4.2 Hyperspectral Remote Sensing Processing
4.2.3 Object-based tree species classification using hyperspectral
4.2.3.1 Development of the eCognition workflow
The process can be divided into four main areas, these are: (i) the construction of a forest area mask; (ii) the delineation of individual tree crown objects; (iii) the broad classification of coniferous and deciduous areas; and (iv) the more specific classification of tree species types. A combination of leaf-on and leaf-off geo-corrected hyperspectral MNF image-bands and a leaf-on DR LiDAR-derived canopy height model (CHM) (this dataset is defined in section 4.3.3) were added together into an image stack and used for this approach. Table 4.7 summarises all these input datasets. Those hyperspectral MNF image-bands which were chosen contained the most distinction between ground cover types and contained the fewest artefacts.
The object-based classification algorithms were developed in the ‘Cognition Network Language’ (CNL). CNL is a modular programming language allowing typical programming tasks such as branching, looping and variable definition (Tiede et al., 2006). Thus, these modular algorithms can be combined to form a complete ‘ruleware’ for automated information extraction. A workflow was thus devised for the identification of the forest area, the creation of ITC segments and species classification of each object within a class hierarchy.
A class hierarchy is defined broadly as a series of interrelated classes, which form a series of parent and child classes. Only specific child classes can be selected based upon the parent class.
Table 4.7 – Hyperspectral data derived image input list
Dataset Name: Description:
LiDAR CHM (leaf-on) LiDAR derived CHM (leaf-on). 1.1x1.1m pixel size
Leaf-on MNF 1 Hyperspectral data derived MNF band 1 from leaf-on eagle data Leaf-on MNF 2 Hyperspectral data derived MNF band 2 from leaf-on eagle data Leaf-on MNF 3 Hyperspectral data derived MNF band 3 from leaf-on eagle data Leaf-on MNF 4 Hyperspectral data derived MNF band 4 from leaf-on eagle data Leaf-on MNF 5 Hyperspectral data derived MNF band 5 from leaf-on eagle data Leaf-on MNF 6 Hyperspectral data derived MNF band 6 from leaf-on eagle data Leaf-off MNF 1 Hyperspectral data derived MNF band 1 from leaf-off eagle data Leaf-off MNF 2 Hyperspectral data derived MNF band 2 from leaf-off eagle data Leaf-off MNF 3 Hyperspectral data derived MNF band 3 from leaf-off eagle data Leaf-off MNF 4 Hyperspectral data derived MNF band 4 from leaf-off eagle data Leaf-off MNF 5 Hyperspectral data derived MNF band 5 from leaf-off eagle data Leaf-off MNF 6 Hyperspectral data derived MNF band 6 from leaf-off eagle data 4.2.3.2Creation of the forest mask
The forest mask process used only the CHM raster layer as input. This process began with the use of a contrast-split segmentation, where the raster was segmented based upon the contrast between high and low regions (Trimble Navigation LTD., 2012). Matthews and Mackie (2006) broadly define saplings as tree species which are below 1.3m in height; heights above this threshold are considered as trees. Thus, raster grid-cell values above a height threshold value of 1.3m were considered forest. Initial segments were created around these regions classifying high (>1.3m) areas as forest and low (≤1.3m) areas as non-forest.
What followed was an additional optional step to classify any segments which remained unclassified based upon the mean height values of the object. The final operations were to merge all forest or non-forest segments. This step created the first level of the class hierarchy, forested and non-forested areas.
4.2.3.3 Individual tree crown delineation
The method used to segment the data into ITC objects is a modification of the approach presented in Bunting and Lucas (2006), where the crown-centroids were detected through the use of a LiDAR-derived CHM instead of spectral data. Tree tops or ‘local maxima’ were detected by a moving search window, once found these maxima cells were reclassified to crown seeds. Further iterative steps were then implemented for growing ITC objects around these seed points into grid-cells of lower elevation using a progressive set of rules to
The CHM raster layer height values were used as input for the ITC delineation procedure. The first step was to identify any isolated tree crowns from the application of the forest mask; i.e. these trees could exist separately from larger forested areas and would be surrounded by non-forest objects. This was performed by identifying these forest objects based on size and shape parameters of the object segment (e.g. area, elliptic fit, length/width ratios, and roundness). If the tree crown shape and size criteria were met these objects were given the class of ‘tree crown’, and not considered in further delineation steps.
The larger forest segments, or clusters of trees, were visited and processed iteratively. The first process within each iteration was to perform a chessboard segmentation, effectively separating that cluster region into its individual raster grid-cells (1x1m), or rather each raster grid-cell in a forest classified area had a corresponding object. An iterative procedure was then implemented to delineate ITC objects. In theory, tree crowns form ‘dome’ shapes within the CHM raster surface, where the higher (brighter)dome tops correspond with the crown tops. Therefore by identifying these tops as seeds and expanding the seeds into neighbouring areas of lower height the ITCs were delineated. ITC object growing could also halt in a particular direction if it came into contact with other seed or crown object boundaries. The ‘find local extrema’ algorithm was used to find the highest points within a mobile initial search window size with a radius of 5m.What followed was an iterative expansion of tree crown objects from the seed points, where 1x1m objects bordering the seed point of the same or lower height were merged, and the process repeated until height values bordering the object increased or the object boundaries came into contact with other ITC objects or non- forested areas. This process was applied to each of the forest cluster objects.
The final stage was to merge any remaining unclassified forest or crown objects (that wereleft where no crown seeds have been identified)and then re-apply the isolated tree crown identification procedure using the aforementioned criteria (area, elliptic fit, length/width ratios and roundness). Those objects which remained unidentified were assessed individually by the user.
The result of this processing is a map of overstorey tree crown locations, see Figure 4.7.It should be noted that this was a very computationally intensive process, taking on average one hour for a single 1x1km area (at a spatial resolution of 1m).
Figure 4.7 – An example of the ITC segments created through the crown-centroid detection approach, overlaid on the CHM.
4.2.3.4 Broad tree species classification
A broad classification was then applied to the ITC segments to distinguish deciduous and coniferous species types. This was accomplished through a membership function applied to the mean of the groups of pixels intersecting each ITC object, which was applied to only forest classified objects. Values derived from a single hyperspectral image, MNF3 (leaf-on), was utilised to determine which areas belonged to the different broad species types. For the case of the MNF 3 (leaf-on) image, it was found that it best exemplified the difference between coniferous and deciduous species. Thus, the average pixel value was calculated for those pixels in the MNF3 (leaf-on) image intersecting each ITC segment. The classification was based upon membership function thresholds, where higher values (2 to 50) were classified as a coniferous ITC, whereas lower values (-40 to 2) were classified as deciduous.
region, whereas the latter related to the slight misalignment of LiDAR derived ITC objects and the hyperspectral image, often at the edge between forest and open areas, where non-tree pixels could skew the results of the classification. Both of these features could be determined by high pixel values (>50) in the leaf-on MNF3 image. These objects were then removed from the classification process.
This step formed the second level of the class hierarchy where forested areas became one of the three classes, coniferous, deciduous or man-made.
4.2.3.5 In-depth tree species classification
The next step was to add another level in the class hierarchy; a more detailed classification of tree species types within the existing coniferous and deciduous class contexts. Those species types the user would expect to occur within the study area, according to fieldwork and FC inventory data, were entered into the appropriate class hierarchy. For example the deciduous class was expanded to include many sub-classes such as oak, beech, silver birch, sweet chestnut, and holly. Likewise the coniferous class could be broken down into sub-classes such as scots pine, corsican pine, douglas fir, or norway spruce. All input hyperspectral MNF rasters (leaf-on and leaf-off) including those raster layers derived from LiDAR data could be used as an input to the classification process.
Owing to the presence of plantation woodland and the age differences in the tree species between stands an additional step was added to the classification: a separation of older and younger trees .Difference in ages within a tree species group could potentially result in a different hyperspectral signature within the MNF-bands. An additional classification step was applied to both the coniferous and deciduous classified ITC objects. A ‘height filter’ was used to classify those objects above a certain height threshold as ‘mature’ woodland (CHM >15m in height), whereas values below this threshold were classified as ‘immature’ woodland (CHM <15m in height). The choice of the threshold was determined by information provided in the Forestry Commission inventory data and unsatisfactory classification results whilst experimenting with the method. Recently planted tree species were typically below 15m in height and some of which exhibited different pixel values to those of the same species in other compartments.
A number of studies (e.g. Gougeon and Leckie, 2003; Leckie et al., 2005) have extracted tree spectra from the mean-lit (sunlit) sections of the proportions of the crown rather than individual pixels. MNF image values cannot be related to any spectral measurements because of how they were calculated. Information held within the MNF images is sufficient for classification purposes however (Onojeghuo and Blackburn, 2011). The dimensionally reduced Eagle datasets were then used to identify young and mature tree species types.
Each of the input images was interrogated by calculating the mean or maximum value of the image layer’s pixels which intersect each ITC object, creating a value for each. Classifying tree species from ITC objects is a complicated task due to the variability of each of the hyperspectral input data for each object. A number of membership functions were developed manually for each tree species class. Each species potentially utilise a number of input images, combinations of functions based on the relationships between that ITC object and its neighbours. Logical operators (e.g. AND, OR, NOT) were also used to account for conflicting or consistent features between different class membership functions. A full list of all classes and membership functions is listed in Table 4.8.
The final stage to complete within eCognition was to export the classified ITC layer, using the ‘export as vector’ function. The output file was set as an ESRI format shapefile, where each ITC object was converted to a polygon, and the species type added as an attribute in the linked database table. A total of 28 tree species classes were developed.
Table 4.8 – Class list and membership functions
Class name Class hierarchy(parent classes) Membershipfunction 1 Membershipfunction 2 Membershipfunction 3
Forest - N/A – assigned bysegmentation - -
Non-forest - N/A – assigned bysegmentation - -
Crown Forest height maxima searchN/A – assigned
and region growing - -
Coniferous Crown Difference in Leaf-onMNF image 3
(values: -40>x<2) - -
Deciduous Crown Difference in Leaf-onMNF image 3
(values: 2>x<50) - -
Manmade structures and
edge effects Crown
Difference in Leaf-on MNF image 3 (values: 50>x) - - Immature Coniferous Coniferous Maximum CHM value within object
(values: 1.3>x<15) - -
Mature
Coniferous Coniferous
Maximum CHM value within object
(values: 15>x<50) - -
Immature
Deciduous Deciduous
Maximum CHM value within object
(values: 1.3>x<15) - -
Mature
Deciduous Deciduous
Maximum CHM value within object
(values: 15>x<50) - -
(Young) corsicanpine (Pinusnigra)
Immature
Coniferous Mean leaf-on MNF 2(values -6>x<-1) - -
(Young) douglasfir (Pseudotsugamen
ziesii)
Immature
Coniferous Mean leaf-on MNF 2(values -13>x<-6)
<NOT>Mean leaf-off MNF 4 (values -16>x<0) <NOT>Mean leaf- on MNF 3 (values -11>x<11) (Young) grand fir
(Abiesgrandis) ConiferousImmature Mean leaf-on MNF 2(values -15>x<-6) MeanMNF 3 (values-1>x<11) (values -4>x<7)MeanMNF 4 (Young)
japaneselarch (Larixkaempferi)
Immature
Coniferous Mean leaf-off MNF 4(values -16>x<0) - -
(Young) hybrid larch (Larixeurolepis)
Immature
Coniferous Mean leaf-on MNF 5(values 2>x<7) - -
(Young) norwayspruce
(Piceaabies)
Immature
Coniferous Mean leaf-on MNF 4(values -1>x<7)
<NOT>Mean leaf-on MNF 3 (values -25>x<-14) <NOT>Mean leaf- on MNF 3 (values -1>x<11) (Young)scots pine (Pinussylvestris) Immature
Coniferous Mean leaf-on MNF 2(values -1>x<6) - -
(Young) western hemlock (Tsugaheterophyl
la)
Immature
Table 4.8 (continued) coast redwood
(Sequoia
sempervirens)
Mature
Coniferous Mean leaf-on MNF 2(values -15>x<-6) Mean leaf-on MNF 3(values -1>x<11)
Mean leaf-on MNF 4
(values -4>x<7) corsicanpine ConiferousMature Mean leaf-on MNF 2(values -1>x<20) - -
douglasfir ConiferousMature Mean leaf-on MNF 2(values -8>x<-2) Mean leaf-on MNF 3(values -7>x<0) - grand fir ConiferousMature Mean leaf-on MNF 2(values -15>x<-6) MeanMNF 3 (values-1>x<11) (values -4>x<7)MeanMNF 4 hybrid larch ConiferousMature Mean leaf-on MNF 5(values 2>x<7) - - japaneselarch ConiferousMature Mean leaf-off MNF 4(values -16>x<0) - - norwayspruce ConiferousMature Mean leaf-on MNF 4(values -1>x<7) <NOT>Mean leaf-onMNF 3
(values -25>x<-14)
<NOT>Mean leaf- on MNF 3 (values -1>x<11) scots pine ConiferousImmature Mean leaf-on MNF 2(values -1>x<6) - - lawsons cypress
(Chamaecyparisl
awsoniana)
Mature
Coniferous Mean leaf-off MNF 4(values 7>x<13) - -
western hemlock ConiferousMature Mean leaf-on MNF 3(values -25>x<-4) - - (Young) common
alder (Alnusglutinosa)
Immature
Deciduous Mean leaf-on MNF 4(values -13>x<-6) - -
(Young) oak
(Quercusrobur) DeciduousImmature Mean leaf-on MNF 4(values -5>x<2) - - (Young) beech
(Fagussylvatica) DeciduousImmature Mean leaf-on MNF 4(values 2>x<25) - - (Young) silver
birch (Betulapendula)
Immature
Deciduous Mean leaf-on MNF 4(values -20>x<-5) - -
(Young) sweet chestnut (Castaneasativa) Immature Deciduous Maximum pixel values of leaf-on MNF 3 (Values from 24 to 45) - -
common alder DeciduousMature Mean leaf-on MNF 4(values -13>x<-6) - - oak DeciduousMature Mean leaf-on MNF 3(values 1>x<20) <AND>Mean leaf-onMNF 4
(values -7>x<2) - beech DeciduousMature Mean leaf-on MNF 4(values 2>x<25) - - silver birch DeciduousMature Mean leaf-on MNF 4(values -20>x<-7) - -
sweet chestnut DeciduousMature
Maximum pixel values of leaf-on
4.2.3.6 Classification of 30x30m summary metrics
Area-based summaries of the classified ITC-objects were required for input into statistical modelling for the estimation of forest attributes. The resulting classified ITC map was then exported as a shapefile for use in ArcMap. Point-centroids were extracted from the ITC polygons using the GME software. Summary metrics were extracted for the 30x30m field- plot extent shapefiles (generated from the coordinates recorded in the two fieldwork operations) and for a regularly spaced grid of 30x30m shapefiles produced by the GME software for the whole study site. A spatial join operation was then performed with thefield- plot location shapefiles, and separately, for the whole study site.
Area based metrics were extracted and/or calculated for the 30x30m extents using custom R code (http://cran.r-project.org/), documented in Appendix B section B.3. This R script calculated the number of ITC objects, number of species and number of native species. A Shannon and Simpson index was calculated using the R package: Vegan (http://cran.r- project.org/web/packages/vegan/index.html)(Oksanenet al., 2012). A total of eight output metrics were derived, these were: (i) number of ITC objects which intersected the grid cell; (ii) number of native tree species classified objects which intersected the grid cell; (iii) number of tree species encountered; (iv) number of native tree species encountered (i.e. of the species: oak, beech, silver birch, scots pine, or common adler); (v) Shannon Index calculated from counts and species within the cell; (vi) Simpson Index calculated from counts and species within the cell; (vi) Evenness index, derived from the Shannon Index; (vii) a count of the ITC objects of the same species with the largest population relative to the others in the 30x30m extent; and (viii) a count of the ITC objects of the same species with the lowest population relative to the others in the 30x30m extent. The initial six metrics were extracted for the field plot areas and used for statistical analysis. The remaining two were required for use as input for the Complex Stand Diversity Index (Jaehne and Dohrenbusch, 1997 in Vorčák et al., 2006).
4.3 Discrete-return LiDAR processing
This section describes the pre-processing steps required in order to allow analysis with the LiDAR datasets.
The 2010 LAS files were delivered by the ARSF in a format in which the following processing steps could be directly applied. It should be noted that for each of the pre- processing steps listed in sections 4.3.1 – 4.3.3 both the leaf-on and leaf-off DR LiDAR datasets were processed independently of one another. Only leaf-on LiDAR data was utilised in the ITC delineation however.
The initial quality assessment provided by the ARSF prior to delivery of the datasets indicates the geometric accuracy of the LiDAR data agrees with the Ordnance Survey vectors on average to within 0-1 metres. The geometric accuracy for the scanner is stated as a vertical nominal accuracy of 0.05-0.10m, and a horizontal accuracy of between 0.13-0.61m (Leica Geosytems, 2003).