TESTING REMOTE SENSING TECHNIQUES FOR MONITORING LARGE SCALE INSECT DEFOLIATION Svein SOLBERG1, Lars EKLUNDH2, Arnt Kristian GJERTSEN1, Tomas JOHANSSON2, Steve JOYCE3, Holger LANGE1, Erik NÆSSET4, Håkan OLSSON3, Yong PANG5, Anne SOLBERG6 1
Norwegian forest and landscape institute, Norway, e-mail:
[email protected] 2 Lund University, Sweden 3 Swedish University of Agricultural Sciences, Sweden 4 Norwegian University of Life Sciences, Norway 5 Chinese Academy of forestry, China 6 Norwegian computing centre, Norway
ABSTRACT The REMFOR project evaluates remote sensing data and methods for monitoring forest health using variation in leaf area index (LAI) as a primary measure of defoliation. A large-scale pine sawfly outbreak in Norway serves as a test case. An LAI map of the study area was derived from airborne LIDAR measurements before and after the insect attack to serve as ground truth for satellite image analysis. The method predicts LAI from laser penetration rates through the canopy layer in accordance with the BeerLambert law calibrated with point measurements of LAI with LICOR LAI-2000. Comparing two cloud-free SPOT scenes from September 2004 and September 2005 shows obvious visual patterns of defoliation in pine forests from the 2005 outbreak. Preliminary analysis shows that the insect defoliation caused an increase in middle-infrared (SPOT band4) reflectance and a decrease in SPOT NDVI, and both these responses may be used as a reasonable predictor of LAI loss as derived from laser scanning. MODIS NDVI data were gathered for the area over the period 2000-2006, and the Timesat algorithm is used to smooth the seasonal variation. The insect attack is evident from the smoothed NDVI data both as a reduction in the summer mean value, and as an alteration of the seasonal profile during the larvae feeding period in June and July. REMFOR also encompasses a range of other remote sensing data types, including GLAS LIDAR, SAR and hyperspectral data from both airborne and satellite platforms (e.g. Hyspex and Hyperion). Landsat TM is used to generate a tree species map. Keywords: LAI, LIDAR, SPOT, MODIS, forest health, satellite
1 INTRODUCTION Forest health monitoring has been carried out throughout Europe and North America for about 20 years based mainly on visual assessments of defoliation on permanent monitoring plots. Although these assessment data are able to detect defoliation, they are hampered by problems such as subjectivity and a lack of spatial coverage. Remote sensing techniques are now developed to a level where it is likely that they could be applied for operational forest health monitoring. Defoliation is a general stress response, and it is closely linked to Leaf Area Index (LAI). In contrast to the SEMEFOR project (Ekstrand et al. 1998) we use LAI rather than visually assessed defoliation as a ground truth. This paper presents findings from the ongoing REMote sensing of FORest health project (REMFOR), whose aim is to test various remote sensing techniques for monitoring of forest health, and identify the most appropriate method for a
country-wide monitoring system. LAI is the particular focus as an operational variable in such a monitoring. A key feature of this project is three levels of LAI data: First, an algorithm for recalculating airborne laser scanning (LIDAR) data into LAI values is calibrated with ground based, point measurements of LAI. Second, this algorithm and the LIDAR data are used to produce LAI maps as a ground truth for the satellite data. Having both multi-spectral data (SPOT) and high temporal frequency data (MODIS), we are searching for possible monitoring methods based both on spectral responses of the insect damage, as well as effects on the temporal signatures. High spatial resolution hyper-spectral data are used to support the interpretation of the spectral response.
2 MATERIALS AND METHODS A large scale pine sawfly attack on Scots pine in Norway serves as a test case, where a severe defoliation developed during the period of larvae
feeding in June and July. The attack lasted from 2004 through 2006. The major damage in 2005 was known beforehand, and a range of multi-temporal data was gathered during this event which enables the studying of temporal changes. A forest stand map with major forest variables was available for the study, and the forest stand map polygon boundaries were used to calculate stand mean values of LIDAR and SPOT variables. The first step was to calibrate a model for recalculating the penetration rate of the LIDAR through the canopy layer into LAI in accordance with the Beer-Lambert law ,
(1)
where LAIe is effective leaf area index, k is an extinction factor that is calibrated from point based LAI measurements with LICOR LAI2000; Na is the total number of laser pulses and Nb is the number of 1st return ground echoes being echoes from below 1m above ground (Solberg et al. 2006). Two cloud-free SPOT images were acquired over the study area; the first is from September 2004 and the second from September 2005. A local image-to-image geometric matching was first done using Imagine AutoSync from Leica Geosystems to resample the 2004 image to the 2005 image. Both images were then converted to at-sensor radiance values using the calibration coefficients supplied with the image data. Radiance was converted to top-of-atmosphere reflectance to account for differences in sensor bandpasses in SPOT4 and SPOT5, the sun elevation, and time-dependent differences in solar irradiance. No correction was done for atmospheric effects. Differences in stand mean values were related to standwise mean change in LAI derived from the LIDAR measurements. We have extracted MODIS NDVI 1982-2006 time-series for the test area, being daily measurements that are aggregated to 16-day periods. In order to remove random errors in the data due to erroneous geo-location, angular variations, clouds and atmospheric disturbances (Eklundh et al. 2007), we have smoothed the time series with the SavitskyGolay algorithm of TIMESAT (Jönsson and Eklundh 2004), which fits smooth model functions to the upper envelope of the NDVI signal. In the present study we are testing two approaches for detecting the defoliation, i.e. changes in the yearly amplitude and the slope of the seasonal trend. A flight in late August 2005 retrieved a set of hyperspectral images (330 channels in the wavelength range 400 nm to 2000 nm) with a pixel size of 25 cm x 25 cm.
3 RESULTS LAI was derived from the LIDAR data as a linear, no-intercept model; LAIe = 1.48·ln(Na/Nb), where the parameter 1/k = 1.48 was estimated from ground based measurements with the LICOR device. The model had RMSE = 0.18 (Fig.1). With this model LAI values were predicted for every spatial unit in the LIDAR scanned area, e.g. the forest stand map (Fig. 2). The geographical pattern of defoliation was clear, although the absolute changes in LAI were very small. 2.5
2 LICOR LAI2000® LAI
LAIe = 1/k · ln(Na/Nb)
Finally, we have obtained LIDAR waveform data from the L3F orbit of the IceSAT-GLAS satellite, which was acquired on May-29, 2006. A number of footprints inside the study area were matched with the airborne LIDAR data from within a radius of 50m around the footprint centre. For the GLAS waveform we recalculated the penetration rate into the term ln(Na/Nb), i.e. corresponding to what was done with the airborne LIDAR data in equation (1), where Na is the integral of the entire waveform, while Nb is the integral of the waveform below 1 m above ground.
1.5
1
0.5
0 0
0.4
0.8 1.2 LIDAR ln(Na/Nb)
1.6
Figure 1. Calibrating LAI from LIDAR The symbols represent three consecutive LIDAR scans and LICOR LAI2000 ground measurements; ▲ May 11, ● July 29, and x September 2.
Comparison of SPOT images from before and after the outbreak showed clear patterns of defoliation, seen as an increase in SWIR and red reflectance, while there was a minor change in the NIR reflectance. The simplest defoliation mapping model is shown in Fig. 2b based on the SWIR band only. The very similar spatial pattern of a decrease in
NDVI is shown in Fig. 2c. The correspondence between the LIDAR LAI changes and the SPOT
SWIR and NDVI changes is demonstrated in Fig. 3.
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
Change in LAI
Change in LAI
Figure 2. Standwise predicted LAI loss based on from left a) LIDAR, b) SPOT SWIR, and c) SPOT NDVI, on a scale from green (no defoliation) to red (severe). The SPOT patterns correspond well with the laser derived measurements of LAI loss. Only stands having at least 90% of the standing volume as Scots pine are shown. Note there are individual stands that were harvested during the winter 2004-05 that show up as severe defoliation in the satellite maps, but having no change in LAI during summer 2005 in the LIDAR map.
0.2 0.1
0.2 0.1
0
0
-0.1
-0.1
-0.2
-0.2
-0.3 -0.05
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
Change in SWIR reflectance
-0.3 -0.16
-0.11
-0.06
-0.01
0.04
0.09
0.14
0.19
Change in NDVI
Figure 3. Change in LAILIDAR (May-July 2005) plotted against change (September 2004-September 2005) in spectral variables derived from SPOT for all pine-dominated stands: SWIR reflectance (left) and NDVI (right).
The time series of MODIS NDVI demonstrated regular seasonal variations, with the insect damage clearly seen as alterations of this regularity (Fig. 4).
The seven year time series in Fig. 4 shows MODIS NDVI data from a severely damaged part of the study area. During the years 2004-2006 the seasonal
mean value is decreasing, and in the main year of attack 2005 the trend during summer is negative. A relationship between changes in LAILIDAR and changes in yearly amplitude NDVIMODIS was found (Fig. 5), and the spatial pattern of decreasing NDVI coincided with the spatial defoliation pattern seen in the LIDAR LAI data. However, in a large portion of the pixels the relationships diverged between MODIS and LIDAR (Fig. 5). Several causes may
explain this, including the fact that the MODIS change is from 2004 to 2005, while LIDAR change is from May to August 2005. Also, the MODIS spatial resolution is coarse, and the defoliation signal in the NDVI data are likely obscured by forest management, stands of tree species other than Scots pine, and scattered agricultural fields in the area.
Figure 4. An NDVIMODIS timeseries 2000-2006 for a pixel with a severe insect damage in 2005. Note both the temporal decrease during 2003-2006 and the altered seasonal profile during the 2005 summer visualized as a changing summer trend angle. 4000 undamaged median damaged median 3500
Intensity
3000 2500 2000 1500 1000 500 0 400
600
800
1000
1200
1400
1600
1800
λ (nm) Figure 5. Change in LAILIDAR (May-July 2005) plotted against change in NDVIMODIS (yearly amplitude 2005 – yearly amplitude 2004) for MODIS pixels having at least 50% pine of the standing volume, and from the the northern part of the study area. Red circles had an increase in NDVI, while blue crosses had a decrease.
Figure 6. Contrasts in hyperspectral signatures between a damaged and an un-damaged area. Apart from the difference in damage, the two areas were comparable in all other aspects.
The hyperspectral data confirmed the findings of the SPOT and MODIS data, i.e. a general increase in
reflectance across wavelengths was seen as an effect of the defoliation. This was least pronounced in a part of the the NIR range around 800 nm (Fig. 7). Apart from that, an extensive set of multivariate comparisons revealed minor differences only for the two areas, both for first and for second order statistics. The penetration rates of the airborne LIDAR and the satellite borne LIDAR through the canopy layer were highly correlated and close to 1:1 relationship (Fig. 7). This demonstrates the potential for LAI monitoring based on Icesat GLAS or any future satellite borne LIDAR, using the same principle as with airborne LIDAR.
GLAS waveform ln(Na/Nb)
0.4
NIR reflectance from ground vegetation may obscure severe defoliation and removal of defoliated trees (Ekstrand et al. 1998). In the present study we have the advantage of a ground layer with almost no green vegetation. It may be expected that the increase in SWIR reflectance is a more useful indicator of defoliation, being less influenced by ground vegetation than the NDVI. MODIS NDVI has a potential for identifying homogeneous, large damaged areas. However, a wide scatter was seen when plotting MODIS NDVI change (2004-2005) against the change in LIDAR LAI during the insect attack in 2005. We need to refine an algorithm that takes into account both the amplitude and the changing slope of the summer NDVI values during insect years. This work is currently in progress. A country-wide map of major tree species, spruce, pine and birch is being made with a kNN method, and will help to interpret the causes of any defoliation event seen by remote sensing methods. The data input here Landsat TM together with an elevation model and a site index map over the area. As ground reference, plots from the National Forest Inventory will be used.
0.3
0.2
0.1
ACKNOWLEDGMENTS
0 0
0.1
0.2 0.3 LIDAR ln(Na/Nb)
0.4
0.5
Figure 7. The term ln(Na/Nb), being proportional to effective LAI, of GLAS LIDAR plotted against the same term from airborne LIDAR.
The Norwegian research council is acknowledged for financing the major part of the REMFOR project. We also want to acknowledge the financial contributions from the Norwegian Ministry of agriculture and food, as well as the fund Skogtiltaksfondet which is covered by the Norwegian forest owners.
4 DISCUSSION It is shown that the insect defoliation could be mapped in several ways, i.e. from an increased penetration of LIDAR beams through the canopy layer; as an increase in SWIRSPOT and decrease in NDVISPOT; and as a decrease in the seasonal mean and a changing seasonal trend of NDVIMODIS. These methods produced corresponding spatial patterns of defoliation. These early results indicate that using multitemporal SPOT data is a viable method for detecting and mapping the extent of defoliation. However, it is possible that the promising results obtained here would not be seen in other forest types with a well developed ground layer. A major obstacle in predicting LAI from optical remote sensing is that as defoliation increases, the ground vegetation has a gradually increasing influence on the data. High
REFERENCES Eklundh, L., Jönsson, P. and Kuusk, A. 2007. Investigating modelled and observed Terra/MODIS 500-m reflectance data for viewing and illumination effects. Advances in Space Research 39: 119-124. Ekstrand, S., Schardt, M., Granica, K., Koch, B., Kahabka, H., Carnemolla, S. and Häusler, T. 1998. SEMEFOR. Satellite based environmental monitoring of European forests. EC Community research. Luxembourg, 103 pp. Jönsson, P. and Eklundh, L. 2004. TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences 30: 833-845. Solberg, S., Næsset, E., Hanssen, K.H. and Christiansen, E. 2006. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment 102: 364-376.
