Allen:

The brute force way would be to simply pull out each pixel via ?getValues
(be aware, this is a memory intensive process) -- or use a subsample the
raster using ?sampleRandom.

Just as a note: brightness temperature is not the same thing as surface
temperature.  Brightness temperature assumes a blackbody.  While most dense
vegetation has a fairly high emissivity (~.98: approaching a blackbody),
soil does not, so low NDVI values, which are (likely) high soil sites, must
be treated somewhat differently.  There are some papers floating around on
how to use NDVI to predict emissivity -- you might want to look into those.

--j


On Fri, Nov 8, 2013 at 10:43 PM, Allen McIlwee <amcilwee@westnet.com.au>wrote:

> Hi Sarah
>
> I'm a student at Flinders University in South Australia looking at ways to
> support the adaptive land management of grazing lands in the north-west
> corner of the State. I am interested in how we can use various
> multi-temporal image analysis techniques to enhance our understanding of
> ecosystem dynamics and landscape processes.
>
> I have over 100 cloud free Landsat images for one scene I have a field trip
> out to next week. There are 5-6 images per year on average dating back to
> 1986. Each of the images has been processed by the ESPA who use the Landsat
> Ecosystem Disturbance Adaptive Processing System (LEDAPS) to calculate
> at-surface reflectance. I think the level of processing for these products
> is 2A? The thermal band is given as brightness temperature.
>
> I have a subset of the images I am working with at the moment which
> represents the single driest image in every year. I am working with a bunch
> of indices, all calculated in ERDAS imagine that can act as proxies for the
> dynamic processes I am trying to understand. In terms of landscape drying
> out and rehydration processes, I want to compare a number of indices
> including the Tassel Cap Wetness Index, Normalised Difference Moisture
> Index, Normalised Soil moisture Index, and the soil moisture index
> mentioned
> below.
>
> I can import the surface temperature & ndvi images into R by:
>
> rm(list=ls())
>
> library(raster)
> library(sp)
> library(rgdal)
>
> setwd("D:\\SMI\\")
>
> ndvi <- raster("a_1987_298_l5_ndvi3.img", format="HFA", datatype='FLT4S',
> NAflag=0)
> projection(ndvi) <- "+proj=utm +zone=53 +south +ellps=WGS84 +datum=WGS84
> +units=m +no_defs"
> ndvi
>
> class       : RasterLayer
> dimensions  : 6450, 6874, 44337300  (nrow, ncol, ncell)
> resolution  : 30, 30  (x, y)
> extent      : 217875, 424095, 6867295, 7060795  (xmin, xmax, ymin, ymax)
> coord. ref. : +proj=utm +zone=53 +south +ellps=WGS84 +datum=WGS84 +units=m
> +no_defs +towgs84=0,0,0
> data source : D:\SMI\a_1987_298_l5_ndvi3.img
> names       : a_1987_298_l5_ndvi3
> values      : -45.92834, 49.92658  (min, max)
>
> band6 <- raster("a_1987_298_L5_band6.img", format="HFA", datatype='FLT4S',
> NAflag=0)
> projection(band6) <- "+proj=utm +zone=53 +south +ellps=WGS84 +datum=WGS84
> +units=m +no_defs"
> band6
>
> class       : RasterLayer
> dimensions  : 6450, 6874, 44337300  (nrow, ncol, ncell)
> resolution  : 30, 30  (x, y)
> extent      : 217875, 424095, 6867295, 7060795  (xmin, xmax, ymin, ymax)
> coord. ref. : +proj=utm +zone=53 +south +ellps=WGS84 +datum=WGS84 +units=m
> +no_defs +towgs84=0,0,0
> data source : D:\SMI\a_1987_298_L5_band6.img
> names       : a_1987_298_L5_band6
> values      : 2497, 4259  (min, max)
>
> The frequency distribution of unique NDVI values that I want to calculate a
> min and max brightness temperature is
>
> freq(ndvi)
>
> value    count
>  [1,]   -46        1
>  [2,]   -33        1
>  [3,]   -31        1
>  [4,]   -28        1
>  [5,]   -25        1
>  [6,]   -23        3
>  [7,]   -21        2
>  [8,]   -19        1
>  [9,]   -18        4
> [10,]   -15        3
> [11,]   -13        1
> [12,]   -12        1
> [13,]   -11        2
> [14,]   -10        2
> [15,]    -9        3
> [16,]    -8        3
> [17,]    -7        4
> [18,]    -6        4
> [19,]    -5        6
> [20,]    -3        4
> [21,]    -2        8
> [22,]    -1       12
> [23,]     0 11386427
> [24,]     1       28
> [25,]     2       23
> [26,]     3       39
> [27,]     4      113
> [28,]     5      539
> [29,]     6     2663
> [30,]     7     8683
> [31,]     8    25549
> [32,]     9    81499
> [33,]    10   250318
> [34,]    11  1202842
> [35,]    12  3572881
> [36,]    13  6462049
> [37,]    14  7941048
> [38,]    15  6749783
> [39,]    16  3972814
> [40,]    17  1729160
> [41,]    18   612375
> [42,]    19   204610
> [43,]    20    71817
> [44,]    21    27397
> [45,]    22    14043
> [46,]    23     7939
> [47,]    24     4418
> [48,]    25     2790
> [49,]    26     1805
> [50,]    27     1158
> [51,]    28      773
> [52,]    29      487
> [53,]    30      339
> [54,]    31      206
> [55,]    32      163
> [56,]    33      109
> [57,]    34       74
> [58,]    35       74
> [59,]    36       38
> [60,]    37       42
> [61,]    38       30
> [62,]    39       21
> [63,]    40       19
> [64,]    41       19
> [65,]    42        3
> [66,]    43        6
> [67,]    44        4
> [68,]    45        4
> [69,]    46        3
> [70,]    47        3
> [71,]    48        2
> [72,]    49        2
> [73,]    50        1
>
> Not sure where to go from here
>
> Thanks
> Allen
>
> -----Original Message-----
> From: Sarah Goslee [mailto:sarah.goslee@gmail.com]
> Sent: 09 November 2013 08:00
> To: Allen McIlwee
> Cc: r-sig-geo@r-project.org
> Subject: Re: [R-sig-Geo] Landsat soil moisture index
>
> Hi,
>
> Well, you need to start at the beginning.
> Where did you get your Landsat images?
> What format are they in?
> Has someone already calculated NDVI and surface temperature for you, or do
> you need to do that? If so, what level of processing have the images
> already
> received?
> How have you imported the images into R?
>
> Do you already know how to perform a linear regression in R?
>
> It's not a difficult task programmatically, but there are a number of steps
> that you need to consider, and things you must understand before you can
> get
> good results. Remote sensing isn't necessarily an easy field.
>
> The landsat package and associated references may help you get started.
>
> Sarah
>
> On Fri, Nov 8, 2013 at 8:52 AM, Allen McIlwee <amcilwee@westnet.com.au>
> wrote:
> > Dear list
> >
> >
> >
> > WANG et al. 2009 "Satellite remote sensing applications for surface
> > soil moisture monitoring" derived a Landsat soil moisture index:
> >
> >
> >
> > SMI = (Tsmax - Ts) / (Tsmax - Tsmin)
> >
> >
> >
> > where Tsmax, Tsmin are the maximum and minimum surface temperatures
> > for a given NDVI value, and Ts is the surface temperature at a given
> > pixel for a given NDVI. Values near 1 (low surface temp & low
> > greenness) have the highest estimated soil moisture, while values near
> > 0 (high surface temp &
> > greenness) have the lowest.
> >
> >
> >
> > Could anyone please advise me how I can create an image matrix that
> > will extract the min and max surface temperatures for each unique NDVI
> > value. I then need to apply a linear regression to the max temp vs
> > NDVI & min temp vs NDVI scatter plots, and use the corresponding
> > slopes and intercepts to calculate Tsmax and Tsmin
> >
> >
> >
> > Tsmax = (a1 x NDVI) + b1
> >
> > TSmin = (a2 x NDVI) + b2
> >
> >
> >
> > Hoping this is not such a difficult task for someone mathematically
> > inclined
> >
> >
> >
> > Thanks
> >
> > Allen
> >
> >
>
> --
> Sarah Goslee
> http://www.functionaldiversity.org
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>



-- 
Jonathan A. Greenberg, PhD
Assistant Professor
Global Environmental Analysis and Remote Sensing (GEARS) Laboratory
Department of Geography and Geographic Information Science
University of Illinois at Urbana-Champaign
259 Computing Applications Building, MC-150
605 East Springfield Avenue
Champaign, IL  61820-6371
Phone: 217-300-1924
http://www.geog.illinois.edu/~jgrn/
AIM: jgrn307, MSN: jgrn307@hotmail.com, Gchat: jgrn307, Skype: jgrn3007

	[[alternative HTML version deleted]]

