CropPhenology: Extract phenologic metrics from timeseries vegetation index...

In SofanitAraya/CropPhenology: Extract phenologic metrics from timeseries vegetation index data

Description

Extract phenological metrics from time series vegetation index data

Details

Introduction

Multi temporal vegetation index data can be used to get information on seasonal vegetation growth dynamics. This information indicates vegetation phenological growth stages and conditions of environmental factors influencing the vegetation growth. In cropping regions the crop growth dynamics observed from multi temporal vegetation index data has been used in applications such as crop type detection (Zhong et.al. 2011, Roerink et.al. 2011), regional crop yield estimation (Hill et.al. 2003) and many more related studies. Moreover, the long term vegetation dynamics can provide information about influential environmental factors such as soil property mapping (Araya et,al. 2016). Plotting a time series of vegetation index values across time creates a curve that summarises the vegetation dynamics (Figure 1). Extraction of seasonal parameters is an essential step for analysing such vegetation dynamics curve. CropPhenology package has been developed to extract phenological parameters from time series vegetation index data in cropping regions.

Overview of data processing

CropPhenology has three functions: PhenoMetrics, SinglePhenology and MultiPointsPlot.

SinglePhenology:- provides phenological metrics for a single pixel. It allows to implement other preprocessing steps on the vegetation index curve, such as smoothing. The following example illustrate the use of Savitzky-Golay smoothing on the single pixed prior to the phenological extraction.

> install.packages(c("signal", "devtools")) > lapply(c("signal", "devtools"), library, character = TRUE) > # install CropPhenology > install_github("SofanitAraya/CropPhenology") > # NDVI test data for location F1 > MODIS_2015<-c(0.15, 0.1488, 0.152, 0.1526, 0.1699, 0.1634, 0.1647, 0.1693, 0.1899 ,0.2369 ,0.2594, 0.2274, 0.2382, 0.2168, 0.2033, 0.198, 0.183, 0.183, 0.1797, 0.1635, 0.1468, 0.1621, 0.1487) > # Savitzky-Golay smoothing filter > sg_MODIS2015 <- sgolayfilt(MODIS_2015) > ts.plot(ts(MODIS_2015), ts(sg_MODIS2015), col=1:2, lwd=2) > # Apply CropPheno to smoothed NDVI time series > SinglePhenology(sg_MODIS2015)) > ts.plot(ts(MODIS_2015), ts(sg_MODIS2015), col=1:2, lwd=2)

PhenoMetrics:- takes a RasterStack of vegetation index images and a spatialPolygon, extent or SpatialPoints object that defines the Region of Interest (ROI). This function extracts fifteen phenological metrics which represent the seasonal growth condition of the crop at each pixel for the season (Figure 2). The output is presented as a RasterStack of phenological metrics. Table 1 summaises the defined metrics and their descriptions.

MultiPointsPlot:- provides the user with the ability to visualise the NDVI curve by plotting the temporal sequences of NDVI values of user selected raster pixels (maximum of five). This allows the user to observe the spatial and temporal differences in relative dynamics of the vegetation index for the selected points. Figure 3 shows example for the output of MultiPointsPlot.

References

Araya, S., Lyle, G., Lewis, M., and Ostendorf, B. 2016. Phenologic metrics derived from MODIS NDVI as indicators for Plant Available Water-holding Capacity. Ecological Indicators 60:1263-1272. http://dx.doi.org/10.1016/j.ecolind.2015.09.012 Hill, M. J. and Donald, G. E. 2003. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote Sensing of Environment 84:367-384. DOI:10.1016/s0034-4257(02)00128-1 Roerink, G. J., Danes, M. H. G. I., Prieto, O. G., De Wit, A. J. W., and Van Vliet, A. J. H. 2011. Deriving plant phenology from remote sensing. in 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Trento, Italy, Pages 261-264 Zhong, L., Hawkins, T., Biging, G., Gong, P., 2011. A phenology-based approach to map crop types in the San Joaquin Valley, California.International Journal of Remote Sensing, 32, 7777-7804.

SofanitAraya/CropPhenology documentation built on May 9, 2019, 1:51 p.m.