Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

Quality Prediction of DWT-Based Compression for Remote Sensing Image Using Multiscale and Multilevel Differences Assessment Metric

Accurate assessment and prediction of visual quality are of fundamental importance to lossy compression of remote sensing image, since it is not only a basic indicator of coding performance, but also an important guide to optimize the coding procedure. In the paper, a novel quality prediction model based on multiscale and multilevel distortion (MSMLD) assessment metric is preferred for DWT-based coding of remote sensing image. Firstly, we propose an image quality assessment metric named MSMLD, which assesses quality by calculating distortions in three levels and multiscale sampling between original images and compressed images. The MSMLD method not only has a better consistency with subjective perception values, but also shows the distortion features and visual quality of compressed image well. Secondly, some significant characteristics in spatial and wavelet domain that link well with quality criteria of MSMLD are chosen with multiple linear regression and used to establish a compression quality prediction model of MSMLD. Finally, the quality prediction model is extended to a wider range of compression ratios from 4 : 1 to 20 : 1 and tested with experiment. The experimental results show that the prediction accuracy of the proposed model is up to 98.33%, and its mean prediction error is less than state-of-the-art methods

Normalizing and Converting Image DC Data Using Scatter Plot Matching

Remote sensing image data from sources such as Landsat or airborne multispectral digital cameras are typically in the form of digital count (DC) values. To compare images acquired by the same sensor system on different dates, or images acquired by different sensor systems, it is necessary to correct for differences in the DC values due to sensor characteristics (gain and offset), illumination of the surface (a function of sun angle), and atmospheric clarity. A method is described for normalizing one image to another, or converting image DC values to surface reflectance. This method is based on the identification of pseudo-invariant features (bare soil line and full canopy point) in the scatter plot of red and near-infrared image pixel values. The method, called “scatter plot matching” (SPM), is demonstrated by normalizing a Landsat-7 ETM+ image to a Landsat-5 TM image, and by converting the pixel DC values in a Landsat-5 TM image to values of surface reflectance. While SPM has some limitations, it represents a simple, straight-forward method for calibrating remote sensing image data

Normalizing and Converting Image DC Data Using Scatter Plot Matching

Remote sensing image data from sources such as Landsat or airborne multispectral digital cameras are typically in the form of digital count (DC) values. To compare images acquired by the same sensor system on different dates, or images acquired by different sensor systems, it is necessary to correct for differences in the DC values due to sensor characteristics (gain and offset), illumination of the surface (a function of sun angle), and atmospheric clarity. A method is described for normalizing one image to another, or converting image DC values to surface reflectance. This method is based on the identification of pseudo-invariant features (bare soil line and full canopy point) in the scatter plot of red and near-infrared image pixel values. The method, called “scatter plot matching” (SPM), is demonstrated by normalizing a Landsat-7 ETM+ image to a Landsat-5 TM image, and by converting the pixel DC values in a Landsat-5 TM image to values of surface reflectance. While SPM has some limitations, it represents a simple, straight-forward method for calibrating remote sensing image data