Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods

Senthilnath, J.; Kandukuri, Manasa; Dokania, Akanksha; Ramesh, K.

doi:10.1016/j.compag.2017.05.027

Cited by 117 publications

(57 citation statements)

References 26 publications

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“…To date, only a few works have emphasized the benefits of radiometric calibration of multispectral images acquired from narrow bands cameras on UAV platforms. However, the traditional camera with wide bands can only extract the vegetation region [27]. Sun-induced fluorescence (SIF) can be used as an indicator for stress detection in agricultural applications during the growth of crops, particularly during the grain-filling stage when photosynthesis is sensitive to climate factors [28].…”

Section: Introductionmentioning

confidence: 99%

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

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Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

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Section: Introductionmentioning

confidence: 99%

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

et al. 2019

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“…The simplicity and efficiency of the K‐means clustering algorithm have resulted in its application across many disciplines (Bradley and Bradley 1998, Hoffman et al 2008, Kumar et al 2011, Mills et al 2011, Senthilnath et al 2017, Wang et al 2017, Pascucci et al 2018). Because K‐means is independent of location, the algorithm can categorize pixels in a Landsat scene that are not spatially close but belong to the same phenoregion (Kumar et al 2011).…”

Section: Methodsmentioning

confidence: 99%

“…The number of clusters used in the K‐means clustering algorithm determines the number of phenoregions for each study site. There are many unsupervised statistical methods that have been used to determine an ideal number of clusters, such as Bayesian statistics (Senthilnath et al 2017) and the Hierarchical method (Chen et al 2005, Corstanje et al 2016, Grafius et al 2018). The elbow and silhouette methods (Subbalakshmi et al 2015, Rial et al 2017, Scharsich et al 2017, Wang et al 2017 a , b , Pascucci et al 2018) were used in this study because of their ease of interpretation and reasonable processing time.…”

Section: Methodsmentioning

confidence: 99%

Environmental controls on Landsat‐derived phenoregions across an East African megatransect

2020

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Semiarid and savanna‐type (SAST) ecosystems in East Africa have unique plant species compositions and characteristics that make quantifying this biome's seasonality and interannual variability difficult. Phenoregion classification offers a way to use seasonality of vegetation growth to help understand the phenological spatial patterns of complex landscapes. Here, we used Normalized Difference Vegetation Index (NDVI) time series from Landsat 8 to map phenoregions in scenes centered around national parks from Mt. Kenya National Park (Kenya) to Limpopo National Park (Mozambique). We then assessed whether landscape‐scale controls on phenology are consistent across the region or whether they vary across this latitudinal gradient. We compared our phenoregion maps to MODIS Land Cover and geology, and we used multinomial logistic regression to determine the role that elevation, slope, aspect, and geology play in driving phenological differences. The sites' phenoregions showed no unique land cover composition, suggesting that MODIS land cover does not capture the subtle variations identified in phenoregion analysis. Multinomial logistic regression showed that geographic trend (x‐ and y‐directions) was a strong predictor in four of the five landscapes and that, depending on the scene, geology, elevation, or aspect was a strong secondary predictor. Using seasonality of the NDVI time series to generate phenoregions provides different and, in some cases, more ecologically relevant information, compared to vegetation maps that use only land cover from a single season or time period to generate ecoregions. With a significant population of humans and animals that live in and depend on SAST ecosystems, it is important to better understand vegetation processes and factors that affect them as climate change becomes an increasingly pertinent issue in dry systems.

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“…In recent years, the application field of unmanned aerial vehicles system (UAVs) has been continuously expanded. It has become a new trend to observe water quality characteristics through ground experiments and spectral radiometers by using UAVs equipped with spectral cameras, which has been favored by researchers [21][22][23], as they are rapid, efficient, and flexible spectrum information acquisition systems. Peter et al [24] used UAVs remote sensing to obtain data, analyzed the impact of land leveling in catchment areas on linear soil erosion in ditches, and carried out an effective assessment of water and soil loss in water areas.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

et al. 2020

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The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.

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Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods

Cited by 117 publications

References 26 publications

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Environmental controls on Landsat‐derived phenoregions across an East African megatransect

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

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