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# Environ Remote Sensing ERS 186

UCD

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This 31 page Class Notes was uploaded by Dan Skiles IV on Tuesday September 8, 2015. The Class Notes belongs to ERS 186 at University of California - Davis taught by Staff in Fall. Since its upload, it has received 28 views. For similar materials see /class/187674/ers-186-university-of-california-davis in Environmental Resource Science at University of California - Davis.

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Date Created: 09/08/15

Field Sampling Methods Error and Limitations in Remote Sensing Our ability to asses the accuracy of remote sensing products is largely determined by our ability to validate remote sensing estimates with accurate ground measurements Outline Field sampling methods 1 Quadrat sampling 2 Plotless density estimators Estimating error 1 Pilot study 2 How many plots Measuring LAI 0r biomass 1 Destructive sampling 2 Allometric relationships 3 Gap fraction sensors Relating ground data to RS data 1 Regression Error Propagation into Land Surface Models Field Sampling Methods Questions How many Density per unit area What kind Identi cation Characteristics Size shape cover etc Field Sampling Methods Quadrats plot of a xed size in which density of objects can be measured Ancillary information such as type of objects size and shape can be measured as wellThe plots are usually circular or square in shape but can be other shapes as well The main goal is that we want to know the number of objects per unit area density GSA Field Sampling Methods Considerations in Quadrat Sampling What shape should the quadrats be Choice for the shape of a quadrat is generally associated with the ease of implementation and has minimal impact on the results of the sampling For example it may be easier to delineate a circular plot in low lying vegetation using a center post than walking out the boundaries of a square plot What is the difference between a square plot with X m2 area and a circular plot with X m2 area Field Sampling Methods Considerations in Quadrat Sampling 2 What Size should the quadrats be The size of the quadrats in relation to the size of the objects measured is a critical consideration Choosing quadrat sizes that are too small with regards to the size of the objects being measured will negatively affect the sampling precision and accuracy While choosing quadrat sizes that are too large will result in excess work Field Sampling Methods Considerations in Quadrat Sampling 3 How should I distribute the quadrats over the area of interest Strati ed random random regular subjectlve o 39 o o o C C o o o o o Field Sampling Methods Considerations in Quadrat Sampling 4 How many quadrats should I place in the area of interest The number of quadrats along with their size are the most important of the considerations In terms of experimental design each quadrat plot is treated as a replicate The more replicates the better in terms of increasing the precision of estimates We Will return to this topic later in the lecture Field Sampling Methods Plotless Density Estimators PDE Plotless density estimators PDE were developed in order to overcome the limitations of xed plot sampling strategies as well as reduce the amount of manhours necessary for sampling There are no xed plots to delineate In contrast to quadrat techniques which measure the number of organisms per unit area PDE s attempt to estimate the mean area per organism the inverse of density This allows for the use of spacing between organisms to be used in determining mean area per organism Consequently density can be calculated given the mean distance between organisms Cottam and Curtis 1956 Field Sampling Methods Plotless Density Estimators PDE The mean distance between organisms is determined either by measuring the distance from 11 random points to the rth closest organism closest individual methods or by measuring the distance om one organism to its rth closest neighbor nearest neighbor methods In addition to density estimates ancillary data such as the type size shape etcof organisms can be recorded Field Sampling Methods Example Closest Individual Methods Point Center Quarter II I I Measured distances r 4 l I I I I l l I I I I Randomly located point along transect Mean area per organism mean distance to nearest r individuals2 Density unit area mean area per organism Field Sampling Methods Example Closest Individual Methods Variable Area Transect Distances measured 1 while walking quot transect r 3 39 x I Randomly located sample point along transectgt X Density nr 1 W Z Xi Where n number of sample points r number of distances between organisms measured at each sample point W Width of transect Xi distance between organisms Field Sampling Methods Comparison of Quadrat Vs PDE s Quadrats Designed for more intensive measurements over smaller spatial extent Can be made permanent so you can revisit location good for change detection results are dependent on the size of quadrat in relation to organism size and spatial distribution of organisms PDE Suitable for less intensive measurements over larger spatial extent RS Measurements are quick and you can cover a lot of area Assume random distribution of organisms error in estimates increase as distribution becomes less random Estimating Error Question How much error is in my estimates and how can I reduce the error Precision sensitivity or amount of information of a sample is measured as the reciprocal of the sample variance of a mean I1szyns2 1 I precision s2 the sample variance of a mean the sample variance number of samples 3 4 III From equation 1 it is apparent that as sample number increases in the numerator precision increases Similarly the sample variance s2 in the denominator is inversely proportional to the sample size and decreases as sample number increases Estimating Error Accuracy is associated With the concepts of bias or systematic error in measurement and is in uenced by the procedure of taking measurements or the instrument of measure itself While precision increases With larger sample sizes accuracy does not necessarily follow in suit Estimating Error Question How many samples quadrats or PDE samples should I measure to achieve a certain level of error required sample size at a given sample error level is determined by n s t e 2 2 where n sample size s standard deviation of samples t tvalue for a twotailed test with n1 degrees of freedom at the 95 confidence level e acceptable error in terms of a percent of the mean How do you know what the standard deviation is when you have not sampled yet Pilot Study go out and determine how variable the system is beforehand Estimating Error Question If Ive already sampled an area can I determine the error associated with my estimate of the mean Equation 2 can be rearranged to solve for the estimated error level e at a given sample size e st 01 2 Ground Samples Image Samples 0 Spacin cvrglfi l 0f Error level at CV o of ratio Error level at 9 level weipht delg sity n9 vegetation index n9 m 9 ole RVI ole 091 167 104 20 17 152 220 147 2393 2390 Measuring LAI or Biomass Remote sensing estimates are only as good as the ground measurements they are related to Question How can we quantify the LAI or biomass within a given area How good are these estimates Measuring LAI or Biomass Destructive Sampling In shortstature ecosystems eg agricultural crops grasslands shrublands direct estimates of leaf area can be obtained using area harvesting Area harvesting involves the destructive sampling of vegetation Within plots located Within a vegetation community The Widespread utility of this method is limited however by the laborintensive nature of these types of measurements as well as the number of plots needed to capture the spatial heterogeneity of a particular ecosystem Measuring LAI or Biomass Allometric Methods Allometry relates the size of one structure in an organism to the size or amount of another structure in the same organism Example the diameter of a trunk of a tree is related to the amount of leaf area or biomass in the tree Trunk or stem diameters are relatively easy to measure While leaf area is not Measuring LAI or Biomass 00035 00030 y 01143x 00004 R2 09396 00025 00020 00015 00010 total leaf area391 1cm2 00005 00000 I I I I I 0000 0005 0010 0015 0020 0025 0030 shoot length 391 cm39l Figure 2 Relationship between total leaf area and shoot length Both variables were transformed using a reciprocal function Points represent total leaf area primary lateral as a function of shoot length for 50 shoots harvested from study area Data were fitted to a linear function Measuring LAI or Biomass Developing allometric equations related to leaf area and biomass for a particular site requires destructive sampling Consequently investigators commonly use published allometric equations for speci c plant communities Unfortunately allometric coef cients vary between sites and species due to a number of environmental variables As a result the use of generalized equations can lead to signi cant errors in vegetation parameter estimations Grier et al 1984 cited in Gower et al 1999 found that generalized allometric equations produced errors in biomass estimates ranging from 8 to 93 as compared With sitespeci c equations Measuring LAI or Biomass Indirect techniques Gap Fraction Sensors Indirect methods of estimating LAI include canopy gap fraction measurements which are based on an interactive relationship between canopy structure and radiation interception These optical techniques measure the gap fraction ie the proportion of transmitted light which is not blocked by foliage in a band of azimuthal directions Leaf area is then estimated using canopy models with the gap fraction as an input parameter Measuring LAI or Biomass 76 2 Xp k9 Fraction of incident Extlnctlon coef c1ent Solve for this beam radiation from a speci c zenith angle that penetrates the canopy Assumes random distribution of leaves and no light interception by woody elements This is often not the case Measuring LAI or Biomass Indirect techniques Gap Fraction Sensors It is estimated that Violating the random foliage distribution assumption can lead to errors in LAI estimation in excess of 100 Fassnacht et a1 1994 Measuring LAI or Biomass A comparison of direct area harvest and allometry and indirect gap fraction estimates of LAI across a Wide variety of ecosystems showed that the two methods compare to Within 25 30 for most canopy types Gower et a1 1999 This is bad So in terms of taking ground measurements of LAI or Biomass for forests there is m of potential error Relating Ground Data to Remote Sensing Data Question Given the potential error in ground measurements how can we validate remote sensing products Well good question Im not certain I have the answer but we can reevaluate what we currently do now Relating Ground Data to Remote Sensing Data Linear Regression Linear regression assumes that we know the independent variable ie we don t take into account error in our estimate of the independent variable This is problematic in that we often treat ground measurements as independent variables and RS data as dependent variables Our uncertainty levels for ground based measurements are often much greater than the uncertainty levels from RS data Error Propagation into Land Surface Models Given the increased reliance of landsurface biophysical and biogeochemical models on remote sensing inputs a logical question is How sensitive are these models to errors in estimates of LAI and NDVI There are surprisingly few studies that address this question explicitly despite the importance of this type of analysis in assessing the accuracy of model predicted estimates of biospheric function Error Propagation into Land Surface Models 1 The sensitivity of a coupled biosphereatmosphere model NCAR CCM2 BATS land surface parameterization to global changes in LAI was examined by Chase et al 1996 A global decrease in LAI of 208 resulted in a 121 increase in sensible heat ux and a 48 decrease in latent heat ux in the months of January and July In a similar study Bounoua et al 2000 examined the sensitivity of a coupled biosphereatmosphere model SIB2CSU GCM to maximum and minimum distributions of NDVI determined from an eight year period of satellite records They showed that a 01 absolute increase in NDVI approximately 17 relative difference resulted in a 46 increase in FPAR a 42 increase in gross photosynthetic CO2 uptake and a 18K cooling in the northern latitudes during the growing season Error Propagation into Land Surface Models Asner 2000 explicitly examined the effects of potential remote sensing error on the CASA model After determining a plausible range of NDVI values om a perturbation analysis he showed that annually estimated values of NPP from the CASA model showed signi cant sensitivity to NDVI with errors of up to 30 in modeled NPP values due to plausible errors in NDVI estimation Similar results were reported by Kaufman and Holben 1993 who showed that NDVI errors of only 5 caused by instrument calibration resulted in errors up to 30 in annual NPP estimation

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