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Table of contents
- Proceedings of the Symposium held on October 7–10, 1991
- Research Publications
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We must be implemented in the near future. Ecosystem rehabilitation and forest regeneration in the humid. Biological characters and plasticity of juvenile tree stages to restore. Soil site factors influencing the reforestation on mining sites. Programme proposition for genetic. The ecology of Zambezian dry evergreen forest with recommenda. Conversion of exploited natural Dipterocarp forests into semi.
Some effects of selective logging on forest productivity and ecology. Utilization and protection of the forest belt of Mount Kenya East. Less frequent observations over long timescales are sufficient for degradation monitoring via proxies, however, the result may be of lower quality compared to direct mapping approaches [ 69 ].
Laporte et al. Roads were manually digitized and cross-checked by independent observers. The authors found the manual approach detected logging roads more consistently than semi-automated methods, particularly for older roads and when using images of lower quality.
Satellite-based monitoring provides the only practical and reliable means of monitoring legal and illegal logging activity in these remote areas. Solberg et al. Williams et al. Rauste et al. LiDAR is also used to identify logging roads and the potential area of disturbed forest. Quantitative estimates of forest carbon stock change are obtained by modelling AGB using remote sensing as input, or using a quantifiable proxy, such as a change in canopy height or tracking of forest successional stage.
Characterisation of forest age and growth stage is one approach to tracking secondary forest dynamics. Together with knowledge of prior land use and disturbance history, this provides insight into current and future change in forest carbon stocks.
Proceedings of the Symposium held on October 7–10, 1991
A lengthy time-series is desirable, and most readily available from Landsat. Helmer et al. The algorithm is computationally efficient and self-calibrating, but requires testing in other forest types with different seasonality and disturbance histories [ 29 ]. The integration of SAR and optical data has also been investigated for the capacity to improve the discrimination of forest growth stages compared to using single or multi-date optical imagery alone.
Confusion was greatest between the intermediate and mature stages. Rain events during image acquisitions affect the dynamic range of the SAR data, and images during the dry season are strongly preferred. LiDAR also demonstrates potential for characterisation of forest successional stage and change in heavily modified forest by measuring changes in the vertical distribution of the woody components.
The spatial extent of early, intermediate and late stage secondary dry tropical forest in Costa Rica was mapped using a 3-class ISODATA classification, and the change in vertical structure including height associated with each growth stage was assessed using the full waveform LiDAR data [ 11 ].
The accuracy of the LiDAR tree height estimates was assessed by comparing relative height RH metrics representing waveform energy quantiles with RH the height above the ground of the highest reflecting surface; [ 11 ] against field measured tree heights. RH and RH75 were highly related to all successional stages r 2 of 0.
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While data from penetrative sensors, such as SAR and LiDAR, demonstrate a high sensitivity to forest structural parameters, including tree height, volume and AGB, their use in linking forest structural change to degradation in tropical forests is a relatively new application. Similarly, tree height estimation has been demonstrated using optical data and stereo- and photogrammetry techniques, but not used in monitoring programs to account for forest degradation.
LiDAR estimates of canopy height are of a high accuracy e.
Anderson et al. A simple differencing of two canopy height models CHM revealed the loss of 4.
Key to the study was the use of certain LiDAR height metrics to quantify change associated with low-impact selective logging. With no currently operational spaceborne LiDAR however, the method cannot be used for change detection. A comparison of tree heights determined by inversion of a physical scattering model based on ALOS PALSAR correlation magnitude revealed disturbance in the forest as related to a change in vertical structure in Queensland, Australia [ 55 ]. In Tanzania, the InSAR height changes correlated well with reforestation, degradation and deforestation events observed over the year timeframe.
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In Uganda, height changes were mapped on a national scale, from which forest carbon stock changes and emissions were estimated [ 85 ]. A comparison with Landsat derived Global Forest Cover data revealed a similar capacity for detection of forest carbon losses, but improved detection of carbon gains using the InSAR approach [ 85 ]. Obtaining reliable estimates of AGB using EO data can be considered the holy grail of forest carbon science. Forest AGB is a quantifiable attribute, that when estimated continuously over time, and with reference to a baseline reference level representative of the mature intact forest state, could provide a useful indicator of degradation.
The ability to characterise large-area biomass distributions would assist in providing national estimates of forest carbon stocks and GHG emissions. Estimation of carbon stocks and change using EO data is technically challenging, largely because of the uncertainties associated with retrieval but also the prevailing environmental conditions, which often differ between two or more observation periods.
Changes in AGB can be quantified by 1 comparing two observations in time t 1 and t 2 with coincident field data to model change in biomass directly, with differences at the pixel level corresponding to remote sensing observables at t 1 and t 2 , and 2 modelling AGB for t 1 and t 2 separately and taking the difference.
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LiDAR vertical profiles can be correlated with disturbance events or used to quantify the area subject to disturbance and the associated loss of carbon [ 67 ]. LiDAR profiles in Queensland, Australia, exhibited unique patterns when related to disturbance events e.
Full waveform measurements from overlapping scans captured by terrestrial laser scanner TLS allow the stand to be reconstructed in 3D, including woody debris fallen logs and branches , and estimates of AGB obtained in situ [ 73 ]. Englhart et al. AGB regression models had an r 2 of 0.
Use of multi-temporal LiDAR to quantify canopy height and AGB dynamics in tropical peatland forest: a Transect through burnt and adjacent undisturbed peat swamp forest. Reliable estimation of the magnitude and extent of change from low intensity logging in the Brazilian Amazon using LIDAR was demonstrated by [ 2 ]. The multiple r 2 was 0. Jubanski et al. The point cloud exhibited unique signatures when related to disturbance events, e. In a study located in boreal forest in south east Norway, a multinomial logistic regression model was used to predict the change class e.
The change categories were then used as post-strata in estimating the net change in biomass. Allometric equations were used to convert the histograms to AGB estimates. A comparison with field based estimates of biomass revealed an SE of A significant relationship was observed between secondary forest age and AGB r 2 of 0. There was good agreement between the satellite-derived average biomass accumulation rate of 8.
With future spaceborne LiDAR, these methods will be available for operational monitoring of biomass change in all forest types. The sensitivity of SAR to canopy structure and biomass can be exploited to map changes associated with young and degraded low biomass forests. Ryan et al. Data fusion approaches may help overcome sensor specific limitations such as saturation, operating modes and temporal gaps [ 10 ]. Fully polarimetric and InSAR data affords greater detail on forest structure, and may provide further insight into degraded forests.
Canopy height decreased by 2. Temporal measurement of InSAR height and volume, as linked to AGB, may provide countries with a practical approach to estimating forest carbon stocks and emissions arising from gradual processes such as degradation and regrowth [ 85 ]. Resolving change on a small Minimum Mapping Unit MMU will necessitate the use of high to VHR satellite data, the cost of which may be limiting to countries wanting to implement an operational wall-to-wall monitoring system. The use of coarser resolution data may reduce the data demand, but certain areas of activity may escape detection.
The lack of systematic observations by key EO sensors has hampered methods development; as such, large-scale demonstrations are few. Numerous case studies have, however, demonstrated a high potential for retrieving activity data on forest degradation, as well as uncovering history of land use and other causes of disturbance using EO data. Data fusion can assist in mapping degradation, but obtaining near-coincident data is difficult with little to no coordination of SAR and optical satellite observations by space agencies.
Countries need access to low cost, high to VHR data to detect changes in forest cover and carbon stocks and so include estimates of emissions from degradation in their forest inventorys. Access to free high resolution optical data has only recently become available with the launch of Sentinel Other high resolution data, including SAR, are only available from commercial suppliers. With the exception of Sentinel-2, high resolution data are tasked on request, often resulting in fragmented spatial and temporal coverage.
There may be a case for using a sample of high to VHR images within a wall-to-wall monitoring system. Optical imagery is widely used and offers the most operational capability today. Open access to the USGS Landsat archive has spurred methods development using moderate resolution data. The long temporal archives are important for baseline generation i. Data from previous generation L-band SAR systems e.
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Higher resolution optical data is available through commercial missions e. The high cost can be prohibitive, but the high resolution is a requirement for detection of fine-scale degradation processes. High temporal frequency is also a requirement for capturing discrete events or rapid change regrowth in dynamic landscapes.
Ideally, one cloud-free seasonal coverage is required for tracking forest disturbance. We should see a rise in methods development using Sentinel-2 data in the near future. Inclusion of red edge bands and the short wave infrared SWIR will support the retrieval of information on forest condition and so support forest degradation monitoring efforts. Free and open access to data will greatly assist the establishment of long-term monitoring programs.