Monitoring Forest Cover Transitions using Landsat and Forest Inventory Data
Olivier R. van Lier1, Joan E. Luther2 and Donald G. Leckie3
1
Sir Wilfred Grenfell College, Memorial University, 20 University Drive, Corner Brook,
NL, Canada, A2H 6P9; 709.637.4944; OvanLier@NRCan.gc.ca
2
Canadian Forest Service, Natural Resources Canada, 20 University Drive, Corner Brook,
NL, Canada, A2H 6P9; 709.637.4971; JLuther@NRCan.gc.ca
3
Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria,
BC, Canada, V8Z 1M5; 250.363.0624; DLeckie@NRCan.gc.ca
1.0 Introduction
Forested landscapes are dynamic ecosystems which produce a variety of ecological,
social and economic values. Up-to-date land cover (LC) information ensures a sound
foundation for managing forested landscapes while past LC information provides
historical trends and can thus assist in future management planning. Moreover,
quantifying spatial forest change information is important for assessing impacts of human
activities and environmental changes on ecosystem sustainability.
In this paper, we present methods to monitor forest transitions using satellite imagery
and forest inventory data. We demonstrate the applicability of the methods for the
Humber River Basin (HRB), Newfoundland, a region where there are land and resource
management issues centered on maintaining a sustainable timber supply, conserving
wildlife habitat, preserving high profile viewscapes, and protecting water supply areas.
First, we generate a time series of LC maps using a combination of Landsat MSS and
Landsat TM data. We then quantify forest cover transitions over time and produce
spatially explicit maps of change. Such information can provide decision-makers with an
early warning system that can be further linked to human activities and environmental
changes.
2.0 Materials and Methods
2.1 Study area and data preparation
The Humber River Basin study area occupies 9 290 km2 in coastal Western
Newfoundland and Labrador, Canada (Fig. 1). Contained within the Boreal Shield
Ecozone, the HRB is largely dominated by balsam fir (Abies balsamea (L.) Mill.), black
spruce (Picea mariana (Mill.) B.S.P.) and to a lesser extent, white birch (Betula
papyrifera March.). Primarily disturbed by forest harvesting and insect damage, the HRB
has also experienced a significant increase in socio-economic development over the last
two decades. Although 85-90% of harvested areas regenerate naturally in this region,
socio-economic developments result in permanent LC transitions.
The study area was represented by three Landsat TM scenes (0426, 0525 and 0526)
acquired for the years 1990, 2001 and 2007 (Fig. 1a) and a single Landsat MSS scene
(0526) acquired for the year 1976. All Landsat scenes were resampled to a 25 m pixel
size, manually registered, mosaicked and reprojected using manual ground control point
collection with Geomatica OrthoEngine® software (©PCI Geomatics, Richmond Hill,
Ontario, Canada). Root mean square errors were maintained below 0.35 pixels to
maintain sub-pixel precision for change detection. Prior to mosaicking, the 2001 Landsat
images; hereafter referred to as baseline, were radiometrically normalized to a 2001
MODIS 32-day composite image using Theil-Sen robust regression (Olthof et al. 2005).
Similarly, the non-baseline 2007 images were normalized to a 2005 MODIS 32-day
composite image. Non-baseline mosaics were then normalized to the baseline mosaic
using the band statistical comparison technique (Joyce and Olsson, 1999). Processing and
analysis was performed on all pairs of baseline and non-baseline mosaics.
Three disturbance maps were generated from the provincial forest inventory database
to reflect disturbances occurring for each temporal transition period (i.e. 1976-1990,
1990-2001, 2001-2007). Disturbance maps were independently updated by
Newfoundland and Labrador’s Department of Natural Resources, Forestry Services
Branch, within forest management district offices. The HRB is intersected by provincial
management districts 9, 12, 13, 14, 15, 16, and Gros Morne National Park (Fig. 1b) and
the inventoried year varied though 1977 to 2003 from one district to another. The
inventories are updated on average every 13 years by interpreting 1:12,500 scale aerial
photographs. Disturbance polygons from the updated inventories (i.e. harvest, windthrow,
fire or insect) were selected and stratified by year of occurrence to match the analysed
transition periods.
a)
b)
Fig. 1. Location of the Humber River Basin overlaid with a) Landsat TM scene extents
and b) forest management districts.
2.2 Mapping land cover
Through a joint collaboration of Natural Resources Canada and the Canadian Space
Agency, the Earth Observation for Sustainable Development of Forests (EOSD) project
produced a national LC map of the forested areas of Canada (Wulder et al. 2003; 2008a)
which provided baseline coverage of the HRB. Through the EOSD project, the baseline
imagery was classified using unsupervised K-means clustering applied to individual
NDVI strata masks (water, non-vegetated, low reflectance vegetation, high-reflectance
vegetation) which were created during image pre-processing. Clusters were labelled as
one of 23 land cover classes through visual interpretation of multiple false color
composite image enhancements in combination with forest stand maps, ancillary data,
and local knowledge. Updating LC for the non-baseline imagery was accomplished by
first applying an unsupervised k-means clustering algorithm to each pair of baseline and
non-baseline imagery on bands 1 through 5, and 7 (Leckie et al. 2008). To mask change,
clusters were labelled as either change or no change. Unsupervised K-means clustering
was then applied solely to the non-baseline image bands and the resulting clusters were
merged and labelled as per the labelling process of the baseline. The mapping and
updating procedures were validated by comparing 294 ground survey plots sampled in
2008 with the 2007 LC product. Finally, for each LC map, the estimated areal
distribution of a) non-vegetated classes, b) vegetated non-treed classes, and c) vegetated
treed classes were quantified.
2.3 Forest cover transitions
Forest transitions were quantified from LC transition matrices generated by crosstabulating the LC maps for the three transition periods. As suggested by Puyravaud
(2003), annual rates of change were calculated for LC types using the following
compound-interest-rate formula:
A
1
Px 
ln x 2
t 2  t1 Ax1
(1)
where Px is the percent rate of change for a given LC type x, and Ax1 and Ax2 are x’s
areal estimate at time t1 and t2, respectively. The transition classes were validated by
comparing the transition of the 2001 LC map label to the 2008 ground survey plot label
with the 2001 to 2007 LC transition. Net forest cover depletion and regeneration maps
were then produced by mapping the transition of a treed cell to a non-treed cell, and a
non-treed cell to a treed cell, respectively. Percent cover of forest depletion and
regeneration were mapped for 10 ha grid cells.
3.0 Results
The overall classification accuracy of the 2007 LC map at the vegetation type level
was 74.15% with a Kappa Index of Agreement (KIA) of 0.68. The vegetated treed
surface area comprised 70.6% of the total HRB area in 1976; 70.3% in 1990, 67.1% in
2001 and 67.7% in 2007. The annual rates of change varied within transition periods. The
forest cover (i.e. aggregation of all treed LC types) changed at a rate of -0.03%/yr for the
1976 to 1990 transition period, -0.42%/yr for the following transition period (i.e. 1990 to
2001) and increased at a rate of 0.16%/yr for the final transition period (i.e. 2001 to2007).
The coniferous LC type decreased at a rate of 0.21%/yr and was accompanied by
increasing change rates for the vegetated non-treed (0.65%/yr), broadleaf (1.45%/yr),
mixed wood (0.61%/yr) and wetland treed (0.09%/yr) LC types.
The overall accuracy of the 2001-2007 transition classes (depletion, regeneration,
treed no change and non-treed no change) was 87.8% with a KIA of 0.83. We obtained
user accuracies of 91.7% for depletion, 89.7% for regeneration, 92.0% for treed no
change and 82.4% for non-treed no change. Disturbances mapped in the forest inventory
corresponded well with the treed LC to non-treed LC transition (Fig. 2).
a)
b)
Fig. 2. Forest depletion mapped from a) forest inventory disturbance data and b) LC
transition analysis, for the 2001-2007 time period.
Discussion
The implementation of land cover update methods and resulting generation of
multitemporal LC maps enabled post-classification analysis of areas of LC conversion
(i.e. change of LC type) or modification (i.e. change within a LC type) for a largely
forested region of western Newfoundland, Canada. These LC changes formed the basis of
further assessment of forest cover transitions and annual rates of change while the
sequence of generated LC maps portrayed the spatial and temporal pattern of these
changes.
The use of forest inventory data was an obvious source of information to validate our
LC and forest transitions. However, many issues exist when comparing pixel-based LC
maps with vector-based polygon interpretations of forest inventory (Wulder et al. 2006).
Moreover, since the HRB covers several forest districts, it was impossible to represent
the entire basin with a single time stamp of LC derived from forest inventory since the
forest inventory data interpretation years varied by management district. Therefore, the
forest inventory was not well suited for long-term monitoring and reporting of change for
the HRB. On-the-other hand, the analysis of satellite remote sensing data, made it
possible to extract trend information that was otherwise difficult to generate from the
available forest inventory.
Conclusions
This study analysed forest cover transitions within the Humber River Basin from 1976
to 2007, using Landsat and forest inventory data. The LC updating procedure adopted
here was well adapted to integrate continuous monitoring information on LC and forest
change to support resource management decision-making. The continuous Landsat
coverage allowed for spatially-explicit monitoring of change in land cover and was useful
in assessing trends to support local and regional resource management activities.
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