, is chosen as a representative region. Based on land use/land cover (LULC) and normalized difference vegetation index (NDVI) data, indicators that can reflect both UGS quantity and quality are constructed and the spatio-temporal characteristics of UGS in original and expanding urban areas are also explored at different greening policy stages. The findings show that, from 2000 to 2022, the basic trend of UGS reduction during urbanization remained unchanged, despite the greening policies implemented in Chengdu. However, the original urban area has evolved into a new urban area. This has been achieved by integrating the expanded urban area with higher greening rates, resulting in the greening rate in 2022 (44.61%) being restored to the 2000 level (44.21%). The implementation of green policies in Chengdu is primarily reflected in improved UGS quality, especially in the stage of the ecological garden city construction (2008–2018). Specifically, the UGS quality in the original urban area has been improved by 25.25%. Overall, the UGS quality in Chengdu Urban Center has improved, changing from a medium level in 2000 to a medium-high level in 2022. The construction of a national demonstration zone of the park city provides an opportunity for the UGS quantity to increase and quality to improve in Chengdu in the future. However, effectively considering the development positioning of the Tianfu Granary to coordinate the relationship between UGS and high-quality farmland is a huge challenge for urban sustainable development in Chengdu.

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Structural complexity biases vegetation greenness measures

Cited by 34 publications

References 57 publications

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

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

Spatio-Temporal Pattern of Urban Green Space in Chengdu Urban Center under Rapid Urbanization: From the Policy-Oriented Perspective

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