A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data

Ashapure, Akash; Jung, Jinha; Yeom, Junho; Chang, Anjin; Maeda, Murilo M.; Maeda, Andrea; Landivar, Juan

doi:10.1016/j.isprsjprs.2019.04.003

Cited by 42 publications

(32 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data collection and preprocessing was followed from the method of Ashapure et al (2019) [31]. UAS data (both MS and RGB) were collected over the experimental field on a weekly basis.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…Recently, UAS have emerged as an alternate to the satellite, airborne imaging sensors or LiDAR sensors to estimate CC, and this approach is more affordable and could provide higher temporal and spatial resolution [24][25][26][27][28]. UAS-based CC measurements have been efficiently used to estimate LAI [29,30] and have been used as one of the comparison parameters to quantify the difference between various crop management practices throughout the growing season [31]. Moreover, a recent study conducted over maize field indicated that UAS-based CC is significantly correlated with the grain yield [32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

et al. 2019

Self Cite

View full text Add to dashboard Cite

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

show abstract

“…Data collection and preprocessing was followed from the method of Ashapure et al (2019) [31]. UAS data (both MS and RGB) were collected over the experimental field on a weekly basis.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sequential images were stored in Joint Photographic Experts Group (JPEG) format on a memory card. UAV-embedded global navigation satellite system (GNSS) and inertial measurement unit (IMU) equipment provided position and attitude information with relatively low precision [14].…”

Section: Small Uas and Image Acquisitionmentioning

confidence: 99%

“…The rapid development of UAV technology, coupled with lightweight centimeter spatial resolution sensors, is enabling the acquisition of extremely high resolution images with flexible acquisition times. For example, the DJI Phantom 4 Pro (Shenzhen, China) carrying a 20.48-million-pixel optical camera is one of the most popular drone-sensor combinations in recent studies [14]. Robust and accessible algorithms have also been continuously improved to provide high-quality image products, including orthophotos, digital surface models (DSMs), and 3D point clouds.…”

Section: Introductionmentioning

confidence: 99%

Automated Mapping of Typical Cropland Strips in the North China Plain Using Small Unmanned Aircraft Systems (sUAS) Photogrammetry

et al. 2019

View full text Add to dashboard Cite

Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3–8 m in width and 50–300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain.

show abstract

“…Unmanned aerial vehicles (UAVs) deliver spatially very high resolution (VHR) data sets and a 3D point-cloud with a spatial resolution of only a few centimeters [17]. UAV data creates new opportunities in agriculture [18][19][20], e.g., assessment of plant health status [21], water stress [22], management techniques [23], erosion of soils [24], and detection of individual plants [25], among many others. The advantages of VHR data sets compared to common satellite data sets have also been analyzed, e.g., for vegetation indices (VI) [26,27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

2019

View full text Add to dashboard Cite

The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days ( AGDD) were identified: an early temporal window (515-1232 AGDD, 5 May-17 June 2015) with an average accuracy (AA) of 70-75%; a mid-season window (1362 AGDD, 25 June-22 July 2015 with an AA of around 80%; and a late window (2626-3238 AGDD, 21 August-29 September 2015 with an AA of <65%. Therefore, crop separation is most promising in the mid-season window, and an additional NIR band increases the accuracy significantly. However, discrimination of winter crops is most effective in the early window, adding further observational requirements to the first window.

show abstract

A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data

Cited by 42 publications

References 58 publications

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

Automated Mapping of Typical Cropland Strips in the North China Plain Using Small Unmanned Aircraft Systems (sUAS) Photogrammetry

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

Contact Info

Product

Resources

About