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Remote sensing bio-control damage on aquatic invasive alien plant species
摘要： Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes recommendations to guide future research to successfully detect and map bio-control damage on AIAP species.
关键词： machine learning algorithms,multispectral sensors,chlorophyll content,Aquatic Invasive Alien Plant (AIAP) species,chlorophyll fluorescence,hyperspectral sensors,Remote sensing,bio-control damage
更新于2025-09-23 15:19:57
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Improved Estimation of Leaf Chlorophyll Content from Non-Noon Reflectance Spectra of Wheat Canopies by Avoiding the Effect of Soil Background
摘要： Crop leaf chlorophyll content (LCC) is a valuable indicator for agronomists to make fertilization recommendation and can be estimated from canopy reflectance spectra. However, the estimation accuracy of LCC is often influenced by soil background. To alleviate the adverse effect of soil background, this study proposed to collect spectral measurements at non-noon (such as 14:00-16:00 local time) hours and evaluated the performance of these spectral measurements with experimental data and radiative transfer model. The results from the wheat experiment conducted at Rugao demonstrated that the canopy spectra measured at non-noon were less sensitive to the soil background compared with those collected at midday (such as 12:00), which improved the estimation accuracy (R2) for LCC from 0.71 to 0.77. A canopy radiative transfer model called 4SAIL-RowCrop was also used to validate the performance and feasibility of the non-noon measurement scheme. One thousand spectra with different combinations of LCC, soil reflectance, and canopy structure were simulated at three observation times (12:00, 14:00 and 16:00). The CIred-edge calculated from the canopy reflectance spectra simulated for 16:00 exhibited a higher correlation to LCC (R2 = 0.76) than that for 12:00 (R2 = 0.43). These consistent findings from experimental and modeled datasets suggested that the effect of soil background can be alleviated and the estimation accuracy of LCC can be improved by determining a proper timing of spectral observation.
关键词： Remote sensing,Wheat,Leaf chlorophyll content,Non-noon observation,Soil Background
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Retrieval of Leaf Chlorophyll Content of Paddy Rice with Extracted Foliar Hyperspectral Imagery
摘要： Retrieving foliar chlorophyll content with canopy spectral data is critical for understanding the growing status and yield estimation of paddy rice. However, given the field of view of rice canopy is a mixture of plant organs (i.e., leaves, stems and spikes) and backgrounds (i.e., water, soil and gaps), spectral signals from the targeted portions were disturbed by other portions, thus result in a limited accuracy for foliar chlorophyll content estimation, particularly when the canopy closure was low. As an advanced remote sensing technology, hyperspectral imaging sensors could collect information from both image and spectral dimensions, which allows identification and separation of different plant portions and backgrounds within the scene. Based on this principle, this paper proposed a method using refined spectrum for retrieving rice foliar chlorophyll content. The experimental data consisted of 58 hyperspectral images of rice canopy that were obtained by a Cubert S185 hyperspectral imager. Besides, corresponding chlorophyll content of rice leaves was measured with a SPAD meter. To retrieve foliar chlorophyll contents with refined spectra, a spectral purification procedure was established. Background of rice images was firstly removed by a decision tree method. Then, rice spikes could be removed according to an object-oriented classification which thus left the portion of rice leaves. Vegetation indices were extracted from the refined leaf spectra and were correlated with foliar chlorophyll content. Comparing with the performance of vegetation indices with original canopy hyperspectral data in retrieving foliar chlorophyll content, results suggested that retrieving accuracy based on refined spectra resulted in a significant improvement. The proposed spectral purification procedure helps to mitigate the background impact by wiping out those non-target spectral signals, which thus substantially improve the retrieval accuracy. Such a method can lay a good foundation for subsequent development of some built-in algorithms for in-situ sensors and UAV remote sensing platforms.
关键词： Chlorophyll Content,Spectral purification,Retrieving Accuracy,Hyperspectral
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): Synergetic Exploitation of the Sentinel-2 Missions
摘要： Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, validation efforts are required. In the past, these activities have been constrained by the need for costly airborne hyperspectral data acquisition, but the Sentinel-2 Multispectral Instrument (MSI) now offers a promising alternative. In this paper, we explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. High retrieval accuracy (RMSE = 0.20 g m-2) was obtained by applying machine learning techniques to Sentinel-2 MSI data, highlighting the valuable information it can provide when used in synergy with Sentinel-3 OLCI data for land product validation.
关键词： validation,Vegetation biophysical variables,Sentinel-2,Sentinel-3,canopy chlorophyll content
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Detecting Rice Blast Disease Using Model Inverted Biochemical Variables from Close-Range Reflectance Imagery of Fresh Leaves
摘要： Rice blast is one of the most devastating crop diseases around the world. Although previous remote sensing studies have examined the spectral variation at leaf and canopy levels in response to disease severity levels, the non-imaging nature of their data makes it difficult to examine the spectral variation related to the disease within a leaf. This study proposes to monitor the spatial and temporal pattern of rice leaf blast on individual leaves with close-range imaging spectroscopy data. Hyperspectral images were acquired from diseased leaves at different infection stages. The image data were converted to reflectance cubes and then processed with a model inversion algorithm PROCWT to retrieve leaf biochemical variables. The biochemical maps were examined to investigate the within-leaf spatial variation and leaf-level temporal variation. Preliminary results demonstrated that the PROCWT algorithm could perform on reflectance image cubes. The retrieved chlorophyll maps exhibited a decline with infection stage and significant within-leaf spatial patterns in response to the disease.
关键词： Chlorophyll content,Hyperspectral imagery,Rice blast disease,PROCWT
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - BRDF Effect on the Estimation of Canopy Chlorophyll Content in Paddy Rice from UAV-Based Hyperspectral Imagery
摘要： The bidirectional reflectance distribution function (BRDF) effect due to the surface reflectance anisotropy and variations in the solar and viewing geometry has been studied in the remote sensing community for several decades, and most attention was paid to the satellite sensors with large field of view (FOV), such as MODIS with a 110° FOV. With the development of unmanned aerial vehicle (UAV) technique, the imagery acquired at UAV platform provides important information about crop growth status, which is a promising and efficient approach for precise agriculture. However, few studies explored the BRDF effect in UAV images, especially for the sensors with small FOVs. This study investigated the BRDF effect on the estimation of canopy chlorophyll content (CCC) with the UHD 185 hyperspectral imagery (27° FOV) acquired at a UAV platform. Our results from a rice field-plot experiment demonstrated that the CCC was highly correlated to the red-edge chlorophyll index derived at five different view angles. However, the regression models were significantly different among these view angles. This implied that no single CCC estimation model can be applied to the whole image for CCC mapping. The findings suggest the BRDF effect should be considered for providing reliable and consistent CCC estimation.
关键词： Chlorophyll content,BRDF,Hyperspectral imagery,Paddy rice
更新于2025-09-09 09:28:46
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Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model
摘要： The estimation of leaf biochemical constituents is of high interest for the physiological and ecological applications of remote sensing. The multispectral lidar (MSL) system emerges as a promising active remote sensing technology with the ability to acquire both three-dimensional and spectral characteristics of targets. The detection wavelengths of the MSL system can be geared toward the specific application purposes. Therefore, it’s important to conduct the wavelength selection work to maximize the potential of the MSL system in vegetation monitoring. Traditional strategies of wavelength selection attempt to establish an empirical relationship between large quantities of observed reflectance and foliar biochemical constituents. By contrast, this study proposed to select wavelengths through the radiative transfer model PROSPECT. A five-wavelength combination was established to estimate leaf chlorophyll and water contents: 680, 716, 1104, 1882 and 1920 nm. The consistency of the wavelengths selected were tested by running different versions of PROSPECT model. Model inversion using simulated and experimental datasets showed that the selected wavelengths have the ability to retrieve leaf chlorophyll and water contents accurately. Overall, this study demonstrated the potential of the MSL system in vegetation monitoring and can serve as a guide in the design of new MSL systems for the application community.
关键词： Multispectral lidar,Wavelength selection,Leaf water content,Leaf chlorophyll content,PROSPECT model
更新于2025-09-04 15:30:14
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Effects of UV-C radiation on common dandelion and purple coneflower: First results
摘要： Ultraviolet-C (UV-C) light (100 ≤ λ ≤ 280 nm) is a ionizing radiation that can damage living organisms. An experiment was conducted on plants of common dandelion (Taraxacum officinale Weber, T. Densleonis Desf.) and purple coneflower [Echinacea purpurea, (L.) Moench] irradiated with UV-C at different exposition times, under controlled conditions and grown in self-produced characterized compost, to assess the effect of different doses UV-C radiation on some physiological parameters. Trials have been carried out using a black chamber equipped with an UV-C lamp in which plants were divided in four groups on the basis of UV-C irradiation period (10, 30, 60, and 120 min). Non-irradiated plants were kept as controls. Plant photosynthetic performance, chlorophyll content (SPAD) and some morphologic traits were recorded before, immediately after irradiations and 20 days weeks later. The effects on photosynthetic performances and chlorophyll contents (SPAD) were evaluated and compared with data obtained in similar experiments where tomato plants were irradiated at different times with UV-C light. In both species, SPAD values decreased as the irradiation period became longer. The two species showed different gas exchange dynamics, depending on the UV-C exposure time. Two months after the UV-C irradiation, plant dry weight measured at 120-min UV-C exposure was significantly lower than the control.
关键词： photosynthesis,dry matter,gas exchange,chlorophyll content,ozone layer.
更新于2025-09-04 15:30:14