Biomass_intro
Ning Liu
4 July 2016
- 1 dataset
- 2 process
- 2.1 load libraries
- 2.2 load input data
- 2.3 Convert data to dataframe
- 2.4 Calculate number of pixels for each site
- 2.5 Calculate vegetation indices
- 2.6 Merge site inforation to dataframe
- 2.7 Calculate FVC
- 2.8 Merge Biomass and pixel number to dataframe
- 2.9 Regression relationship between AGB and VIs
- 2.10 Slope_TEST for relationship between different density
- 2.11 compare between different time images
- 2.12 Statistic for all site
- 3 Write result
This is an introduction file for paper about Wickepin biomass process.
1 dataset
original images in “24_03-11” and “28_09-10”
original images were atmosphericed by Quick atmospheric method in ENVI
for this study the data was cliped to sites area (Folder “data/”)
the images were classificated by Ecognition 8.4 (Folder “yikang/”)
all the result files are saved in (Folder “results/”)
all the result images are saved in (Folder “images/”)
2 process
2.1 load libraries
library(caTools)
library(raster)
#library(ncdf)
library(plyr)
library(ggplot2)
library(lattice)
library(reshape2)
library(hydroGOF)
library(Hmisc)2.2 load input data
data_11<-read.ENVI("data/W_24_03-11-AC")
class_11<-read.ENVI("yikang/class_saltbush_dissolve") # vegetation classifation result based on ecognition
data_10<-read.ENVI("data/W_28_09-10-AC")
site_buf<-read.ENVI("data/W_Site_buffer_round") # site boundary
AGB<-read.csv("data/AGB.csv",header = T) # biomass data
vars<-c("BLUE","GREEN","RED","NIR", "NDVI","RVI","SAVI","GCC","EG","FVC","Rveg")
# c("BLUE","GREEN","RED","NIR", "NDVI","mNDVI","RVI","REEI","EVI", "SAVI","MSAVI","OSAVI","mARI1","mARI2","FVC","Rveg")2.3 Convert data to dataframe
data_11_f<-data.frame(ID=c(1:(nrow(data_11)*ncol(data_11))),BLUE=as.integer(data_11[,,1]),GREEN=as.integer(data_11[,,2]),RED=as.integer(data_11[,,3]),NIR=as.integer(data_11[,,4]))
summary(data_11_f)## ID BLUE GREEN RED
## Min. : 1 Min. : 162 Min. : 97 Min. : 67
## 1st Qu.: 257078 1st Qu.:1162 1st Qu.:1397 1st Qu.:1730
## Median : 514156 Median :1388 Median :1611 Median :1986
## Mean : 514156 Mean :1429 Mean :1605 Mean :1973
## 3rd Qu.: 771233 3rd Qu.:1634 3rd Qu.:1861 3rd Qu.:2264
## Max. :1028310 Max. :4620 Max. :2749 Max. :3532
## NIR
## Min. : 278
## 1st Qu.:2248
## Median :2484
## Mean :2538
## 3rd Qu.:2787
## Max. :5725data_10_f<-data.frame(ID=c(1:(nrow(data_10)*ncol(data_10))),BLUE=as.integer(data_10[,,1]),GREEN=as.integer(data_10[,,2]),RED=as.integer(data_10[,,3]),NIR=as.integer(data_10[,,4]))
summary(data_10_f)## ID BLUE GREEN RED
## Min. : 1 Min. : 190 Min. : 283 Min. : 300
## 1st Qu.: 257078 1st Qu.: 750 1st Qu.:1116 1st Qu.:1214
## Median : 514156 Median : 896 Median :1297 Median :1424
## Mean : 514156 Mean :1043 Mean :1362 Mean :1591
## 3rd Qu.: 771233 3rd Qu.:1258 3rd Qu.:1725 3rd Qu.:1983
## Max. :1028310 Max. :3007 Max. :2300 Max. :3006
## NIR
## Min. : 485
## 1st Qu.:1750
## Median :1944
## Mean :2028
## 3rd Qu.:2302
## Max. :4300site_info<-data.frame(ID=c(1:(nrow(site_buf)*ncol(site_buf))),Site=as.integer(site_buf),VEG=as.integer(class_11))
summary(site_info)## ID Site VEG
## Min. : 1 Min. : 0.0 Min. :0.0000
## 1st Qu.: 257078 1st Qu.:255.0 1st Qu.:0.0000
## Median : 514156 Median :255.0 Median :0.0000
## Mean : 514156 Mean :214.1 Mean :0.1917
## 3rd Qu.: 771233 3rd Qu.:255.0 3rd Qu.:0.0000
## Max. :1028310 Max. :255.0 Max. :1.00002.4 Calculate number of pixels for each site
Here the round boundary for each site was used. This boundary was built by using the central point of each site as the Circ+pt and 10cm as the radius.
Therefore, for each site, there is 1250 pixels totally.
The number of pixels for vegetation in each site can be calculated by below code.
.linshi<-melt(site_info[site_info$VEG==1,-3],id=c(2))
N_pixels<-dcast(.linshi,Site~variable,length)
names(N_pixels)[2]<-c("Veg_pixels")2.5 Calculate vegetation indices
2.5.1 data_11
attach(data_11_f)
#---VIs
# NDVI (NIR-RED/NIR+RED)
data_11_f$NDVI<-(NIR-RED)/(NIR+RED)
# mNDVI (NIR-RED/NIR+RED)
data_11_f$mNDVI<-(NIR-RED)/((NIR+RED)-2*BLUE)
#RVI (NIR/RED)
data_11_f$RVI<-(NIR/RED)
data_11_f$REEI<-(RED/NIR)
#EVI (2.5*(NIR-RED)/(NIR+6RED-7.5blue+1))
data_11_f$EVI<-2.5*(NIR-RED)/(NIR+6*RED-7.5*BLUE+1)
#SAVI (1+0.5)(NIR-RED)/(NIR+RED+0.5)
data_11_f$SAVI<-1.5*(NIR-RED)/(NIR+RED+0.5)
# MSAVI
data_11_f$MSAVI<-(2*(NIR+1)-sqrt((2*NIR+1)^2-8*(NIR-RED)))/2
#OSAVI
data_11_f$OSAVI<-(NIR-RED)/(NIR+RED+0.16)
# mARI1
data_11_f$mARI1=(GREEN-1)/(RED-1)
# mARI2
data_11_f$mARI2=NIR*(GREEN-1)/(RED-1)
#GCC
data_11_f$GCC=GREEN/(GREEN+RED+BLUE)
#EG
data_11_f$EG=2*GREEN-RED-BLUE
detach(data_11_f)
summary(data_11_f)## ID BLUE GREEN RED
## Min. : 1 Min. : 162 Min. : 97 Min. : 67
## 1st Qu.: 257078 1st Qu.:1162 1st Qu.:1397 1st Qu.:1730
## Median : 514156 Median :1388 Median :1611 Median :1986
## Mean : 514156 Mean :1429 Mean :1605 Mean :1973
## 3rd Qu.: 771233 3rd Qu.:1634 3rd Qu.:1861 3rd Qu.:2264
## Max. :1028310 Max. :4620 Max. :2749 Max. :3532
## NIR NDVI mNDVI RVI
## Min. : 278 Min. :-0.2821 Min. :-1089.0000 Min. : 0.560
## 1st Qu.:2248 1st Qu.: 0.0830 1st Qu.: 0.2324 1st Qu.: 1.181
## Median :2484 Median : 0.1003 Median : 0.2992 Median : 1.223
## Mean :2538 Mean : 0.1303 Mean : Inf Mean : 1.348
## 3rd Qu.:2787 3rd Qu.: 0.1291 3rd Qu.: 0.3790 3rd Qu.: 1.297
## Max. :5725 Max. : 0.8250 Max. : Inf Max. :10.430
## REEI EVI SAVI MSAVI
## Min. :0.09587 Min. :-4160.000 Min. :-0.4229 Min. :-0.2840
## 1st Qu.:0.77128 1st Qu.: 0.227 1st Qu.: 0.1245 1st Qu.: 0.6533
## Median :0.81776 Median : 0.327 Median : 0.1504 Median : 0.6822
## Mean :0.78012 Mean : Inf Mean : 0.1955 Mean : 0.7198
## 3rd Qu.:0.84672 3rd Qu.: 0.460 3rd Qu.: 0.1937 3rd Qu.: 0.7287
## Max. :1.78571 Max. : Inf Max. : 1.2374 Max. : 1.4041
## OSAVI mARI1 mARI2 GCC
## Min. :-0.2820 Min. :0.2261 Min. : 117.8 Min. :0.1181
## 1st Qu.: 0.0830 1st Qu.:0.7784 1st Qu.:1809.6 1st Qu.:0.3159
## Median : 0.1003 Median :0.8217 Median :2025.9 Median :0.3227
## Mean : 0.1303 Mean :0.8188 Mean :2081.7 Mean :0.3221
## 3rd Qu.: 0.1291 3rd Qu.:0.8548 3rd Qu.:2323.3 3rd Qu.:0.3286
## Max. : 0.8250 Max. :1.9721 Max. :8125.0 Max. :0.5201
## EG
## Min. :-3023.0
## 1st Qu.: -256.0
## Median : -157.0
## Mean : -192.5
## 3rd Qu.: -68.0
## Max. : 1094.02.5.2 data_10
attach(data_10_f)
#---VIs
# NDVI (NIR-RED/NIR+RED)
data_10_f$NDVI<-(NIR-RED)/(NIR+RED)
# mNDVI (NIR-RED/NIR+RED)
data_10_f$mNDVI<-(NIR-RED)/((NIR+RED)-2*BLUE)
#RVI (NIR/RED)
data_10_f$RVI<-(NIR/RED)
data_10_f$REEI<-(RED/NIR)
#EVI (2.5*(NIR-RED)/(NIR+6RED-7.5blue+1))
data_10_f$EVI<-2.5*(NIR-RED)/(NIR+6*RED-7.5*BLUE+1)
#SAVI (1+0.5)(NIR-RED)/(NIR+RED+0.5)
data_10_f$SAVI<-1.5*(NIR-RED)/(NIR+RED+0.5)
# MSAVI
data_10_f$MSAVI<-(2*(NIR+1)-sqrt((2*NIR+1)^2-8*(NIR-RED)))/2
#OSAVI
data_10_f$OSAVI<-(NIR-RED)/(NIR+RED+0.16)
# mARI1
data_10_f$mARI1=(GREEN-1)/(RED-1)
# mARI2
data_10_f$mARI2=NIR*(GREEN-1)/(RED-1)
#GCC
data_10_f$GCC=GREEN/(GREEN+RED+BLUE)
#EG
data_10_f$EG=2*GREEN-RED-BLUE
detach(data_10_f)
summary(data_10_f)## ID BLUE GREEN RED
## Min. : 1 Min. : 190 Min. : 283 Min. : 300
## 1st Qu.: 257078 1st Qu.: 750 1st Qu.:1116 1st Qu.:1214
## Median : 514156 Median : 896 Median :1297 Median :1424
## Mean : 514156 Mean :1043 Mean :1362 Mean :1591
## 3rd Qu.: 771233 3rd Qu.:1258 3rd Qu.:1725 3rd Qu.:1983
## Max. :1028310 Max. :3007 Max. :2300 Max. :3006
## NIR NDVI mNDVI RVI
## Min. : 485 Min. :-0.17037 Min. : -Inf Min. :0.7089
## 1st Qu.:1750 1st Qu.: 0.05948 1st Qu.:0.1602 1st Qu.:1.1265
## Median :1944 Median : 0.13032 Median :0.2925 Median :1.2997
## Mean :2028 Mean : 0.13530 Mean : NaN Mean :1.3604
## 3rd Qu.:2302 3rd Qu.: 0.18077 3rd Qu.:0.3889 3rd Qu.:1.4413
## Max. :4300 Max. : 0.78229 Max. : Inf Max. :8.1866
## REEI EVI SAVI MSAVI
## Min. :0.1222 Min. :-1195.0000 Min. :-0.2555 Min. :0.08963
## 1st Qu.:0.6938 1st Qu.: 0.1472 1st Qu.: 0.0892 1st Qu.:0.61225
## Median :0.7694 Median : 0.2985 Median : 0.1954 Median :0.73054
## Mean :0.7758 Mean : 0.3314 Mean : 0.2029 Mean :0.72418
## 3rd Qu.:0.8877 3rd Qu.: 0.4365 3rd Qu.: 0.2711 3rd Qu.:0.80612
## Max. :1.4107 Max. : 1130.0000 Max. : 1.1733 Max. :1.37783
## OSAVI mARI1 mARI2 GCC
## Min. :-0.17036 Min. :0.3145 Min. : 240.2 Min. :0.1887
## 1st Qu.: 0.05947 1st Qu.:0.7988 1st Qu.:1567.7 1st Qu.:0.3319
## Median : 0.13031 Median :0.8915 Median :1755.2 Median :0.3544
## Mean : 0.13529 Mean :0.8829 Mean :1773.8 Mean :0.3476
## 3rd Qu.: 0.18076 3rd Qu.:0.9518 3rd Qu.:1939.7 3rd Qu.:0.3658
## Max. : 0.78226 Max. :2.3018 Max. :6083.2 Max. :0.5552
## EG
## Min. :-1917.00
## 1st Qu.: -18.00
## Median : 215.00
## Mean : 90.46
## 3rd Qu.: 323.00
## Max. : 1578.002.6 Merge site inforation to dataframe
data_11_f<-cbind(site_info,data_11_f[-1])
data_10_f<-cbind(site_info,data_10_f[-1])
# delete data not in study site and soil pixels
data_11_f$Site[data_11_f$Site>25 |data_11_f$Site<1]<-NA
data_10_f$Site[data_10_f$Site>25 |data_10_f$Site<1]<-NA
#data_11_f$VEG[data_11_f$VEG==0]<-NA
data_11_f<-na.omit(data_11_f)
data_10_f<-na.omit(data_10_f)
summary(data_11_f)## ID Site VEG BLUE
## Min. : 52551 Min. : 1.0 Min. :0.0000 Min. : 288
## 1st Qu.:360487 1st Qu.: 6.0 1st Qu.:0.0000 1st Qu.: 883
## Median :638706 Median :13.0 Median :1.0000 Median :1151
## Mean :574939 Mean :12.5 Mean :0.5352 Mean :1142
## 3rd Qu.:788819 3rd Qu.:19.0 3rd Qu.:1.0000 3rd Qu.:1359
## Max. :996072 Max. :24.0 Max. :1.0000 Max. :2628
## GREEN RED NIR NDVI
## Min. : 239 Min. : 289 Min. : 491 Min. :-0.08606
## 1st Qu.:1067 1st Qu.:1258 1st Qu.:2049 1st Qu.: 0.10940
## Median :1391 Median :1642 Median :2382 Median : 0.16009
## Mean :1362 Mean :1609 Mean :2424 Mean : 0.20334
## 3rd Qu.:1628 3rd Qu.:1919 3rd Qu.:2772 3rd Qu.: 0.26220
## Max. :2490 Max. :3186 Max. :5078 Max. : 0.77447
## mNDVI RVI REEI EVI
## Min. :-39.0000 Min. :0.8415 Min. :0.1271 Min. :-143.1818
## 1st Qu.: 0.3010 1st Qu.:1.2457 1st Qu.:0.5845 1st Qu.: 0.3253
## Median : 0.4007 Median :1.3812 Median :0.7240 Median : 0.4721
## Mean : 0.4422 Mean :1.5997 Mean :0.6787 Mean : 0.6277
## 3rd Qu.: 0.5714 3rd Qu.:1.7108 3rd Qu.:0.8028 3rd Qu.: 0.8004
## Max. : 4.6471 Max. :7.8678 Max. :1.1883 Max. : 270.0000
## SAVI MSAVI OSAVI mARI1
## Min. :-0.1290 Min. :0.3119 Min. :-0.08605 Min. :0.4662
## 1st Qu.: 0.1641 1st Qu.:0.6972 1st Qu.: 0.10940 1st Qu.:0.8021
## Median : 0.2401 Median :0.7760 Median : 0.16009 Median :0.8426
## Mean : 0.3050 Mean :0.8213 Mean : 0.20333 Mean :0.8489
## 3rd Qu.: 0.3932 3rd Qu.:0.9154 3rd Qu.: 0.26219 3rd Qu.:0.8910
## Max. : 1.1615 Max. :1.3729 Max. : 0.77443 Max. :1.4313
## mARI2 GCC EG
## Min. : 299.2 Min. :0.2153 Min. :-1034.00
## 1st Qu.:1700.7 1st Qu.:0.3212 1st Qu.: -152.00
## Median :2015.3 Median :0.3299 Median : -43.00
## Mean :2076.5 Mean :0.3309 Mean : -26.72
## 3rd Qu.:2418.3 3rd Qu.:0.3417 3rd Qu.: 97.00
## Max. :5524.6 Max. :0.4268 Max. : 791.00summary(data_10_f)## ID Site VEG BLUE
## Min. : 52551 Min. : 1.0 Min. :0.0000 Min. : 275.0
## 1st Qu.:360487 1st Qu.: 6.0 1st Qu.:0.0000 1st Qu.: 634.0
## Median :638706 Median :13.0 Median :1.0000 Median : 785.0
## Mean :574939 Mean :12.5 Mean :0.5352 Mean : 799.3
## 3rd Qu.:788819 3rd Qu.:19.0 3rd Qu.:1.0000 3rd Qu.: 914.0
## Max. :996072 Max. :24.0 Max. :1.0000 Max. :2005.0
## GREEN RED NIR NDVI
## Min. : 383 Min. : 400 Min. : 682 Min. :-0.04076
## 1st Qu.: 928 1st Qu.:1027 1st Qu.:1627 1st Qu.: 0.12136
## Median :1168 Median :1267 Median :1867 Median : 0.16111
## Mean :1164 Mean :1263 Mean :1891 Mean : 0.19976
## 3rd Qu.:1352 3rd Qu.:1468 3rd Qu.:2120 3rd Qu.: 0.24244
## Max. :2199 Max. :2679 Max. :3697 Max. : 0.70636
## mNDVI RVI REEI EVI
## Min. :-0.07901 Min. :0.9217 Min. :0.1721 Min. :-8.0980
## 1st Qu.: 0.25741 1st Qu.:1.2762 1st Qu.:0.6097 1st Qu.: 0.2499
## Median : 0.35241 Median :1.3841 Median :0.7225 Median : 0.3802
## Mean : 0.39268 Mean :1.5671 Mean :0.6802 Mean : 0.4939
## 3rd Qu.: 0.50699 3rd Qu.:1.6400 3rd Qu.:0.7835 3rd Qu.: 0.6458
## Max. : 1.23284 Max. :5.8110 Max. :1.0850 Max. : 8.5783
## SAVI MSAVI OSAVI mARI1
## Min. :-0.06112 Min. :0.4151 Min. :-0.04075 Min. :0.4095
## 1st Qu.: 0.18201 1st Qu.:0.7164 1st Qu.: 0.12135 1st Qu.:0.8480
## Median : 0.24163 Median :0.7775 Median : 0.16110 Median :0.9162
## Mean : 0.29959 Mean :0.8197 Mean : 0.19975 Mean :0.9269
## 3rd Qu.: 0.36357 3rd Qu.:0.8902 3rd Qu.: 0.24242 3rd Qu.:1.0027
## Max. : 1.05938 Max. :1.3279 Max. : 0.70632 Max. :1.5722
## mARI2 GCC EG
## Min. : 408.7 Min. :0.2191 Min. :-794.0
## 1st Qu.:1436.1 1st Qu.:0.3476 1st Qu.: 131.0
## Median :1705.4 Median :0.3606 Median : 263.0
## Mean :1771.7 Mean :0.3604 Mean : 265.9
## 3rd Qu.:2063.6 3rd Qu.:0.3750 3rd Qu.: 401.0
## Max. :4853.9 Max. :0.4616 Max. :1236.02.7 Calculate FVC
FVC is derived from NDVI, and to help distinguish vegetation and soil pixels NDVI values at 5% and 95% of the study area were selected as NDVIsoil and NDVIveg, respectively (Wu et al. 2004). Rveg is simply the proportion (%) of vegetation pixels compared to the total number of pixels.
#FVC=(NDVI-NDVIsoil)/(NDVIveg-NDVIsoil)
data_11_f$FVC<-(data_11_f$NDVI-quantile(data_11_f$NDVI, 0.05))/(quantile(data_11_f$NDVI, 0.95)-quantile(data_11_f$NDVI, 0.05))
data_10_f$FVC<-(data_10_f$NDVI-quantile(data_10_f$NDVI, 0.05))/(quantile(data_10_f$NDVI, 0.95)-quantile(data_10_f$NDVI, 0.05))2.8 Merge Biomass and pixel number to dataframe
2.8.1 The whole site
# Get the mean VIs for each site
.linshi<-melt(data_11_f[c(-1,-3)],id=c(1))
site_mean_11<-dcast(.linshi,Site~variable,mean,na.rm=TRUE)
# Merge with ABG and N_pixels which was used to calculate Rveg
site_mean_11<-merge(site_mean_11,AGB,by="Site",all.y=T)
site_mean_11<-merge(site_mean_11,N_pixels,by="Site",all.x=T)
site_mean_11$Rveg<-site_mean_11$Veg_pixels/1250
head(site_mean_11)## Site BLUE GREEN RED NIR NDVI mNDVI RVI
## 1 3 1251.412 1463.091 1720.905 2248.138 0.1323445 0.3556694 1.312914
## 2 4 1150.087 1365.044 1596.349 2173.728 0.1534737 0.3912133 1.378765
## 3 7 1158.619 1416.218 1736.070 2355.169 0.1537679 0.3483549 1.380388
## 4 8 1083.016 1280.325 1515.360 2057.573 0.1496345 0.3783016 1.363077
## 5 11 1088.809 1308.681 1572.494 2159.667 0.1595254 0.3769048 1.399254
## 6 12 1175.474 1422.486 1742.642 2281.863 0.1381761 0.3280308 1.339342
## REEI EVI SAVI MSAVI OSAVI mARI1 mARI2
## 1 0.7693612 0.4357775 0.1984914 0.7306002 0.1323391 0.8502275 1922.480
## 2 0.7395435 0.5215826 0.2301789 0.7604135 0.1534670 0.8576070 1880.175
## 3 0.7393226 0.4101167 0.2306222 0.7606376 0.1537616 0.8150715 1938.100
## 4 0.7436494 0.4705997 0.2244201 0.7563054 0.1496278 0.8441791 1753.005
## 5 0.7309390 0.4698508 0.2392545 0.7690167 0.1595183 0.8333135 1817.213
## 6 0.7634840 0.4023200 0.2072364 0.7364776 0.1381702 0.8194847 1882.473
## GCC EG FVC Density Name Y5 Y6
## 1 0.3292444 -46.13440 0.1382554 HD S2An2HD 11.078032 7.271884
## 2 0.3311611 -16.34800 0.1905260 HD S2An3HD 8.601376 9.932828
## 3 0.3272377 -62.25300 0.1912536 LD S2An1LD 6.580034 10.962102
## 4 0.3290658 -37.72516 0.1810283 HD S2An1HD 15.899325 13.901538
## 5 0.3286287 -43.94099 0.2054970 LD S2An2LD 12.376185 12.227722
## 6 0.3269435 -73.14240 0.1526819 LD S2An3LD 10.612028 5.449810
## Y7 Y8 Y9 Y11 Ct Veg_pixels Rveg
## 1 10.05684 7.695246 9.305107 10.945360 24.512 277 0.2216
## 2 10.64388 10.864941 13.347178 17.544068 39.336 568 0.4544
## 3 11.77028 12.158156 14.532373 14.902354 32.888 500 0.4000
## 4 15.86886 14.635193 17.084150 17.385223 42.503 504 0.4032
## 5 12.45627 12.243385 15.576473 19.018632 41.707 523 0.4184
## 6 5.50925 5.612370 6.121482 8.329726 18.811 307 0.2456summary(site_mean_11)## Site BLUE GREEN RED
## Min. : 3.00 Min. :1083 Min. :1280 Min. :1515
## 1st Qu.: 7.75 1st Qu.:1156 1st Qu.:1363 1st Qu.:1603
## Median :13.00 Median :1213 Median :1443 Median :1739
## Mean :12.75 Mean :1244 Mean :1460 Mean :1727
## 3rd Qu.:16.75 3rd Qu.:1331 3rd Qu.:1533 3rd Qu.:1801
## Max. :23.00 Max. :1470 Max. :1690 Max. :1937
##
## NIR NDVI mNDVI RVI
## Min. :2058 Min. :0.1108 Min. :0.3195 Min. :1.253
## 1st Qu.:2170 1st Qu.:0.1271 1st Qu.:0.3400 1st Qu.:1.300
## Median :2265 Median :0.1353 Median :0.3557 Median :1.326
## Mean :2265 Mean :0.1364 Mean :0.3562 Mean :1.329
## 3rd Qu.:2358 3rd Qu.:0.1506 3rd Qu.:0.3772 3rd Qu.:1.367
## Max. :2484 Max. :0.1595 Max. :0.3912 Max. :1.399
##
## REEI EVI SAVI MSAVI
## Min. :0.7309 Min. :0.3707 Min. :0.1662 Min. :0.6972
## 1st Qu.:0.7426 1st Qu.:0.4082 1st Qu.:0.1906 1st Qu.:0.7218
## Median :0.7664 Median :0.4378 Median :0.2029 Median :0.7335
## Mean :0.7646 Mean :0.4432 Mean :0.2046 Mean :0.7353
## 3rd Qu.:0.7781 3rd Qu.:0.4754 3rd Qu.:0.2259 3rd Qu.:0.7573
## Max. :0.8028 Max. :0.5216 Max. :0.2393 Max. :0.7690
##
## OSAVI mARI1 mARI2 GCC
## Min. :0.1108 Min. :0.8151 Min. :1753 Min. :0.3269
## 1st Qu.:0.1271 1st Qu.:0.8415 1st Qu.:1864 1st Qu.:0.3273
## Median :0.1353 Median :0.8472 Median :1910 Median :0.3288
## Mean :0.1364 Mean :0.8459 Mean :1928 Mean :0.3289
## 3rd Qu.:0.1506 3rd Qu.:0.8577 3rd Qu.:1969 3rd Qu.:0.3304
## Max. :0.1595 Max. :0.8764 Max. :2177 Max. :0.3319
##
## EG FVC Density Name Y5
## Min. :-85.35 Min. :0.0850 HD:6 S1An1HD:1 Min. : 6.144
## 1st Qu.:-64.98 1st Qu.:0.1253 LD:6 S1An1LD:1 1st Qu.: 6.567
## Median :-45.33 Median :0.1455 S1An2HD:1 Median : 9.607
## Mean :-51.35 Mean :0.1483 S1An2LD:1 Mean : 9.760
## 3rd Qu.:-37.46 3rd Qu.:0.1834 S1An3HD:1 3rd Qu.:12.099
## Max. :-16.35 Max. :0.2055 S1An3LD:1 Max. :15.899
## (Other):6
## Y6 Y7 Y8 Y9
## Min. : 4.866 Min. : 5.509 Min. : 4.236 Min. : 5.253
## 1st Qu.: 5.931 1st Qu.: 6.522 1st Qu.: 5.602 1st Qu.: 7.717
## Median : 8.158 Median :10.350 Median : 7.992 Median :10.243
## Mean : 8.480 Mean : 9.598 Mean : 8.527 Mean :10.667
## 3rd Qu.:10.842 3rd Qu.:11.552 3rd Qu.:11.188 3rd Qu.:13.643
## Max. :13.902 Max. :15.869 Max. :14.635 Max. :17.084
##
## Y11 Ct Veg_pixels Rveg
## Min. : 4.314 Min. :14.77 Min. : 87.0 Min. :0.0696
## 1st Qu.: 9.780 1st Qu.:19.19 1st Qu.:273.2 1st Qu.:0.2186
## Median :12.193 Median :24.15 Median :358.0 Median :0.2864
## Mean :12.352 Mean :27.05 Mean :361.7 Mean :0.2893
## 3rd Qu.:15.523 3rd Qu.:34.50 3rd Qu.:501.0 3rd Qu.:0.4008
## Max. :19.019 Max. :42.50 Max. :568.0 Max. :0.4544
## # Get the mean VIs for each site
.linshi<-melt(data_10_f[c(-1,-3)],id=c(1))
site_mean_10<-dcast(.linshi,Site~variable,mean,na.rm=TRUE)
# Merge with ABG and N_pixels which was used to calculate Rveg
site_mean_10<-merge(site_mean_10,AGB,by="Site",all.y=T)
site_mean_10<-merge(site_mean_10,N_pixels,by="Site",all.x=T)
site_mean_10$Rveg<-site_mean_10$Veg_pixels/1250
head(site_mean_10)## Site BLUE GREEN RED NIR NDVI mNDVI RVI
## 1 3 803.8912 1197.510 1382.075 1757.196 0.1192529 0.2468313 1.272213
## 2 4 771.0528 1139.130 1274.610 1696.048 0.1420810 0.3005757 1.334485
## 3 7 846.7462 1237.726 1314.018 1808.871 0.1581124 0.3467331 1.381685
## 4 8 761.3237 1127.792 1302.133 1693.357 0.1309847 0.2702037 1.303843
## 5 11 812.3931 1209.103 1312.376 1723.586 0.1352068 0.2932069 1.318736
## 6 12 844.0008 1218.302 1359.949 1794.336 0.1393924 0.3007693 1.330889
## REEI EVI SAVI MSAVI OSAVI mARI1 mARI2
## 1 0.7875697 0.2408802 0.1788507 0.7123828 0.1192468 0.8659861 1526.202
## 2 0.7524999 0.3171752 0.2130849 0.7474450 0.1420732 0.8942779 1523.785
## 3 0.7291465 0.3881143 0.2371299 0.7707990 0.1581041 0.9414881 1713.778
## 4 0.7693978 0.2722649 0.1964438 0.7305498 0.1309776 0.8661033 1471.682
## 5 0.7641445 0.3115609 0.2027762 0.7358037 0.1351996 0.9223429 1599.170
## 6 0.7580588 0.3238652 0.2090541 0.7418900 0.1393850 0.8956121 1619.722
## GCC EG FVC Density Name Y5 Y6
## 1 0.3531230 209.0528 0.09053319 HD S2An2HD 11.078032 7.271884
## 2 0.3564502 232.5960 0.15363325 HD S2An3HD 8.601376 9.932828
## 3 0.3633497 314.6878 0.19794644 LD S2An1LD 6.580034 10.962102
## 4 0.3521623 192.1266 0.12296169 HD S2An1HD 15.899325 13.901538
## 5 0.3619501 293.4362 0.13463211 LD S2An2LD 12.376185 12.227722
## 6 0.3549057 232.6536 0.14620175 LD S2An3LD 10.612028 5.449810
## Y7 Y8 Y9 Y11 Ct Veg_pixels Rveg
## 1 10.05684 7.695246 9.305107 10.945360 24.512 277 0.2216
## 2 10.64388 10.864941 13.347178 17.544068 39.336 568 0.4544
## 3 11.77028 12.158156 14.532373 14.902354 32.888 500 0.4000
## 4 15.86886 14.635193 17.084150 17.385223 42.503 504 0.4032
## 5 12.45627 12.243385 15.576473 19.018632 41.707 523 0.4184
## 6 5.50925 5.612370 6.121482 8.329726 18.811 307 0.2456summary(site_mean_10)## Site BLUE GREEN RED
## Min. : 3.00 Min. :737.4 Min. :1105 Min. :1117
## 1st Qu.: 7.75 1st Qu.:787.0 1st Qu.:1165 1st Qu.:1295
## Median :13.00 Median :828.2 Median :1214 Median :1336
## Mean :12.75 Mean :822.5 Mean :1211 Mean :1328
## 3rd Qu.:16.75 3rd Qu.:866.0 3rd Qu.:1256 3rd Qu.:1366
## Max. :23.00 Max. :884.6 Max. :1300 Max. :1483
##
## NIR NDVI mNDVI RVI
## Min. :1693 Min. :0.1136 Min. :0.2422 Min. :1.260
## 1st Qu.:1745 1st Qu.:0.1301 1st Qu.:0.2681 1st Qu.:1.302
## Median :1780 Median :0.1374 Median :0.2969 Median :1.325
## Mean :1792 Mean :0.1493 Mean :0.3189 Mean :1.361
## 3rd Qu.:1860 3rd Qu.:0.1597 3rd Qu.:0.3504 3rd Qu.:1.386
## Max. :1897 Max. :0.2238 Max. :0.4620 Max. :1.599
##
## REEI EVI SAVI MSAVI
## Min. :0.6392 Min. :0.2399 Min. :0.1704 Min. :0.7024
## 1st Qu.:0.7266 1st Qu.:0.2713 1st Qu.:0.1952 1st Qu.:0.7289
## Median :0.7602 Median :0.3144 Median :0.2061 Median :0.7397
## Mean :0.7435 Mean :0.3559 Mean :0.2239 Mean :0.7564
## 3rd Qu.:0.7711 3rd Qu.:0.3944 3rd Qu.:0.2396 3rd Qu.:0.7733
## Max. :0.7976 Max. :0.6083 Max. :0.3357 Max. :0.8607
##
## OSAVI mARI1 mARI2 GCC
## Min. :0.1136 Min. :0.8623 Min. :1472 Min. :0.3512
## 1st Qu.:0.1301 1st Qu.:0.8725 1st Qu.:1526 1st Qu.:0.3532
## Median :0.1374 Median :0.9001 Median :1626 Median :0.3585
## Mean :0.1493 Mean :0.9140 Mean :1649 Mean :0.3594
## 3rd Qu.:0.1597 3rd Qu.:0.9379 3rd Qu.:1729 3rd Qu.:0.3623
## Max. :0.2238 Max. :1.0107 Max. :1898 Max. :0.3728
##
## EG FVC Density Name Y5
## Min. :192.1 Min. :0.07502 HD:6 S1An1HD:1 Min. : 6.144
## 1st Qu.:223.1 1st Qu.:0.12059 LD:6 S1An1LD:1 1st Qu.: 6.567
## Median :263.0 Median :0.14070 S1An2HD:1 Median : 9.607
## Mean :271.0 Mean :0.17363 S1An2LD:1 Mean : 9.760
## 3rd Qu.:310.5 3rd Qu.:0.20244 S1An3HD:1 3rd Qu.:12.099
## Max. :390.0 Max. :0.37958 S1An3LD:1 Max. :15.899
## (Other):6
## Y6 Y7 Y8 Y9
## Min. : 4.866 Min. : 5.509 Min. : 4.236 Min. : 5.253
## 1st Qu.: 5.931 1st Qu.: 6.522 1st Qu.: 5.602 1st Qu.: 7.717
## Median : 8.158 Median :10.350 Median : 7.992 Median :10.243
## Mean : 8.480 Mean : 9.598 Mean : 8.527 Mean :10.667
## 3rd Qu.:10.842 3rd Qu.:11.552 3rd Qu.:11.188 3rd Qu.:13.643
## Max. :13.902 Max. :15.869 Max. :14.635 Max. :17.084
##
## Y11 Ct Veg_pixels Rveg
## Min. : 4.314 Min. :14.77 Min. : 87.0 Min. :0.0696
## 1st Qu.: 9.780 1st Qu.:19.19 1st Qu.:273.2 1st Qu.:0.2186
## Median :12.193 Median :24.15 Median :358.0 Median :0.2864
## Mean :12.352 Mean :27.05 Mean :361.7 Mean :0.2893
## 3rd Qu.:15.523 3rd Qu.:34.50 3rd Qu.:501.0 3rd Qu.:0.4008
## Max. :19.019 Max. :42.50 Max. :568.0 Max. :0.4544
## 2.8.2 only veg pixels in each site
As only vegetation pixels were calculated. Therefor the mean VIs for each site was calculated by [sum(VI_veg)/1250]
# Get the sum vegetation's VIs for each site
.linshi<-melt(data_11_f[data_11_f$VEG==1,c(-1,-3)],id=c(1))
site_veg_sum_11<-dcast(.linshi,Site~variable,sum,na.rm=TRUE)
.linshi<-melt(data_10_f[data_10_f$VEG==1,c(-1,-3)],id=c(1))
site_veg_sum_10<-dcast(.linshi,Site~variable,sum,na.rm=TRUE)
# average sum of VIs by dividing the total number of pixels
site_veg_mean_11<-site_veg_sum_11
for(var in vars[-length(vars)]){site_veg_mean_11[[var]]<-site_veg_mean_11[[var]]/1250}
site_veg_mean_10<-site_veg_sum_10
for(var in vars[-length(vars)]){site_veg_mean_10[[var]]<-site_veg_mean_10[[var]]/1250}
# Merge with ABG and N_pixels which was used to calculate Rveg
site_veg_mean_11<-merge(site_veg_mean_11,AGB,by="Site",all.y=T)
site_veg_mean_11<-merge(site_veg_mean_11,N_pixels,by="Site",all.x=T)
site_veg_mean_11$Rveg<-site_veg_mean_11$Veg_pixels/1250
head(site_veg_mean_11)## Site BLUE GREEN RED NIR NDVI mNDVI RVI
## 1 3 263.1248 318.1232 347.8360 516.2600 0.04298983 137.2811 0.3305504
## 2 4 515.3232 629.2576 688.7472 1039.0440 0.09291328 288.3671 0.6947498
## 3 7 425.3872 533.8664 615.5136 952.5120 0.08678800 235.7533 0.6277143
## 4 8 442.9024 541.1168 604.0896 893.5704 0.07790600 237.9156 0.6013766
## 5 11 422.8400 520.3656 586.4856 914.1000 0.09199950 261.6842 0.6633542
## 6 12 245.6072 302.3592 335.9088 536.7072 0.05744080 163.8066 0.4021266
## REEI EVI SAVI MSAVI OSAVI mARI1 mARI2
## 1 187.8177 196.5256 0.06447637 227.6695 53.73506 253.2755 594275.6
## 2 377.4004 428.0197 0.13935080 474.5718 116.13650 520.8222 1197109.2
## 3 323.6249 306.4578 0.13016470 426.3510 108.48039 432.2488 1039634.2
## 4 342.5322 325.0294 0.11684298 413.4433 97.37823 452.3787 1008026.2
## 5 337.0097 364.4880 0.13797915 447.4629 114.99401 463.2074 1024550.6
## 6 192.4228 248.8934 0.08614819 268.0606 71.79753 275.9116 612350.7
## GCC EG FVC Density Name Y5 Y6
## 1 0.07569574 25.2856 0.06443590 HD S2An2HD 11.078032 7.271884
## 2 0.15565754 54.4448 0.14390611 HD S2An3HD 8.601376 9.932828
## 3 0.13488912 26.8320 0.13904259 LD S2An1LD 6.580034 10.962102
## 4 0.13719928 35.2416 0.11646455 HD S2An1HD 15.899325 13.901538
## 5 0.14156769 31.4056 0.14845479 LD S2An2LD 12.376185 12.227722
## 6 0.08330674 23.2024 0.09564599 LD S2An3LD 10.612028 5.449810
## Y7 Y8 Y9 Y11 Ct Veg_pixels Rveg
## 1 10.05684 7.695246 9.305107 10.945360 24.512 277 0.2216
## 2 10.64388 10.864941 13.347178 17.544068 39.336 568 0.4544
## 3 11.77028 12.158156 14.532373 14.902354 32.888 500 0.4000
## 4 15.86886 14.635193 17.084150 17.385223 42.503 504 0.4032
## 5 12.45627 12.243385 15.576473 19.018632 41.707 523 0.4184
## 6 5.50925 5.612370 6.121482 8.329726 18.811 307 0.2456summary(site_veg_mean_11)## Site BLUE GREEN RED
## Min. : 3.00 Min. : 84.1 Min. :103.0 Min. :111.2
## 1st Qu.: 7.75 1st Qu.:243.3 1st Qu.:299.5 1st Qu.:330.6
## Median :13.00 Median :358.1 Median :429.5 Median :467.9
## Mean :12.75 Mean :325.0 Mean :396.6 Mean :439.2
## 3rd Qu.:16.75 3rd Qu.:423.5 3rd Qu.:523.7 3rd Qu.:590.9
## Max. :23.00 Max. :515.3 Max. :629.3 Max. :688.7
##
## NIR NDVI mNDVI RVI
## Min. : 166.3 Min. :0.01377 Min. : 43.29 Min. :0.1050
## 1st Qu.: 505.4 1st Qu.:0.04282 1st Qu.:136.19 1st Qu.:0.3275
## Median : 687.8 Median :0.05640 Median :180.49 Median :0.4241
## Mean : 664.3 Mean :0.05944 Mean :180.52 Mean :0.4434
## 3rd Qu.: 898.7 3rd Qu.:0.08013 3rd Qu.:236.29 3rd Qu.:0.6080
## Max. :1039.0 Max. :0.09291 Max. :288.37 Max. :0.6947
##
## REEI EVI SAVI MSAVI
## Min. : 58.65 Min. : 62.09 Min. :0.02065 Min. : 71.85
## 1st Qu.:184.60 1st Qu.:197.15 1st Qu.:0.06422 1st Qu.:225.26
## Median :244.09 Median :265.14 Median :0.08459 Median :292.90
## Mean :239.84 Mean :258.10 Mean :0.08914 Mean :302.64
## 3rd Qu.:326.97 3rd Qu.:322.67 3rd Qu.:0.12017 3rd Qu.:416.67
## Max. :377.40 Max. :428.02 Max. :0.13935 Max. :474.57
##
## OSAVI mARI1 mARI2 GCC
## Min. : 17.21 Min. : 80.81 Min. : 194124 Min. :0.02402
## 1st Qu.: 53.52 1st Qu.:250.72 1st Qu.: 583343 1st Qu.:0.07485
## Median : 70.50 Median :329.18 Median : 792998 Median :0.09784
## Mean : 74.29 Mean :326.76 Mean : 755552 Mean :0.09854
## 3rd Qu.:100.15 3rd Qu.:437.28 3rd Qu.:1012157 3rd Qu.:0.13547
## Max. :116.14 Max. :520.82 Max. :1197109 Max. :0.15566
##
## EG FVC Density Name Y5
## Min. :10.63 Min. :0.02090 HD:6 S1An1HD:1 Min. : 6.144
## 1st Qu.:24.76 1st Qu.:0.06487 LD:6 S1An1LD:1 1st Qu.: 6.567
## Median :30.10 Median :0.08944 S1An2HD:1 Median : 9.607
## Mean :28.90 Mean :0.09231 S1An2LD:1 Mean : 9.760
## 3rd Qu.:32.67 3rd Qu.:0.12211 S1An3HD:1 3rd Qu.:12.099
## Max. :54.44 Max. :0.14845 S1An3LD:1 Max. :15.899
## (Other):6
## Y6 Y7 Y8 Y9
## Min. : 4.866 Min. : 5.509 Min. : 4.236 Min. : 5.253
## 1st Qu.: 5.931 1st Qu.: 6.522 1st Qu.: 5.602 1st Qu.: 7.717
## Median : 8.158 Median :10.350 Median : 7.992 Median :10.243
## Mean : 8.480 Mean : 9.598 Mean : 8.527 Mean :10.667
## 3rd Qu.:10.842 3rd Qu.:11.552 3rd Qu.:11.188 3rd Qu.:13.643
## Max. :13.902 Max. :15.869 Max. :14.635 Max. :17.084
##
## Y11 Ct Veg_pixels Rveg
## Min. : 4.314 Min. :14.77 Min. : 87.0 Min. :0.0696
## 1st Qu.: 9.780 1st Qu.:19.19 1st Qu.:273.2 1st Qu.:0.2186
## Median :12.193 Median :24.15 Median :358.0 Median :0.2864
## Mean :12.352 Mean :27.05 Mean :361.7 Mean :0.2893
## 3rd Qu.:15.523 3rd Qu.:34.50 3rd Qu.:501.0 3rd Qu.:0.4008
## Max. :19.019 Max. :42.50 Max. :568.0 Max. :0.4544
## site_veg_mean_10<-merge(site_veg_mean_10,AGB,by="Site",all.y=T)
site_veg_mean_10<-merge(site_veg_mean_10,N_pixels,by="Site",all.x=T)
site_veg_mean_10$Rveg<-site_veg_mean_10$Veg_pixels/1250
head(site_veg_mean_10)## Site BLUE GREEN RED NIR NDVI mNDVI RVI
## 1 3 182.4624 272.4456 290.9480 384.4976 0.03055138 84.26654 0.2929318
## 2 4 361.6720 535.5872 557.7688 771.6264 0.07299333 202.94313 0.6299595
## 3 7 328.2176 488.9560 486.9712 715.7512 0.07541739 208.70005 0.5883808
## 4 8 320.6152 477.9224 516.8288 694.7440 0.05937380 159.87709 0.5435623
## 5 11 331.5056 497.6168 506.3976 707.2520 0.06853058 189.26575 0.5858051
## 6 12 184.8048 277.0344 282.0744 409.6448 0.04480389 121.20094 0.3578630
## REEI EVI SAVI MSAVI OSAVI mARI1 mARI2
## 1 210.1170 89.35629 0.04581953 205.3685 38.18721 259.4321 451651.9
## 2 411.5221 229.44915 0.10947100 440.4444 91.23660 545.9118 930665.7
## 3 342.3488 252.14325 0.11310699 407.6209 94.26664 501.5485 902925.8
## 4 375.1587 171.10828 0.08904571 380.8135 74.21325 466.9116 805731.1
## 5 377.2840 217.39316 0.10277793 407.1852 85.65844 513.3204 875649.8
## 6 213.3675 146.09643 0.06719360 247.1131 56.00159 300.7750 508954.1
## GCC EG FVC Density Name Y5 Y6
## 1 0.08078233 71.4808 0.03146407 HD S2An2HD 11.078032 7.271884
## 2 0.16683868 151.7336 0.09311751 HD S2An3HD 8.601376 9.932828
## 3 0.14963963 162.7232 0.11282492 LD S2An1LD 6.580034 10.962102
## 4 0.14623447 118.4008 0.06771303 HD S2An1HD 15.899325 13.901538
## 5 0.15545777 157.3304 0.08938938 LD S2An2LD 12.376185 12.227722
## 6 0.09095828 87.1896 0.06512168 LD S2An3LD 10.612028 5.449810
## Y7 Y8 Y9 Y11 Ct Veg_pixels Rveg
## 1 10.05684 7.695246 9.305107 10.945360 24.512 277 0.2216
## 2 10.64388 10.864941 13.347178 17.544068 39.336 568 0.4544
## 3 11.77028 12.158156 14.532373 14.902354 32.888 500 0.4000
## 4 15.86886 14.635193 17.084150 17.385223 42.503 504 0.4032
## 5 12.45627 12.243385 15.576473 19.018632 41.707 523 0.4184
## 6 5.50925 5.612370 6.121482 8.329726 18.811 307 0.2456summary(site_veg_mean_10)## Site BLUE GREEN RED
## Min. : 3.00 Min. : 59.81 Min. : 90.28 Min. : 89.02
## 1st Qu.: 7.75 1st Qu.:180.01 1st Qu.:269.48 1st Qu.:279.21
## Median :13.00 Median :226.72 Median :341.84 Median :313.94
## Mean :12.75 Mean :230.15 Mean :343.93 Mean :347.43
## 3rd Qu.:16.75 3rd Qu.:322.52 3rd Qu.:480.68 3rd Qu.:491.83
## Max. :23.00 Max. :361.67 Max. :535.59 Max. :557.77
##
## NIR NDVI mNDVI RVI
## Min. :128.7 Min. :0.01267 Min. : 35.81 Min. :0.1011
## 1st Qu.:383.6 1st Qu.:0.03449 1st Qu.: 93.34 1st Qu.:0.2969
## Median :510.6 Median :0.05810 Median :160.08 Median :0.4477
## Mean :505.4 Mean :0.05355 Mean :146.20 Mean :0.4252
## 3rd Qu.:697.9 3rd Qu.:0.06965 3rd Qu.:192.97 3rd Qu.:0.5864
## Max. :771.6 Max. :0.09520 Max. :242.24 Max. :0.6300
##
## REEI EVI SAVI MSAVI
## Min. : 60.43 Min. : 44.7 Min. :0.01900 Min. : 70.06
## 1st Qu.:204.21 1st Qu.:107.6 1st Qu.:0.05173 1st Qu.:206.21
## Median :230.45 Median :187.3 Median :0.08714 Median :305.25
## Mean :250.27 Mean :181.7 Mean :0.08030 Mean :292.21
## 3rd Qu.:350.55 3rd Qu.:235.1 3rd Qu.:0.10445 3rd Qu.:395.98
## Max. :411.52 Max. :367.4 Max. :0.14277 Max. :440.44
##
## OSAVI mARI1 mARI2 GCC
## Min. : 15.83 Min. : 88.6 Min. :164714 Min. :0.02622
## 1st Qu.: 43.12 1st Qu.:258.1 1st Qu.:460228 1st Qu.:0.07996
## Median : 72.62 Median :375.1 Median :675425 Median :0.10847
## Mean : 66.93 Mean :359.5 Mean :633117 Mean :0.10779
## 3rd Qu.: 87.05 3rd Qu.:475.6 3rd Qu.:839300 3rd Qu.:0.14709
## Max. :118.99 Max. :545.9 Max. :930666 Max. :0.16684
##
## EG FVC Density Name Y5
## Min. : 31.74 Min. :0.01837 HD:6 S1An1HD:1 Min. : 6.144
## 1st Qu.: 76.27 1st Qu.:0.04451 LD:6 S1An1LD:1 1st Qu.: 6.567
## Median :121.59 Median :0.07845 S1An2HD:1 Median : 9.607
## Mean :110.28 Mean :0.07883 S1An2LD:1 Mean : 9.760
## 3rd Qu.:153.13 3rd Qu.:0.09804 S1An3HD:1 3rd Qu.:12.099
## Max. :162.72 Max. :0.18319 S1An3LD:1 Max. :15.899
## (Other):6
## Y6 Y7 Y8 Y9
## Min. : 4.866 Min. : 5.509 Min. : 4.236 Min. : 5.253
## 1st Qu.: 5.931 1st Qu.: 6.522 1st Qu.: 5.602 1st Qu.: 7.717
## Median : 8.158 Median :10.350 Median : 7.992 Median :10.243
## Mean : 8.480 Mean : 9.598 Mean : 8.527 Mean :10.667
## 3rd Qu.:10.842 3rd Qu.:11.552 3rd Qu.:11.188 3rd Qu.:13.643
## Max. :13.902 Max. :15.869 Max. :14.635 Max. :17.084
##
## Y11 Ct Veg_pixels Rveg
## Min. : 4.314 Min. :14.77 Min. : 87.0 Min. :0.0696
## 1st Qu.: 9.780 1st Qu.:19.19 1st Qu.:273.2 1st Qu.:0.2186
## Median :12.193 Median :24.15 Median :358.0 Median :0.2864
## Mean :12.352 Mean :27.05 Mean :361.7 Mean :0.2893
## 3rd Qu.:15.523 3rd Qu.:34.50 3rd Qu.:501.0 3rd Qu.:0.4008
## Max. :19.019 Max. :42.50 Max. :568.0 Max. :0.4544
## 2.9 Regression relationship between AGB and VIs
2.9.1 Spearm test for each variables with Carbon
# Build a function for calculate "spearman rho"
f_spear<-function(data,var){
a<-pspearman::spearman.test(data[[var]],data[["Ct"]])
return(c(var,a$estimate,a$p.value))
}
# the whole site
Rho_11<-sapply(vars,f_spear,data=site_mean_11)
Rho_11<-data.frame(t(Rho_11))
for(i in c(2:3)){Rho_11[[i]]<-as.numeric(as.character(Rho_11[[i]]))}
names(Rho_11)<-c("VIs","Rho","P")
print(Rho_11,digits=3)## VIs Rho P
## BLUE BLUE -0.706 0.012923
## GREEN GREEN -0.580 0.052076
## RED RED -0.685 0.017015
## NIR NIR -0.301 0.342415
## NDVI NDVI 0.881 0.000335
## RVI RVI 0.881 0.000335
## SAVI SAVI 0.881 0.000335
## GCC GCC 0.420 0.176733
## EG EG 0.636 0.029942
## FVC FVC 0.881 0.000335
## Rveg Rveg 0.909 0.000115Rho_10<-sapply(vars,f_spear,data=site_mean_10)
Rho_10<-data.frame(t(Rho_10))
for(i in c(2:3)){Rho_10[[i]]<-as.numeric(as.character(Rho_10[[i]]))}
names(Rho_10)<-c("VIs","Rho","P")
print(Rho_10,digits=3)## VIs Rho P
## BLUE BLUE -0.6014 0.042800
## GREEN GREEN -0.6713 0.020194
## RED RED -0.6364 0.029942
## NIR NIR -0.6923 0.015546
## NDVI NDVI 0.1608 0.619183
## RVI RVI 0.2308 0.470802
## SAVI SAVI 0.1608 0.619183
## GCC GCC 0.1399 0.667310
## EG EG -0.0909 0.782983
## FVC FVC 0.1608 0.619183
## Rveg Rveg 0.9091 0.000115# only vegetation
Rho_veg_11<-sapply(vars,f_spear,data=site_veg_mean_11)
Rho_veg_11<-data.frame(t(Rho_veg_11))
for(i in c(2:3)){Rho_veg_11[[i]]<-as.numeric(as.character(Rho_veg_11[[i]]))}
names(Rho_veg_11)<-c("VIs","Rho_Veg","P_veg")
print(Rho_veg_11,digits=3)## VIs Rho_Veg P_veg
## BLUE BLUE 0.937 2.79e-05
## GREEN GREEN 0.937 2.79e-05
## RED RED 0.916 8.39e-05
## NIR NIR 0.881 3.35e-04
## NDVI NDVI 0.860 6.44e-04
## RVI RVI 0.895 2.03e-04
## SAVI SAVI 0.860 6.44e-04
## GCC GCC 0.909 1.15e-04
## EG EG 0.727 9.63e-03
## FVC FVC 0.846 9.45e-04
## Rveg Rveg 0.909 1.15e-04Rho<-cbind(Rho_veg_11,Rho_11[-1])
print(Rho,digits=3)## VIs Rho_Veg P_veg Rho P
## BLUE BLUE 0.937 2.79e-05 -0.706 0.012923
## GREEN GREEN 0.937 2.79e-05 -0.580 0.052076
## RED RED 0.916 8.39e-05 -0.685 0.017015
## NIR NIR 0.881 3.35e-04 -0.301 0.342415
## NDVI NDVI 0.860 6.44e-04 0.881 0.000335
## RVI RVI 0.895 2.03e-04 0.881 0.000335
## SAVI SAVI 0.860 6.44e-04 0.881 0.000335
## GCC GCC 0.909 1.15e-04 0.420 0.176733
## EG EG 0.727 9.63e-03 0.636 0.029942
## FVC FVC 0.846 9.45e-04 0.881 0.000335
## Rveg Rveg 0.909 1.15e-04 0.909 0.000115Rho_veg_10<-sapply(vars,f_spear,data=site_veg_mean_10)
Rho_veg_10<-data.frame(t(Rho_veg_10))
for(i in c(2:3)){Rho_veg_10[[i]]<-as.numeric(as.character(Rho_veg_10[[i]]))}
names(Rho_veg_10)<-c("VIs","Rho_Veg","P_veg")
print(Rho_veg_10,digits=3)## VIs Rho_Veg P_veg
## BLUE BLUE 0.881 3.35e-04
## GREEN GREEN 0.881 3.35e-04
## RED RED 0.937 2.79e-05
## NIR NIR 0.881 3.35e-04
## NDVI NDVI 0.727 9.63e-03
## RVI RVI 0.797 2.91e-03
## SAVI SAVI 0.727 9.63e-03
## GCC GCC 0.888 2.61e-04
## EG EG 0.699 1.42e-02
## FVC FVC 0.580 5.21e-02
## Rveg Rveg 0.909 1.15e-04Rho<-cbind(Rho_veg_10,Rho_10[-1])
print(Rho,digits=3)## VIs Rho_Veg P_veg Rho P
## BLUE BLUE 0.881 3.35e-04 -0.6014 0.042800
## GREEN GREEN 0.881 3.35e-04 -0.6713 0.020194
## RED RED 0.937 2.79e-05 -0.6364 0.029942
## NIR NIR 0.881 3.35e-04 -0.6923 0.015546
## NDVI NDVI 0.727 9.63e-03 0.1608 0.619183
## RVI RVI 0.797 2.91e-03 0.2308 0.470802
## SAVI SAVI 0.727 9.63e-03 0.1608 0.619183
## GCC GCC 0.888 2.61e-04 0.1399 0.667310
## EG EG 0.699 1.42e-02 -0.0909 0.782983
## FVC FVC 0.580 5.21e-02 0.1608 0.619183
## Rveg Rveg 0.909 1.15e-04 0.9091 0.000115#---------------------2.9.2 Linear regression model
# Build a function for calculate "coefficients for carbon and vis"
f_coef<-function(data,var){
all<-summary.lm(lm(data[["Ct"]] ~ data[[var]]))
all_rms<-rmse(data[["Ct"]],data[[var]])
HD<-summary.lm(lm(data[data$Density=="HD","Ct"] ~ data[data$Density=="HD",var]))
HD_rms<-rmse(data[data$Density=="HD","Ct"] , data[data$Density=="HD",i])
LD<-summary.lm(lm(data[data$Density=="LD","Ct"] ~ data[data$Density=="LD",var]))
LD_rms<-rmse(data[data$Density=="LD","Ct"] , data[data$Density=="LD",var])
return(c(var,all$r.squared,all$coefficients[1],all$coefficients[2],all$coefficients[8],all_rms,HD$r.squared,HD$coefficients[1],HD$coefficients[2],HD$coefficients[8],HD_rms,LD$r.squared,LD$coefficients[1],LD$coefficients[2],LD$coefficients[8],LD_rms))
} 2.9.2.1 2011
# The whole site
Coef_11<-sapply(vars[-c(1:4)],f_coef,data=site_mean_11)
Coef_11<-data.frame(t(Coef_11))
for(i in c(2:16)){Coef_11[[i]]<-as.numeric(as.character(Coef_11[[i]]))}
names(Coef_11)<-c("VIs","r_all","inte_all","slope_all","P_all","Rmse_all","r_HD","inte_HD","slope_HD","P_HD","Rmse_HD","r_LD","inte_LD","slope_LD","P_LD","Rmse_LD")
print(Coef_11,digits=3)## VIs r_all inte_all slope_all P_all Rmse_all r_HD inte_HD
## NDVI NDVI 0.783 -45.52 532.080 1.32e-04 28.5 0.9361 -61.58
## RVI RVI 0.754 -207.52 176.564 2.51e-04 27.3 0.9290 -273.74
## SAVI SAVI 0.783 -45.52 354.777 1.32e-04 28.4 0.9361 -61.59
## GCC GCC 0.104 -559.11 1782.293 3.07e-01 28.3 0.0755 -565.17
## EG EG 0.391 41.52 0.282 2.97e-02 80.1 0.4335 40.65
## FVC FVC 0.783 -4.84 215.081 1.32e-04 28.5 0.9361 -10.60
## Rveg Rveg 0.827 5.44 74.707 4.14e-05 28.3 0.8315 7.22
## slope_HD P_HD Rmse_HD r_LD inte_LD slope_LD P_LD Rmse_LD
## NDVI 666.74 0.00157 1469 0.754 -37.99 463.375 0.0248 27.5
## RVI 228.47 0.00194 1469 0.740 -181.16 155.135 0.0279 26.4
## SAVI 444.58 0.00157 1469 0.754 -37.99 308.962 0.0248 27.5
## GCC 1797.73 0.59825 1469 0.182 -970.74 3040.603 0.3995 27.4
## EG 0.31 0.15507 1469 0.488 51.48 0.411 0.1226 88.2
## FVC 269.52 0.00157 1469 0.754 -2.56 187.309 0.0248 27.5
## Rveg 71.86 0.01131 1469 0.848 3.07 79.521 0.0092 27.4# reshape coef_11
.all<-cbind(Coef_11[c(1:6)],type="ALL")
.hd<-cbind(Coef_11[c(1,7:11)],type="HD")
.ld<-cbind(Coef_11[c(1,12:16)],type="LD")
names(.all)<-names(.hd)<-names(.ld)<-c("VIs","R2","intercept","slope","P","Rmse","Type")
Coef_11_reshape<-rbind(.all,.hd,.ld)
Coef_11_reshape<-arrange(Coef_11_reshape,VIs,desc(Type))
print(Coef_11_reshape,digits=3)## VIs R2 intercept slope P Rmse Type
## 1 EG 0.4881 51.48 0.411 1.23e-01 88.2 LD
## 2 EG 0.4335 40.65 0.310 1.55e-01 1468.6 HD
## 3 EG 0.3909 41.52 0.282 2.97e-02 80.1 ALL
## 4 FVC 0.7544 -2.56 187.309 2.48e-02 27.5 LD
## 5 FVC 0.9361 -10.60 269.516 1.57e-03 1468.6 HD
## 6 FVC 0.7827 -4.84 215.081 1.32e-04 28.5 ALL
## 7 GCC 0.1816 -970.74 3040.603 4.00e-01 27.4 LD
## 8 GCC 0.0755 -565.17 1797.733 5.98e-01 1468.6 HD
## 9 GCC 0.1040 -559.11 1782.293 3.07e-01 28.3 ALL
## 10 NDVI 0.7544 -37.99 463.375 2.48e-02 27.5 LD
## 11 NDVI 0.9361 -61.58 666.744 1.57e-03 1468.6 HD
## 12 NDVI 0.7827 -45.52 532.080 1.32e-04 28.5 ALL
## 13 Rveg 0.8475 3.07 79.521 9.20e-03 27.4 LD
## 14 Rveg 0.8315 7.22 71.863 1.13e-02 1468.6 HD
## 15 Rveg 0.8269 5.44 74.707 4.14e-05 28.3 ALL
## 16 RVI 0.7403 -181.16 155.135 2.79e-02 26.4 LD
## 17 RVI 0.9290 -273.74 228.470 1.94e-03 1468.6 HD
## 18 RVI 0.7535 -207.52 176.564 2.51e-04 27.3 ALL
## 19 SAVI 0.7544 -37.99 308.962 2.48e-02 27.5 LD
## 20 SAVI 0.9361 -61.59 444.576 1.57e-03 1468.6 HD
## 21 SAVI 0.7827 -45.52 354.777 1.32e-04 28.4 ALL# only with vegetation pixels
Coef_veg_11<-sapply(vars[-c(1:4)],f_coef,data=site_veg_mean_11)
Coef_veg_11<-data.frame(t(Coef_veg_11))
for(i in c(2:16)){Coef_veg_11[[i]]<-as.numeric(as.character(Coef_veg_11[[i]]))}
names(Coef_veg_11)<-c("VIs","r_all","inte_all","slope_all","P_all","Rmse_all","r_HD","inte_HD","slope_HD","P_HD","Rmse_HD","r_LD","inte_LD","slope_LD","P_LD","Rmse_LD")
print(Coef_veg_11,digits=3)## VIs r_all inte_all slope_all P_all Rmse_all r_HD inte_HD
## NDVI NDVI 0.782 7.09 335.866 1.35e-04 28.55 0.835 7.82
## RVI RVI 0.811 6.09 47.278 6.46e-05 28.14 0.836 7.46
## SAVI SAVI 0.782 7.09 223.943 1.35e-04 28.51 0.835 7.82
## GCC GCC 0.826 5.35 220.241 4.30e-05 28.50 0.830 7.21
## EG EG 0.459 10.50 0.573 1.56e-02 8.51 0.605 9.32
## FVC FVC 0.742 8.37 202.442 3.20e-04 28.51 0.833 8.28
## Rveg Rveg 0.827 5.44 74.707 4.14e-05 28.30 0.832 7.22
## slope_HD P_HD Rmse_HD r_LD inte_LD slope_LD P_LD Rmse_LD
## NDVI 356.51 0.0108 29.4 0.807 5.29 334.515 0.01494 27.60
## RVI 47.40 0.0107 29.4 0.831 4.09 48.547 0.01144 27.19
## SAVI 237.70 0.0108 29.4 0.807 5.29 223.041 0.01494 27.57
## GCC 210.40 0.0116 29.4 0.845 2.83 236.595 0.00948 27.57
## EG 0.58 0.0685 29.4 0.292 9.17 0.661 0.26867 7.71
## FVC 230.98 0.0111 29.4 0.781 6.52 197.612 0.01942 27.56
## Rveg 71.86 0.0113 29.4 0.848 3.07 79.521 0.00920 27.36# reshape coef_11
.all<-cbind(Coef_veg_11[c(1:6)],type="ALL")
.hd<-cbind(Coef_veg_11[c(1,7:11)],type="HD")
.ld<-cbind(Coef_veg_11[c(1,12:16)],type="LD")
names(.all)<-names(.hd)<-names(.ld)<-c("VIs","R2","intercept","slope","P","Rmse","Type")
Coef_veg_11_reshape<-rbind(.all,.hd,.ld)
Coef_veg_11_reshape<-arrange(Coef_veg_11_reshape,VIs,desc(Type))
print(Coef_veg_11_reshape,digits=3)## VIs R2 intercept slope P Rmse Type
## 1 EG 0.292 9.17 0.661 2.69e-01 7.71 LD
## 2 EG 0.605 9.32 0.580 6.85e-02 29.41 HD
## 3 EG 0.459 10.50 0.573 1.56e-02 8.51 ALL
## 4 FVC 0.781 6.52 197.612 1.94e-02 27.56 LD
## 5 FVC 0.833 8.28 230.979 1.11e-02 29.41 HD
## 6 FVC 0.742 8.37 202.442 3.20e-04 28.51 ALL
## 7 GCC 0.845 2.83 236.595 9.48e-03 27.57 LD
## 8 GCC 0.830 7.21 210.402 1.16e-02 29.41 HD
## 9 GCC 0.826 5.35 220.241 4.30e-05 28.50 ALL
## 10 NDVI 0.807 5.29 334.515 1.49e-02 27.60 LD
## 11 NDVI 0.835 7.82 356.507 1.08e-02 29.41 HD
## 12 NDVI 0.782 7.09 335.866 1.35e-04 28.55 ALL
## 13 Rveg 0.848 3.07 79.521 9.20e-03 27.36 LD
## 14 Rveg 0.832 7.22 71.863 1.13e-02 29.41 HD
## 15 Rveg 0.827 5.44 74.707 4.14e-05 28.30 ALL
## 16 RVI 0.831 4.09 48.547 1.14e-02 27.19 LD
## 17 RVI 0.836 7.46 47.398 1.07e-02 29.41 HD
## 18 RVI 0.811 6.09 47.278 6.46e-05 28.14 ALL
## 19 SAVI 0.807 5.29 223.041 1.49e-02 27.57 LD
## 20 SAVI 0.835 7.82 237.704 1.08e-02 29.41 HD
## 21 SAVI 0.782 7.09 223.943 1.35e-04 28.51 ALL2.9.2.2 2010
# The whole site
Coef_10<-sapply(vars[-c(1:4)],f_coef,data=site_mean_10)
Coef_10<-data.frame(t(Coef_10))
for(i in c(2:16)){Coef_10[[i]]<-as.numeric(as.character(Coef_10[[i]]))}
names(Coef_10)<-c("VIs","r_all","inte_all","slope_all","P_all","Rmse_all","r_HD","inte_HD","slope_HD","P_HD","Rmse_HD","r_LD","inte_LD","slope_LD","P_LD","Rmse_LD")
print(Coef_10,digits=3)## VIs r_all inte_all slope_all P_all Rmse_all r_HD inte_HD
## NDVI NDVI 1.11e-04 27.51 -3.0771 9.74e-01 28.5 0.0722 42.25
## RVI RVI 4.47e-04 29.81 -2.0279 9.48e-01 27.3 0.0736 72.65
## SAVI SAVI 1.11e-04 27.51 -2.0552 9.74e-01 28.4 0.0722 42.25
## GCC GCC 6.93e-06 25.78 3.5540 9.94e-01 28.3 0.3133 337.53
## EG EG 1.13e-02 31.37 -0.0159 7.42e-01 252.2 0.4149 53.05
## FVC FVC 1.11e-04 27.25 -1.1132 9.74e-01 28.4 0.0722 33.88
## Rveg Rveg 8.27e-01 5.44 74.7069 4.14e-05 28.3 0.8315 7.22
## slope_HD P_HD Rmse_HD r_LD inte_LD slope_LD P_LD Rmse_LD
## NDVI -96.6739 0.6067 29.2 0.0454 18.522 51.7728 0.6854 27.5
## RVI -33.0257 0.6030 29.2 0.0360 5.957 14.8826 0.7187 26.4
## SAVI -64.4676 0.6066 29.2 0.0454 18.521 34.5218 0.6854 27.5
## GCC -862.2440 0.2481 29.2 0.3096 -209.638 656.0979 0.2515 27.3
## EG -0.0927 0.1675 29.2 0.4037 -0.604 0.0995 0.1752 249.0
## FVC -34.9743 0.6067 29.2 0.0454 23.000 18.7301 0.6854 27.5
## Rveg 71.8626 0.0113 29.2 0.8475 3.069 79.5214 0.0092 27.4# reshape coef_10
.all<-cbind(Coef_10[c(1:6)],type="ALL")
.hd<-cbind(Coef_10[c(1,7:11)],type="HD")
.ld<-cbind(Coef_10[c(1,12:16)],type="LD")
names(.all)<-names(.hd)<-names(.ld)<-c("VIs","R2","intercept","slope","P","Rmse","Type")
Coef_10_reshape<-rbind(.all,.hd,.ld)
Coef_10_reshape<-arrange(Coef_10_reshape,VIs,desc(Type))
print(Coef_10_reshape,digits=3)## VIs R2 intercept slope P Rmse Type
## 1 EG 4.04e-01 -0.604 0.0995 1.75e-01 249.0 LD
## 2 EG 4.15e-01 53.054 -0.0927 1.67e-01 29.2 HD
## 3 EG 1.13e-02 31.366 -0.0159 7.42e-01 252.2 ALL
## 4 FVC 4.54e-02 23.000 18.7301 6.85e-01 27.5 LD
## 5 FVC 7.22e-02 33.883 -34.9743 6.07e-01 29.2 HD
## 6 FVC 1.11e-04 27.247 -1.1132 9.74e-01 28.4 ALL
## 7 GCC 3.10e-01 -209.638 656.0979 2.52e-01 27.3 LD
## 8 GCC 3.13e-01 337.531 -862.2440 2.48e-01 29.2 HD
## 9 GCC 6.93e-06 25.777 3.5540 9.94e-01 28.3 ALL
## 10 NDVI 4.54e-02 18.522 51.7728 6.85e-01 27.5 LD
## 11 NDVI 7.22e-02 42.245 -96.6739 6.07e-01 29.2 HD
## 12 NDVI 1.11e-04 27.513 -3.0771 9.74e-01 28.5 ALL
## 13 Rveg 8.48e-01 3.069 79.5214 9.20e-03 27.4 LD
## 14 Rveg 8.32e-01 7.220 71.8626 1.13e-02 29.2 HD
## 15 Rveg 8.27e-01 5.439 74.7069 4.14e-05 28.3 ALL
## 16 RVI 3.60e-02 5.957 14.8826 7.19e-01 26.4 LD
## 17 RVI 7.36e-02 72.647 -33.0257 6.03e-01 29.2 HD
## 18 RVI 4.47e-04 29.813 -2.0279 9.48e-01 27.3 ALL
## 19 SAVI 4.54e-02 18.521 34.5218 6.85e-01 27.5 LD
## 20 SAVI 7.22e-02 42.247 -64.4676 6.07e-01 29.2 HD
## 21 SAVI 1.11e-04 27.514 -2.0552 9.74e-01 28.4 ALL# only with vegetation pixels
Coef_veg_10<-sapply(vars[-c(1:4)],f_coef,data=site_veg_mean_10)
Coef_veg_10<-data.frame(t(Coef_veg_10))
for(i in c(2:16)){Coef_veg_10[[i]]<-as.numeric(as.character(Coef_veg_10[[i]]))}
names(Coef_veg_10)<-c("VIs","r_all","inte_all","slope_all","P_all","Rmse_all","r_HD","inte_HD","slope_HD","P_HD","Rmse_HD","r_LD","inte_LD","slope_LD","P_LD","Rmse_LD")
print(Coef_veg_10,digits=3)## VIs r_all inte_all slope_all P_all Rmse_all r_HD inte_HD
## NDVI NDVI 0.390 13.73 248.806 2.99e-02 28.6 0.436 12.65
## RVI RVI 0.663 7.88 45.099 1.27e-03 28.2 0.700 8.31
## SAVI SAVI 0.390 13.73 165.903 2.99e-02 28.5 0.436 12.65
## GCC GCC 0.798 5.69 198.166 8.99e-05 28.5 0.800 7.29
## EG EG 0.534 9.51 0.159 6.93e-03 90.9 0.446 10.90
## FVC FVC 0.127 21.13 75.199 2.56e-01 28.5 0.115 21.16
## Rveg Rveg 0.827 5.44 74.707 4.14e-05 28.3 0.832 7.22
## slope_HD P_HD Rmse_HD r_LD inte_LD slope_LD P_LD Rmse_LD
## NDVI 304.249 0.1532 29.4 0.419 13.25 227.677 0.1647 27.6
## RVI 47.568 0.0377 29.4 0.668 6.95 43.909 0.0471 27.2
## SAVI 202.871 0.1532 29.4 0.419 13.25 151.815 0.1647 27.6
## GCC 193.603 0.0161 29.4 0.828 3.61 207.064 0.0119 27.6
## EG 0.162 0.1474 29.4 0.719 6.91 0.167 0.0329 97.2
## FVC 96.297 0.5100 29.4 0.194 19.52 76.425 0.3822 27.6
## Rveg 71.863 0.0113 29.4 0.848 3.07 79.521 0.0092 27.4# reshape coef_10
.all<-cbind(Coef_veg_10[c(1:6)],type="ALL")
.hd<-cbind(Coef_veg_10[c(1,7:11)],type="HD")
.ld<-cbind(Coef_veg_10[c(1,12:16)],type="LD")
names(.all)<-names(.hd)<-names(.ld)<-c("VIs","R2","intercept","slope","P","Rmse","Type")
Coef_veg_10_reshape<-rbind(.all,.hd,.ld)
Coef_veg_10_reshape<-arrange(Coef_veg_10_reshape,VIs,desc(Type))
print(Coef_veg_10_reshape,digits=3)## VIs R2 intercept slope P Rmse Type
## 1 EG 0.719 6.91 0.167 3.29e-02 97.2 LD
## 2 EG 0.446 10.90 0.162 1.47e-01 29.4 HD
## 3 EG 0.534 9.51 0.159 6.93e-03 90.9 ALL
## 4 FVC 0.194 19.52 76.425 3.82e-01 27.6 LD
## 5 FVC 0.115 21.16 96.297 5.10e-01 29.4 HD
## 6 FVC 0.127 21.13 75.199 2.56e-01 28.5 ALL
## 7 GCC 0.828 3.61 207.064 1.19e-02 27.6 LD
## 8 GCC 0.800 7.29 193.603 1.61e-02 29.4 HD
## 9 GCC 0.798 5.69 198.166 8.99e-05 28.5 ALL
## 10 NDVI 0.419 13.25 227.677 1.65e-01 27.6 LD
## 11 NDVI 0.436 12.65 304.249 1.53e-01 29.4 HD
## 12 NDVI 0.390 13.73 248.806 2.99e-02 28.6 ALL
## 13 Rveg 0.848 3.07 79.521 9.20e-03 27.4 LD
## 14 Rveg 0.832 7.22 71.863 1.13e-02 29.4 HD
## 15 Rveg 0.827 5.44 74.707 4.14e-05 28.3 ALL
## 16 RVI 0.668 6.95 43.909 4.71e-02 27.2 LD
## 17 RVI 0.700 8.31 47.568 3.77e-02 29.4 HD
## 18 RVI 0.663 7.88 45.099 1.27e-03 28.2 ALL
## 19 SAVI 0.419 13.25 151.815 1.65e-01 27.6 LD
## 20 SAVI 0.436 12.65 202.871 1.53e-01 29.4 HD
## 21 SAVI 0.390 13.73 165.903 2.99e-02 28.5 ALL2.9.3 plot Linear regression model
.df <- data.frame(x = site_mean_11\(Veg_pixels/1250, y = site_mean_11\)Ct, z = site_mean_11$Density)
# Build a function for ploting relationship between carbon and vis
f_plot<-function(data,index,name,outname){
.df <- data.frame(x = data[[index]], y = data$Ct, z = data$Density)
.plot <- ggplot(data = .df, aes(x = x, y = y, col = z, shape = z)) + geom_point(aes(cex = 3)) + stat_smooth(aes(fill = z), method =
"lm", se = FALSE) + scale_colour_brewer(palette = "Set1") + xlab(name) +
xlim(min(.df$x),max(.df$x))+ylim(0, 45)+
ylab("Carbon storage (Ct) (t/ha)") + labs(colour = "Density", shape = "Density", fill = "Density") + theme_bw(base_size = 14, base_family = "Times") +
theme(legend.position = "right")+
geom_abline(size=1,intercept = lm(.df$y~.df$x)$coefficients[1], slope = lm(.df$y~.df$x)$coefficients[2])
print(.plot)
ggsave(paste("results/",outname,sep=""),.plot)
rm(.df, .plot)
}2.9.3.1 2011 in Summer
# Whole site
f_plot(site_mean_11,"RVI","RVI","RVI_R.pdf")
f_plot(site_mean_11,"Rveg","Rveg","Rveg_R.pdf")
f_plot(site_mean_11,"FVC","FVC","FVC_R.pdf")
f_plot(site_mean_11,"GCC","GCC","GCC_R.pdf")
f_plot(site_mean_11,"EG","EG","EG_R.pdf")
# only veg pixels in each site
f_plot(site_veg_mean_11,"RVI","RVI","RVI_VEG_R.pdf")
f_plot(site_veg_mean_11,"GCC","GCC","GCC_VEG_R.pdf")
f_plot(site_veg_mean_11,"EG","EG","EG_R.pdf")
2.9.3.2 2010 in Winter
# Whole site
f_plot(site_mean_10,"RVI","RVI","RVI_R.pdf")
f_plot(site_mean_10,"Rveg","Rveg","Rveg_R.pdf")
f_plot(site_mean_10,"FVC","FVC","FVC_R.pdf")
f_plot(site_mean_10,"GCC","GCC","GCC_R.pdf")
f_plot(site_mean_10,"EG","EG","EG_R.pdf")
# only veg pixels in each site
f_plot(site_veg_mean_10,"RVI","RVI","RVI_VEG_R.pdf")
f_plot(site_veg_mean_10,"GCC","GCC","GCC_VEG_R.pdf")
f_plot(site_veg_mean_10,"EG","EG","EG_R.pdf")
2.10 Slope_TEST for relationship between different density
library(smatr)
a<-site_mean_11
a$Site[a$Density=="HD"]<-1
a$Site[a$Density=="LD"]<-2
with(a, slope.com(RVI,Ct,Site, method = 'SMA', alpha = 0.05)) ## $LR
## [1] 0.7459778
##
## $p
## [1] 0.3877527
##
## $b
## [1] 0.004453623
##
## $ci
## [1] 0.003321831 0.006340080
##
## $varb
## [1] 2.923973e-07
##
## $lambda
## [1] 1.983476e-05
##
## $bs
## 1 2
## slope 0.004218641 0.005546200
## lower.CI.lim 0.002937351 0.002870149
## upper.CI.lim 0.006058836 0.010717329
##
## $df
## [1] 1 with(a, slope.com(Rveg,Ct,Site, method = 'SMA', alpha = 0.05)) ## $LR
## [1] 0.09119924
##
## $p
## [1] 0.7626581
##
## $b
## [1] 0.01209224
##
## $ci
## [1] 0.008628563 0.017014310
##
## $varb
## [1] 2.957932e-06
##
## $lambda
## [1] 0.0001462223
##
## $bs
## 1 2
## slope 0.012689036 0.011577007
## lower.CI.lim 0.007373859 0.006893115
## upper.CI.lim 0.021835466 0.019443615
##
## $df
## [1] 1 a<-site_mean_10
a$Site[a$Density=="HD"]<-1
a$Site[a$Density=="LD"]<-2
with(a, slope.com(RVI,Ct,Site, method = 'SMA', alpha = 0.05)) ## $LR
## [1] 0.3546547
##
## $p
## [1] 0.55149
##
## $b
## [1] -0.01019141
##
## $ci
## [1] -0.022145458 -0.004713874
##
## $varb
## [1] 1.229988e-05
##
## $lambda
## [1] 0.0001038649
##
## $bs
## 1 2
## slope -0.008215308 0.012752331
## lower.CI.lim -0.002733814 0.004176329
## upper.CI.lim -0.024687593 0.038938974
##
## $df
## [1] 1 with(a, slope.com(Rveg,Ct,Site, method = 'SMA', alpha = 0.05)) ## $LR
## [1] 0.09119924
##
## $p
## [1] 0.7626581
##
## $b
## [1] 0.01209224
##
## $ci
## [1] 0.008628563 0.017014310
##
## $varb
## [1] 2.957932e-06
##
## $lambda
## [1] 0.0001462223
##
## $bs
## 1 2
## slope 0.012689036 0.011577007
## lower.CI.lim 0.007373859 0.006893115
## upper.CI.lim 0.021835466 0.019443615
##
## $df
## [1] 1#-----------2.11 compare between different time images
2.11.1 T_TEST between saltbush and pasture
# difference between saltbush and pastureof for each VI
f_diff<-function(data,var){
#require(Hmisc)
#print(describe(data$VEG))
a<-t.test(data[data$VEG==0,var],data[data$VEG==1,var])
return(c(var,a$p.value))
}
diff_11<-sapply(vars[-length(vars)],f_diff,data=data_11_f)
diff_11<-data.frame(t(diff_11))
diff_11[[2]]<-as.numeric(as.character(diff_11[[2]]))
names(diff_11)<-c("VIs","P")
print(diff_11,digits=4)## VIs P
## BLUE BLUE 0.000e+00
## GREEN GREEN 0.000e+00
## RED RED 0.000e+00
## NIR NIR 7.452e-143
## NDVI NDVI 0.000e+00
## RVI RVI 0.000e+00
## SAVI SAVI 0.000e+00
## GCC GCC 0.000e+00
## EG EG 0.000e+00
## FVC FVC 0.000e+00# difference of VIS between 2011 and 2010
f_diff_sea<-function(data1,data2,var){
#require(Hmisc)
#print(describe(data1$VEG))
#print(describe(data2$VEG))
a0<-t.test(data1[data1$VEG==0,var],data2[data2$VEG==0,var])
a1<-t.test(data1[data1$VEG==1,var],data2[data2$VEG==1,var])
return(c(var,a0$p.value,mean(data1[data1$VEG==0,var]),mean(data2[data2$VEG==0,var]),a1$p.value,mean(data1[data1$VEG==1,var]),mean(data2[data2$VEG==1,var])))
}
diff_sea<-sapply(vars[-length(vars)],f_diff_sea,data1=data_10_f,data2=data_11_f)
diff_sea<-data.frame(t(diff_sea))
for(i in c(2:7)){diff_sea[[i]]<-as.numeric(as.character(diff_sea[[i]]))}
names(diff_sea)<-c("VIs","P_soil","mean_10_soil","mean_11_soil","P_saltbush","mean_10_veg","mean_11_veg")
print(diff_sea,digits=4) ## VIs P_soil mean_10_soil mean_11_soil P_saltbush mean_10_veg
## BLUE BLUE 0.000e+00 884.7444 1331.60403 0.000e+00 725.1085
## GREEN GREEN 0.000e+00 1263.7646 1531.27492 4.041e-270 1077.3630
## RED RED 0.000e+00 1438.1830 1864.73045 0.000e+00 1110.4405
## NIR NIR 0.000e+00 1866.9595 2327.47008 0.000e+00 1912.4932
## NDVI NDVI 0.000e+00 0.1306 0.10995 5.128e-73 0.2598
## RVI RVI 0.000e+00 1.3057 1.24990 2.725e-56 1.7940
## SAVI SAVI 0.000e+00 0.1959 0.16491 4.573e-73 0.3896
## GCC GCC 0.000e+00 0.3509 0.32245 0.000e+00 0.3685
## EG EG 0.000e+00 204.6019 -133.78463 0.000e+00 319.1769
## FVC FVC 6.058e-248 0.1219 0.08286 2.559e-23 0.4790
## mean_11_veg
## BLUE 977.5688
## GREEN 1215.8140
## RED 1387.8080
## NIR 2506.9705
## NDVI 0.2844
## RVI 1.9035
## SAVI 0.4266
## GCC 0.3382
## EG 66.2512
## FVC 0.5145 P_veg<-summary(as.factor(data_11_f$VEG))/length(data_11_f$VEG)
names(P_veg)<-c("Nonveg","Veg")
print(P_veg)## Nonveg Veg
## 0.4647747 0.5352253#----------------------2.11.2 Anova two-way test between saltbush and pasture
Anova_data<-rbind(cbind(data_11_f,YEAR=2011),cbind(data_10_f,YEAR=2010))
Anova_data$YEAR<-as.factor(Anova_data$YEAR)
Anova_data$VEG<-as.factor(Anova_data$VEG)
Anova_data$group<-NA
# define different groups
Anova_data$group[Anova_data$YEAR==2010 & Anova_data$VEG==0]<-"A"
Anova_data$group[Anova_data$YEAR==2010 & Anova_data$VEG==1]<-"B"
Anova_data$group[Anova_data$YEAR==2011 & Anova_data$VEG==0]<-"C"
Anova_data$group[Anova_data$YEAR==2011 & Anova_data$VEG==1]<-"D"
Anova_data$group<-as.factor(Anova_data$group)
summary(Anova_data)## ID Site VEG BLUE GREEN
## Min. : 52551 Min. : 1.0 0:27906 Min. : 275.0 Min. : 239
## 1st Qu.:360487 1st Qu.: 6.0 1:32136 1st Qu.: 715.0 1st Qu.: 985
## Median :638706 Median :13.0 Median : 903.0 Median :1255
## Mean :574939 Mean :12.5 Mean : 970.7 Mean :1263
## 3rd Qu.:788819 3rd Qu.:19.0 3rd Qu.:1201.0 3rd Qu.:1510
## Max. :996072 Max. :24.0 Max. :2628.0 Max. :2490
## RED NIR NDVI mNDVI
## Min. : 289 Min. : 491 Min. :-0.08606 Min. :-39.0000
## 1st Qu.:1114 1st Qu.:1756 1st Qu.: 0.11549 1st Qu.: 0.2795
## Median :1403 Median :2089 Median : 0.16071 Median : 0.3757
## Mean :1436 Mean :2157 Mean : 0.20155 Mean : 0.4174
## 3rd Qu.:1733 3rd Qu.:2505 3rd Qu.: 0.25249 3rd Qu.: 0.5400
## Max. :3186 Max. :5078 Max. : 0.77447 Max. : 4.6471
## RVI REEI EVI SAVI
## Min. :0.8415 Min. :0.1271 Min. :-143.1818 Min. :-0.1290
## 1st Qu.:1.2611 1st Qu.:0.5968 1st Qu.: 0.2855 1st Qu.: 0.1732
## Median :1.3830 Median :0.7231 Median : 0.4264 Median : 0.2410
## Mean :1.5834 Mean :0.6794 Mean : 0.5608 Mean : 0.3023
## 3rd Qu.:1.6756 3rd Qu.:0.7929 3rd Qu.: 0.7229 3rd Qu.: 0.3787
## Max. :7.8678 Max. :1.1883 Max. : 270.0000 Max. : 1.1615
## MSAVI OSAVI mARI1 mARI2
## Min. :0.3119 Min. :-0.08605 Min. :0.4095 Min. : 299.2
## 1st Qu.:0.7070 1st Qu.: 0.11548 1st Qu.:0.8171 1st Qu.:1535.0
## Median :0.7769 Median : 0.16071 Median :0.8721 Median :1861.5
## Mean :0.8205 Mean : 0.20154 Mean :0.8879 Mean :1924.1
## 3rd Qu.:0.9031 3rd Qu.: 0.25247 3rd Qu.:0.9479 3rd Qu.:2261.6
## Max. :1.3729 Max. : 0.77443 Max. :1.5722 Max. :5524.6
## GCC EG FVC YEAR
## Min. :0.2153 Min. :-1034.0 Min. :-0.40205 2010:30021
## 1st Qu.:0.3273 1st Qu.: -72.0 1st Qu.: 0.08868 2011:30021
## Median :0.3444 Median : 114.0 Median : 0.20654
## Mean :0.3456 Mean : 119.6 Mean : 0.31347
## 3rd Qu.:0.3628 3rd Qu.: 293.0 3rd Qu.: 0.44608
## Max. :0.4616 Max. : 1236.0 Max. : 1.72677
## group
## A:13953
## B:16068
## C:13953
## D:16068
##
## ano_result<-c(NA)
f_anov<-function(data,var){
.a<-anova(lm(data[[var]] ~ group, data))
m1<-aov(data[[var]] ~ group,data=data)
.c<-TukeyHSD(m1)
return(c(var,.c$group[,4]))
}
anov_result<-sapply(vars[-length(vars)],f_anov,data=Anova_data)
anov_result<-data.frame(t(anov_result))
for(i in c(2:7)){anov_result[[i]]<-as.numeric(as.character(anov_result[[i]]))}
names(anov_result)[1]<-c("VIs")
print(anov_result,digits=4) ## VIs B.A C.A D.A C.B D.B D.C
## BLUE BLUE 0.000e+00 0.000e+00 0 0 0 0
## GREEN GREEN 0.000e+00 0.000e+00 0 0 0 0
## RED RED 0.000e+00 0.000e+00 0 0 0 0
## NIR NIR 1.353e-12 0.000e+00 0 0 0 0
## NDVI NDVI 0.000e+00 0.000e+00 0 0 0 0
## RVI RVI 0.000e+00 3.719e-14 0 0 0 0
## SAVI SAVI 0.000e+00 0.000e+00 0 0 0 0
## GCC GCC 0.000e+00 0.000e+00 0 0 0 0
## EG EG 0.000e+00 0.000e+00 0 0 0 0
## FVC FVC 0.000e+00 0.000e+00 0 0 0 02.12 Statistic for all site
.linshi<-melt(Anova_data[c(2,4:21)],id=c(1,19))
site_sta<-dcast(.linshi,Site+YEAR~variable,mean)
site_sta<-merge(site_sta,AGB,by="Site",all.y=T)
site_sta<-site_sta[c("Density","Name","YEAR","NDVI","RVI","SAVI","GCC","EG","FVC","Ct")]
site_sta<-arrange(site_sta,desc(Density),Name,YEAR)
print(site_sta[-1],digits=3)## Name YEAR NDVI RVI SAVI GCC EG FVC Ct
## 1 S1An1LD 2010 0.128 1.30 0.191 0.351 196.4 0.1135 19.3
## 2 S1An1LD 2011 0.127 1.30 0.190 0.327 -58.8 0.1242 19.3
## 3 S1An2LD 2010 0.114 1.26 0.170 0.353 227.7 0.0750 18.1
## 4 S1An2LD 2011 0.111 1.26 0.166 0.327 -85.4 0.0854 18.1
## 5 S1An3LD 2010 0.224 1.60 0.336 0.373 355.7 0.3796 26.8
## 6 S1An3LD 2011 0.143 1.35 0.214 0.330 -44.5 0.1644 26.8
## 7 S2An1LD 2010 0.158 1.38 0.237 0.363 314.7 0.1979 32.9
## 8 S2An1LD 2011 0.154 1.38 0.231 0.327 -62.3 0.1913 32.9
## 9 S2An2LD 2010 0.135 1.32 0.203 0.362 293.4 0.1346 41.7
## 10 S2An2LD 2011 0.160 1.40 0.239 0.329 -43.9 0.2055 41.7
## 11 S2An3LD 2010 0.139 1.33 0.209 0.355 232.7 0.1462 18.8
## 12 S2An3LD 2011 0.138 1.34 0.207 0.327 -73.1 0.1527 18.8
## 13 S1An1HD 2010 0.165 1.40 0.247 0.362 309.0 0.2159 23.8
## 14 S1An1HD 2011 0.131 1.31 0.197 0.331 -36.7 0.1353 23.8
## 15 S1An2HD 2010 0.135 1.32 0.203 0.361 298.6 0.1352 14.8
## 16 S1An2HD 2011 0.111 1.25 0.166 0.327 -83.2 0.0850 14.8
## 17 S1An3HD 2010 0.202 1.52 0.303 0.371 390.0 0.3185 22.1
## 18 S1An3HD 2011 0.127 1.30 0.191 0.332 -28.1 0.1257 22.1
## 19 S2An1HD 2010 0.131 1.30 0.196 0.352 192.1 0.1230 42.5
## 20 S2An1HD 2011 0.150 1.36 0.224 0.329 -37.7 0.1810 42.5
## 21 S2An2HD 2010 0.119 1.27 0.179 0.353 209.1 0.0905 24.5
## 22 S2An2HD 2011 0.132 1.31 0.198 0.329 -46.1 0.1383 24.5
## 23 S2An3HD 2010 0.142 1.33 0.213 0.356 232.6 0.1536 39.3
## 24 S2An3HD 2011 0.153 1.38 0.230 0.331 -16.3 0.1905 39.33 Write result
site_sta[4:8]<-round(site_sta[4:8],3)
names(site_sta)<-c("Density","Plot name","Year","NDVI","RVI","SAVI","FVC","Sequestered carbon (C, t/ha)")
write.table(site_sta[-1],"results/site_sta.txt",sep="&",row.names = F)
Rho[2:5]<-round(Rho[2:5],3)
write.table(Rho,"results/Rho.txt",sep="&",row.names = F)
diff_sea[-1]<-round(diff_sea[-1],3)
write.table(diff_sea[c(1,3,6,4,7)],"results/diff_sea.txt",sep="&",row.names = F)