Analysis of Cross-Seasonal Spectral Response from Kettle Holes: Application of Remote Sensing Techniques for Chlorophyll Estimation

Igamberdiev, Rahmatulla M.; Bill, Ralf; Schubert, Hendrik; Lennartz, Bernd

doi:10.3390/rs4113481

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…In recent reports, there is a primary concern with using spectroscopy in crop growth monitoring because of the principle of light absorption by molecular or chemical bonding [ 9 , 10 ]. Considerable literature focused on the reflected intensity and absorption location of sensitive wavelengths to detect the chlorophyll or water content during the growth stages [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Liu

Zhao

Qiao

et al. 2020

Sensors

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Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

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Section: Introductionmentioning

confidence: 99%

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Liu

Zhao

Qiao

et al. 2020

Sensors

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“…The greenness density, indicated by vegetation indices (VIs), can be inversed from remote sensing images to quantify vegetation abundance [25,26]. So far, numerous VIs have been proposed and applied to map the spatial extent of vegetation cover and vegetation abundance [27]. Among the VIs, the normalized difference vegetation index (NDVI) is probably the most popular one, which can be found by computing the spectral reflectance of wavelengths at red and near infrared from remotely acquired images [28].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Sha

Ali

Wang

et al. 2018

IJGI

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Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.

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“…At the first level, segmented objects (patches) were classified into Ground and non-Ground (vegetation) objects, which was implemented after computing vegetation indices (VIs), indicators of the green density, from remote sensing images. VIs have also been used to separate vegetation species in many other studies [34]. Out of the VIs, normalized difference vegetation index (NDVI) is probably the most popular one which is calculated by comparing the magnitude of spectral reflectance at wavelengths red and near infrared, given by [35] …”

Section: Image Data Preprocessingmentioning

confidence: 99%

Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

Liu

Zhuo

et al. 2018

Journal of Sensors

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The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km 2 ) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km 2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.

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Analysis of Cross-Seasonal Spectral Response from Kettle Holes: Application of Remote Sensing Techniques for Chlorophyll Estimation

Cited by 10 publications

References 31 publications

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

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