Estimation of forest stem volume using ALOS-2 PALSAR-2 satellite images

A first evaluation of ALOS-2 PALSAR-2 data for forest stem volume estimation has been performed at a coniferous dominated test site in southern Sweden. Both the Fine Beam Dual (FBD) polarization and the Quad-polarimetric mode were investigated. Forest plots with stem volume reaching up to a maximum of about 620 m3 ha-1 (corresponding to 370 tons ha-1) were analyzed by relating backscatter intensity to field data using an exponential model derived from the Water Cloud Model. The estimation accuracy of stem volume at plot level (0.5 ha) was calculated in terms of Root Mean Square Error (RMSE). For the best case investigated an RMSE of 43.1% was obtained using one of the FBD HV-polarized images. The corresponding RMSE for the FBD HH-polarized images was 43.9%. In the Quadpolarimetric mode the lowest RMSE at HV- and HHpolarization was found to be 39.8% and 47.4%, respectively

Nation-wide clear-cut mapping in Sweden using ALOS PALSAR strip images

Advanced Land Observing Satellite (ALOS) Phased Array L-band type Synthetic Aperture Radar (PALSAR) backscatter images with 50 m pixel size (strip images) at HV-polarization were used to map clear-cuts at a regional and national level in Sweden. For a set of 31 clear-cuts, on average 59.9% of the pixels within each clear-cut were correctly detected. When compared with a one-pixel edge-eroded version of the reference dataset, the accuracy increased to 88.9%. With respect to statistics from the Swedish Forest Agency, county-wise clear-felled areas were underestimated by the ALOS PALSAR dataset (between 25% and 60%) due to the coarse resolution. When compared with statistics from the Swedish National Forest Inventory, the discrepancies were larger, partly due to the estimation errors from the plot-wise forest inventory data. In Sweden, for the time frame of 2008–2010, the total area felled was estimated to be 140,618 ha, 172,532 ha and 194,586 ha using data from ALOS PALSAR, the Swedish Forest Agency and the Swedish National Forest Inventory, respectively. ALOS PALSAR strip images at HV-polarization appear suitable for detection of clear-felled areas at a national level; nonetheless, the pixel size of 50 m is a limiting factor for accurate delineation of clear-felled areas

Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties

Estimation of change in forest variables using synthetic aperture radar

Large scale mapping of changes in forest variables is needed for both environmental monitoring, planning of climate actions and sustainable forest management. Remote sensing can be used in conjunction with field data to produce wall-to-wall estimates that are practically impossible to produce using traditional field surveys. Synthetic aperture radar (SAR) can observe the forest independent of sunlight, clouds, snow, or rain, providing reliable high frequency coverage. Its wavelength determines the interaction with the forest, where longer wavelengths interact with larger structures of the trees, and shorter wavelengths interact mainly with the top part of the canopy, meaning that it can be chosen to fit specific applications. This thesis contains five studies conducted on the Remningstorp test site in southern Sweden. Studies I – III predicted above ground biomass (AGB) change using long wavelength polarimetric P- (in I) and L-band (in I – III) SAR data. The differences between the bands were small in terms of prediction quality, and the HV polarization, just as for AGB state prediction, was the polarization channel most correlated with AGB change. A moisture correction for L-band data was proposed and evaluated, and it was found that certain polarimetric measures were better for predicting AGB change than all of the polarization channels together. Study IV assessed the detectability of silvicultural treatments in short wavelength TanDEM-X interferometric phase heights. In line with earlier studies, only clear cuts were unambiguously distinguishable. Study V predicted site index and stand age by fitting height development curves to time series of TanDEM-X data. Site index and age were unbiasedly predicted for untreated plots, and the RMSE would likely decrease with longer time series. When stand age was known, SI was predicted with an RMSE comparable to that of the field based measurements. In conclusion, this thesis underscores SAR data's potential for generalizable methods for estimation of forest variable changes

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months

Can remote sensing be used to support sustainable forestry in Malawi?

Sustainable forest management is a key issue in Malawi. Malawi is a relatively small, resource poor, densely populated country, which in some areas is close to exceeding the energy capacity of the environment to support it. Despite the importance of forestry in Malawi, there is a severe lack of knowledge about the current state of Malawi’s forest resources. Remote sensing has the potential to provide current and historical insights into forest cover change. However, Malawi faces a number of key challenges with regards to in-country remote sensing. These include technical capacity for obtaining accurate and consistent forest area and biomass estimates, with errors at acceptable levels, as well as the necessary supporting capacity development for individuals and institutions. This thesis examines how remote sensing can be used to support sustainable forestry in Malawi, by assessing the use of both optical and Synthetic Aperture Radar (SAR) data for mapping forest cover, forest cover change and aboveground biomass (AGB). L-band SAR data was used to try and establish a relationship between radar backscatter and biomass, which has been achieved many times in other areas. However, no correlations between any field-based forest metric and backscatter explained enough of the variability in the datasets to be used to develop empirical relationships between the variables. There were also differences between my field measured AGB and AGB values predicted by a published backscatter-biomass relationship for African dry forests. The speckle inherent in SAR imagery, the heterogeneity of Malawi’s dominant miombo savanna, and Malawi’s variable topography are likely to have played a significant role in this. Two different MODIS products were investigated for their potential for mapping forest cover change, with regards to potential REDD+ schemes. As part of this, a published equation was used to calculate the break-even point for REDD+ schemes in Malawi, using estimates of forest area and deforestation for the United Nations Forest Resources Assessment 2010. The results of this equation show that measurement error is the most important factor in determining whether or not Malawi can make REDD+ economically viable, particularly at lower levels of deforestation. While neither of the MODIS products were able to produce a verifiable forest cover change map, they do confirm that Malawi is experiencing some level of forest loss, and help to narrow down the range of possible forest loss rates Malawi is experiencing to between 1-3% net forest loss per year. Finally, this thesis examines global trends in the engagement of developing country researchers with global academic remote sensing research, to investigate differences in in-country capacity for monitoring forests using remote sensing. The results of this found that while a significant proportion of Earth observation research (44%) has developing countries as their object of research, less than 3% of publications have authors working, or affiliated to, a developing country (excluding China, India and Brazil, which are not only countries in transition, but have well established EO capacity). These patterns appear consistent over the past 20 years, despite the increasing awareness of the importance of capacity development over this period. Despite inconclusive results from the approaches examined here, remote sensing can play a role in improving understanding about the dynamics of Malawi’s forest resources. There is a need for nationwide accurate, validated forest maps that can be repeated at least on a yearly basis, and remote sensing could produced these without the resources needed to conduct full national ground inventories each year. If remote sensing is to be useful as a forest mapping tool in Malawi, it needs to provide consistent, verifiable and updatable estimates of forest cover and biomass change. This ideally needs to be achieved using free or low cost data, and by using open source or open access software, as this will better enable incountry researchers to conduct on-going forest mapping activities