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Optimized angles of the swing hyperspectral imaging system for single corn plant
摘要： During recent years, hyperspectral imaging systems have been widely applied in the greenhouses for plant phenotyping purposes. Current systems are typically designed as either top view or side view imaging mode. Top view is an ideal imaging angle for top leaves with flat leaf surfaces. However, most bottom leaves are either blocked or shaded. From side view, the entire plant structure is viewable. However, most leaf surfaces are not facing the camera, which impacts measurement quality. Besides, there could be advantages with certain tilted angle(s) between top view and side view. It’s interesting to explore the impact of different imaging angles to the phenotyping quality. For this purpose, a swing hyperspectral imaging system capable of capturing images at any angle from side view (0°) to top view (90°) by rotating the camera and the lighting source was designed. Corn plants were grown and allocated into 3 different treatments: high nitrogen (N) and well-watered (control), high N and drought-stressed, and low N and well-watered. Each plant was imaged at 7 different angles from 0° to 90° with an interval of 15°. The soil plant analysis development (SPAD) values and relative water content (RWC) ground truth measurements were used to establish treatment effects. The results showed that averaged plant-level Normalized Difference Vegetation Index (NDVI) values of plants in different treatments changed at different imaging angles. The results also indicated that for pixel-level NDVI distributions, the titled imaging angle of 75° was optimal to distinguish different water treatments, whereas, the tilted imaging angle of 15° was optimal to distinguish different N treatments. For pixel-level RWC distributions, the distribution difference between different water treatments was larger at higher imaging angles.
关键词： Pixel-level NDVI and RWC distributions,Optimal imaging angle,Swing hyperspectral imaging system,Plant phenotyping system,Tilted imaging angle
更新于2025-09-23 15:22:29
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Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2
摘要： Maps of cropping practice, including the level of weed infestation, are useful planning tools e.g. for the assessment of the environmental impact of the crops, and Northern Italy is an important example due to the large and diverse agricultural production and the high weed infestation. Sentinel-2A is a new satellite with a high spatial and temporal resolution which potentially allows the creation of detailed maps of cropping practice including weed infestation. To explore the applicability of Sentinel-2A for mapping cropping practice, we analysed the Normalised Differential Vegetation Index (NDVI) time series from five weed-infested crop fields as well as the areas designated as non-irrigated agricultural land in Corine Land Cover, which also contributed to an increased understanding of the cropping practice in the region. The analysis of the case studies showed that the temporal resolution of Sentinel-2A was high enough to distinguish the gross features of the cropping practice, and that high weed infestations can be detected at this spatial resolution. The analysis of the entire region showed the potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is to some extent applicable for mapping cropping practice with reasonable thematic accuracy.
关键词： Clustering,Phenology,Weed infestation,NDVI,Time-series analysis
更新于2025-09-23 15:22:29
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Rela??es empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5
摘要： The objective of this work was to adjust models to estimate dendrometric characteristics of the Brazilian dry tropical forest (Caatinga) from Landsat 5 TM sensor data. Measures for tree diameter and height were taken in 60 inventory plots (400 m2), in two municipalities of the state of Sergipe, Brazil. Basal area and wood volume were estimated using an allometric equation and form factor (f = 0.9). Explanatory variables were taken from the TM sensor, after radiometric and geometric correction, having considered, in the analysis, six spectral bands, with 30 m spatial resolution, besides the indexes of simple ratio (SR), of normalized difference vegetation (NDVI), and of soil-adjusted vegetation (Savi). To choose the best explanatory variables, the coefficient of determination (R2), the root mean square error (RMSE), and the Bayesian information criterion (BIC) were considered. The basal area per hectare did not show a significant correlation with any of the explanatory variables used. The best models were adjusted to tree mean height per plot (R2 = 0.4; RMSE = 13%) and to wood volume per hectare (R2 = 0.6; RMSE = 42%). The metrics derived from the Landsat 5 TM sensor have great potential to explain variation in the mean height of trees and in the wood volume per hectare, in remaining areas of the tropical dry forest in the Brazilian Northeast.
关键词： Savi,REDD,vegetation index,reducing emissions,remote sensing,NDVI
更新于2025-09-23 15:22:29
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Monitoring of Sheep Migration in Arid Region of Rajasthan, India Using EO Data
摘要： The annual aggregate spatially normal rainfall is extremely variable and most erratic in the western arid region of Rajasthan state. As a result, it frequently experiences spells of drought. Surface water resources are also meagre and distributed unevenly. Drought is a multi-dimensional phenomenon and its direct impacts include like withering of crops, drying of watering points, reduction in fodder for livestock, etc. Such crisis eventually compels Rebari pastoralists to migrate to other places and regions as a coping mechanism against the scarcity of fodder and water in the arid zone of Rajasthan. The scarcity of fodder at any time is a function of stocking rate and carrying capacity of the system at that time, which is affected mainly by the amount of precipitation and livestock population. This has been covered through analysing migration routes and determinants by using the data collected from the ATS plus GPS Collars. It has been found that biomass density changed following grazing across gradients and ground cover. Also, normalized difference vegetation index (NDVI) was 5-10% lower inside the grazing area than outside the grazing area.
关键词： Grazing lands,NDVI,Land degradation,ATS plus global positioning system (GPS) collars,Migration,Small ruminants
更新于2025-09-23 15:21:01
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Enhancement of Ecological Field Experimental Research by Means of UAV Multispectral Sensing
摘要： Although many climate research experiments are providing valuable data, long-term measurements are not always affordable. In the last decades, several facilities have secured long-term experiments, but few studies have incorporated spatial and scale effects. Most of them have been implemented in experimental agricultural fields but none for ecological studies. Scale effects can be assessed using remote sensing images from space or airborne platforms. Unmanned aerial vehicles (UAVs) are contributing to an increased spatial resolution, as well as becoming the intermediate scale between ground measurements and satellite/airborne image data. In this paper we assess the applicability of UAV-borne multispectral images to provide complementary experimental data collected at point scale (field sampling) in a long-term rain manipulation experiment located at the Kiskun Long-Term Socio-Ecological Research (LTSER) site named ExDRain to assess the effects on grassland vegetation. Two multispectral sensors were compared at different scales, the Parrot Sequoia camera on board a UAV and the portable Cropscan spectroradiometer. The NDVI values were used to assess the effect of plastic roofs and a proportional reduction effect was found for Sequoia-derived NDVI values. Acceptable and significant positive relationships were found between both sensors at different scales, being stronger at Cropscan measurement scale. Differences found at plot scale might be due to heterogeneous responses to treatments. Spatial variability analysis pointed out a more homogeneous response for plots submitted to severe and moderate drought. More investigation is needed to address the possible effect of species abundance on NDVI at plot scale contributing to a more consistent representation of ground measurements. The feasibility of carrying out systematic UAV flights coincident or close to ground campaigns will certainly reveal the consistency of the observed spatial patterns in the long run.
关键词： drought,NDVI,multiscale approach,field experiments,LTSER,Sequoia,unmanned aerial vehicles (UAVs)
更新于2025-09-19 17:15:36
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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) - Assessment of Monitoring Regional Cropping System with Temporal Extraction Model Based on GF-1/WFV Imagery
摘要： Obtaining the information on the cropping system in a region accurately and timely is important for optimizing the regional agricultural resource allocation and crop layout. However, there are still technical bottlenecks such as poor spatial resolution, low precision and the lack of imagery in the application of remote sensing research in the spatial and temporal distribution of the cropping system. In this study, a more accurate remote sensing monitoring method for the regional cropping system was proposed. The imagery with wide field of view (WFV) of multi-temporal GF-1 satellite was used to construct a temporal extraction model of the cropping system, which was based on normalized difference vegetation index (NDVI). Using the method proposed in this paper, remote sensing monitoring of the main farming system in Suqian City, Jiangsu Province was carried out, which could provide reference for the extraction of the crop system in southern cloud and rain regions. The results showed that the whole crop development period of the main planting system (rice-winter wheat and winter wheat-summer maize) in the study area was covered by selecting the high time density GF-1 / WFV with 16 m resolution remote sensing imagery acquired from 2016 the complementarity of the multi-temporal imagery avoided the imagery loss caused by the cloudy climate in the middle and lower reaches of the Yangtze River in China. By building a mask and marking the "polluted" areas of the cloud, the interference of cloud imagery on crop information extraction was reduced effectively. In addition, the decision condition was optimized several times by the human-computer interaction, and the key parameter was determined to ensure the accuracy of the temporal extraction model. According to the result of monitoring the major cropping system in Suqian City, the overall classification accuracy was 93.56%, Kappa coefficient was 0.85 and the relative margin of error for individual crops was 7.53%, which met the accuracy requirements for application of agricultural achievements. These results showed that, in comparison with previous remote sensing methods, the method proposed in this study can monitor the regional main crop planting system accurately and can be used to monitor the cropping system based on the high-resolution imagery in multiple ripening areas. In this way, this approach will provide theoretical basis and technical support for the development of the precision agriculture, the optimization of regional cropping patterns and the efficient utilization of agricultural resources.
关键词： condition monitoring,NDVI,cropping system,GF-1/WFV imagery
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 - Investigation of Natural Ecological Enviroment Using Remote Sensing Based Integrated Index at a City Scale
摘要： It is of great importance to monitor and evaluate the dynamics of ecological environment due to severe human activities. In our study, three remote sensing based ecological factors were selected to generate an integrated index for estimating the natural ecological environment at a city scale, specifically including vegetation coverage, soil index and slope. Vegetation coverage was derived from Normalized Difference Vegetation Index (NDVI) of Landsat 8 OLI (Operational Land Imager) imagery. Soil index was generated by the greenness above bare soil (GRABS). Slope was derived from 30 meters resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). An integrated index with the method of weighting average was obtained by normalizing the three indicators. The results show that Hefei has a relatively good ecological environment. The ratio of excellent and good levels accounts for 71.00% of the study area. In general, the ecological environment is better in the southern part than that in the northern part.
关键词： NDVI,Integrated index,Remote sensing,Landsat 8 OLI,Ecological environment
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 - Automatic Derivation of Cropland Phenological Parameters by Adaptive Non-Parametric Regression of Sentinel-2 Ndvi Time Series
摘要： Satellite Image Time Series (SITS), such as the ones acquired by the new Sentinel-2 (S2), combine a large amount of information compared to previous satellite generations since a better trade-off in terms of spatial/spectral/temporal resolutions is guaranteed. The specific characteristic of acquiring images under overlapped orbits, offered by S2, results in: i) availability of irregularly sampled acquisitions and ii) increase of the probability to acquire cloud free images over time. This characteristic becomes relevant in the agricultural analysis, where availability of dense SITS is required to map and analyze fast working crop behaviors. In the literature, several methods exist that extract phenological parameters for agricultural analysis, but none of them is able to deal with irregularly sampled data. Thus, this paper presents an approach for derivation of cropland phenological parameters from irregularly sampled S2-SITS. Experimental results obtained on S2-SITS acquired over Barrax, Spain, confirm the effectiveness of the proposed approach.
关键词： Sentinel-2,Non-parametric regression,NDVI SITS,Vegetation phenology,Data smoothing
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 - Development of Fusion Approach for Estimation of Vegetation Fraction Cover with Drone and Sentinel-2 Data
摘要： Fractional vegetation cover (FVC) is usually referred to as an important parameter for vegetation health monitoring and also used as control parameter in terrestrial ecosystem change detection. In recent year several models have been developed for FVC measurement using satellite data and digital images at regional and global scale. For the validation and modification in these models need to a precise ground truth information. FVC measured using digital camera act as an efficient ground truth information, but it is also lack in accuracy due to limited number of images and sampling points are possible to take with camera. Drone is the recent trend for precision agriculture monitoring and can be used as substitute to overcome these problems. In this paper an efficient method of ground truth FVC measurement using drone image is developed, which is further used for development of a sigmoid model to measure FVC using Sentinel-2 data for larger area. Results of the obtained model are compared with ground truth FVC and obtained value of RMSE is 0.10. FVC are also measured with dimidiate pixel model and obtained RMSE value with ground truth FVC is 0.17. Results show that developed model can be used for efficient measurement of fractional vegetation cover.
关键词： vegetation index,drone,NDVI,Dimidiate pixel model,FVC,Sentinel-2
更新于2025-09-10 09:29:36
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Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8 – A case study in Quito, Ecuador
摘要： The Andean region has a high cloud density throughout the year. The use of optical remote sensing data in the computation of environmental indices of this region has been hampered by the presence of clouds. To maximize accuracy in the computation of several environmental indices including the normalized difference vegetation index (NDVI), we compared the performance of two algorithms in removing clouds in Landsat-8 Operational Land Imager (OLI) data of a high-elevation area. The study area was Quito, Ecuador, which is a city located close to the equator and in a high-elevation area crossed by the Andes Mountains. The first algorithm was the automatic cloud removal method (ACRM), which employs a linear regression between the different spectral bands and the cirrus band. The second algorithm was independent component analysis (ICA), which considers the noise (clouds) as part of independent components applied over the study area. These methods were evaluated based on several images from different years with different cloud conditions. The results indicate that neither algorithm is effective over this region for the removal of clouds or for NDVI computation. However, after improving ACRM, the NDVI computed using ACRM showed a better correlation than ICA with the MODIS NDVI product.
关键词： Quito,optical remote sensing,cloud removal,NDVI,Landsat-8 OLI
更新于2025-09-10 09:29:36