Geographic and Climatic Attributions of Autumn Land Surface Phenology Spatial Patterns in the Temperate Deciduous Broadleaf Forest of China

Lang, Weiguang; Chen, Xiaoqiu; Liang, Liang; Ren, Shuang; Qian, Siwei

doi:10.3390/rs11131546

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…On August 29th and September 19th, we measured the reflectance spectra at 14:00 to 15:00 ( Table 1 ). Regarding the PSRI variation, horizontally sensing of a plant at the canopy level was only able to proportionally capture the carotenoid and mesophyll changes in the cell structure and leaves [ 82 , 88 ], and these proportions remarkably varied from plant to plant. Noticeably, no structural VI was found to be useful for spectral classification because of the similarity in canopy structure and color (i.e., greenness) between healthy and diseased plants in such asymptomatic periods.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

136

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Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

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Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

136

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“…Furthermore, since the temperature is one of the important climatic factors affecting vegetation phenology (Cong et al, 2013;Liang and Zhang, 2016;Lang et al, 2019;Han et al, 2020), the seasonal curve of temperature in the high-, middle-, and low-latitude regions in different land cover types is shown in Figures 4-6. We can find that the fluctuations of temperature were similar in the same latitude region in different land cover types, but the fluctuations of vegetation curves (GPPs, SIF, and VIs) were different, which indicated that the phenological characteristics of different vegetation have different sensitivities to temperature.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Wang

Sun

et al. 2022

Front. Earth Sci.

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Phenology is an important biological indicator for monitoring terrestrial ecosystems and global change. Solar-induced chlorophyll fluorescence (SIF) emitted by chlorophyll has been proven to characterize vegetation photosynthesis and phenology. In this study, we used monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and SIF products to qualitatively compare the effectiveness at detecting the phenological characteristics (SOS (start-of-season), EOS (end-of-season), and LOS (length-of-season)) over China during 2007–2013. The phenological characteristics determined by gross primary productivity (GPP) were applied as the reference to validate the phenological characteristics derived from NDVI, EVI, and SIF. The results demonstrated that the phenological characteristics derived from SIF were more consistent with that of GPP than VIs (NDVI and EVI) when considering all latitude grades, different elevation grades, and different land cover types in China. In the middle- and high-latitude regions, SOS derived from the vegetation indices (SOSVIs) did not deviate from those from GPP (SOSGPP) and SIF (SOSSIF), while in low latitudes, SOSVIs were about 20 d later than SOSSIF and SOSGPP. The VIs (EOSVIs) had a severe lag behind those of SIF (EOSSIF) in estimating the EOS at all latitudes. The EOSSIF had a deviation of fewer than 5 d compared with EOS estimated by GPP (EOSGPP), whereas the deviation of EOSVIs from EOSGPP was about 10–31 d across low to high latitude regions. The biases of SIF and VIs were due to the inconsistency between vegetation photosynthesis and leaf greenness. Also, VIs overestimated the LOS at all latitudes, the difference of LOS between estimated by NDVI and estimated by GPP was as long as 39 d in the high-latitude region. Our study suggests that SIF is suitable for estimating the phenological characteristics of vegetation regardless of different latitudes, elevation grades, and land cover types in China, providing a basis for SIF to study the vegetation phenological characteristics in a regional scope.

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“…У дослідженні B. Ланг зі співроб. (Lang et al, 2019) виявили, що індекс старіння рослин найкраще являв просторовий розподіл змін рослинного покриву, що відбуваються восени наприкінці вегетації у листопадних широколистяних лісах помірного поясу в Китаї. Вчені припускають, що це може бути пов'язано з тим, що PSRI здатен фіксувати не тільки зміну структури клітин мезофілу, але й зміну відносної частки каротиноїдів та хлорофілу в листі, демонструючи більшу чутливість до старіння листя, ніж інші ВІ.…”

Section: рис 8 відношення між початковими і кінцевими значеннями ві рослинного покриву ярої пшениці сортуunclassified

Identifying vegetation indices as the rational spectral indicators of vegetation state under conditions of laboratory experiment

Lyalko

Zholobak

Dugin

et al. 2021

UJRS

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For the vegetation classes, which cover the ground surface with a rather small area and studied by means of the ground-based remote sensors it’s necessary to select the suitable spectral indices, which cloud responded in a fast and effective way to dynamic environmental conditions induced by the different stress factors. The vegetation indices (VIs) can be such indicators calculated by the mathematical operations using reflectances in the different spectral ranges measured by the field remote sensors, i.e. spectroradiometers. Application of VIs allows identifying these changes of vegetation state, which aren’t visible at the visual observation. In order to select these VIs we have conducted the laboratory experiment with the cultivation of durum spring wheat variety “Diana” at the different higher seeding rates to provide by this way, first, the fast 100% plant cover, and, second, establish the stressed conditions for the plants. During this experiment the gasometric and spectrometric observation of the constantly growing phytomass carried out, when the measurements of intensity of СО2 absorption and release by the plants in process of photosynthesis/respiration and spectrometric ones have been performed practically simultaneously that allowed further calculation of VIs. Three vegetation indices such as MTCI, Clrededge and Clgreen were identified as the most sensitive to the changes of vegetation state and, thus, they can serve as the proper spectral indicators of vegetation condition, which are extremely necessary to develop the technique of estimating the variables of carbon cycle in the different ecosystems using satellite data and field measurements, improve of assessment for the climatic effects at the regional and local levels and estimating the IAEG-SDGs indicators of sustainable development.

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Geographic and Climatic Attributions of Autumn Land Surface Phenology Spatial Patterns in the Temperate Deciduous Broadleaf Forest of China

Cited by 11 publications

References 42 publications

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Identifying vegetation indices as the rational spectral indicators of vegetation state under conditions of laboratory experiment

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