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Forest clear-cut detection would be valuable for forest management, if it could be done routinely in near-real-time with a spaceborne synthetic aperture radar (SAR) system, which provides data all year and all-weather. In Sentinel-1 (S-1) time-series data, a forest clearing will lead to reduced backscatter intensity and increased interferometric SAR (InSAR) coherence magnitude. A time-series of 108 interferomtric wide (IW) Single look complex (SLC) S-1 images collected in 2016, 2017, and 2018 are used to study the potential for mapping clear-cut areas in eastern Ireland. We combined multitemporal InSAR coherence and backscatter intensity for the detection. This is an extension of previous studies that used either backscatter intensity or InSAR coherence magnitude, while we show the added value of both together. Coherence magnitude was the strongest predictor of the two. [ABSTRACT FROM AUTHOR]

Monitoring changes in forest height, biomass and carbon stock is important for understanding the drivers of forest change, clarifying the geography and magnitude of the fluxes of the global carbon budget and for providing input data to REDD+. The objective of this study was to investigate the feasibility of covering these monitoring needs using InSAR DEM changes over time and associated estimates of forest biomass change and corresponding net CO2 emissions. A wall-to-wall map of net forest change for Uganda with its tropical forests was derived from two Digital Elevation Model (DEM) datasets, namely the SRTM acquired in 2000 and TanDEM-X acquired around 2012 based on Interferometric SAR (InSAR) and based on the height of the phase center. Errors in the form of bias, as well as parallel lines and belts having a certain height shift in the SRTM DEM were removed, and the penetration difference between X- and C-band SAR into the forest canopy was corrected. On average, we estimated X-band InSAR height to decrease by 7 cm during the period 2000-2012, corresponding to an estimated annual CO2 emission of 5 Mt for the entirety of Uganda. The uncertainty of this estimate given as a 95% confidence interval was 2.9-7.1 Mt. The presented method has a number of issues that require further research, including the particular SRTM biases and artifact errors; the penetration difference between the X- and C-band; the final height adjustment; and the validity of a linear conversion from InSAR height change to AGB change. However, the results corresponded well to other datasets on forest change and AGB stocks, concerning both their geographical variation and their aggregated values. [ABSTRACT FROM AUTHOR]

Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, ..., 1900 m², sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m² to achieve the same precision as an inventory supported by ALS data. [ABSTRACT FROM AUTHOR]

Background: REDD+ implementation requires establishment of a system for measuring, reporting and verification (MRV) of forest carbon changes. A challenge for MRV is the lack of satellite based methods that can track not only deforestation, but also degradation and forest growth, as well as a lack of historical data that can serve as a basis for a reference emission level. Working in a miombo woodland in Tanzania, we here aim at demonstrating a novel 3D satellite approach based on interferometric processing of radar imagery (InSAR). Results: Forest carbon changes are derived from changes in the forest canopy height obtained from InSAR, i.e. decreases represent carbon loss from logging and increases represent carbon sequestration through forest growth. We fitted a model of above-ground biomass (AGB) against InSAR height, and used this to convert height changes to biomass and carbon changes. The relationship between AGB and InSAR height was weak, as the individual plots were widely scattered around the model fit. However, we consider the approach to be unique and feasible for large-scale MRV efforts in REDD+ because the low accuracy was attributable partly to small plots and other limitations in the data set, and partly to a random pixel-to-pixel variation in trunk forms. Further processing of the InSAR data provides data on the categories of forest change. The combination of InSAR data from the Shuttle RADAR Topography Mission (SRTM) and the TanDEM-X satellite mission provided both historic baseline of change for the period 2000-2011, as well as annual change 2011-2012. Conclusions: A 3D data set from InSAR is a promising tool for MRV in REDD+. The temporal changes seen by InSAR data corresponded well with, but largely supplemented, the changes derived from Land sat data. [ABSTRACT FROM AUTHOR]

We investigated the stability of TanDEM-X interferometric synthetic aperture radar (InSAR) heights across eight repeated acquisitions. With InSAR height we mean the height above ground of the scattering phase center. We obtained InSAR heights by subtracting a digital terrain model generated from airborne laser scanning. The acquisitions varied in polarization, normal baseline, and season. The study area was a spruce forest in southeastern Norway. We established 179 field plots within 26 selected forest stands and obtained aboveground biomass (AGB) from field inventory. The InSAR heights were generally stable across the acquisitions as was the relationship between AGB and InSAR height, although systematic and random variations were noted. Two acquisitions had close-to-identical technical properties and weather conditions, and they produced close-to-identical InSAR heights. InSAR heights were fairly stable across a range in temperature and precipitation through spring, summer, and autumn, across a range in baseline values and for both HH and VV polarizations. However, a winter acquisition at temperatures of -7°C had much deeper penetration into the canopy and generated considerably lower InSAR heights and, hence, a very different relationship with biomass. Higher random errors were noted in a cross-pol data set due to lower backscatter and when the normal baseline was very small or very large. A height of ambiguity around 20-50 m appeared to be optimal. Interferometric X-band SAR can be used for monitoring coniferous boreal forests as long as the season and technical properties of the acquisition are kept within certain ranges. [ABSTRACT FROM PUBLISHER]

Radargrammetry has a potential for forest inventories, based on the relationship between the canopy height model (CHM) and the forest variables such as biomass. The objective of this study is to describe the relationship between above-ground biomass and a stripmap TerraSAR-X radargrammetry CHM, with emphasis on accuracy and straightness of the relationship. The study was carried out in a spruce forest in south Norway, comprising biomass data from 145 plots of 250 m2 within 21 selected stands. Above-ground biomass for the plots ranged from 0 to 338 t/ha. We derived a digital surface model (DSM) from six TerraSAR-X stripmap acquisitions by automatic stereo matching. We subtracted a digital terrain model (DTM) from the DSM and obtained a CHM. We assigned the nearest 10 m × 10 m pixel to each field plot. The height of the CHM increased linearly with biomass with 15 t/ha/m. The rmse values were 23 t/ha (18%) at the stand level and 58 t/ha (44%) at the plot level. The tendency of curvilinearity was so weak that it could hardly be distinguished from a straight linear relationship. The straightness of the relationship may enable monitoring of biomass changes without an external DTM as input. A comparison between radargrammetry and interferometric synthetic aperture radar (InSAR) showed that the relationship between the biomass and their respective CHMs was almost identical in terms of parameter estimates. The strength of the relationship was higher with InSAR. By combining ascending and descending pairs followed by editing, the performance of radargrammetry was equally good as with InSAR. [ABSTRACT FROM PUBLISHER]

The aim of this study is to use airborne laser scanning (ALS) data to simulate synthetic aperture radar interferometry (InSAR) elevation data [digital elevation model (DEM)] from the spatial distribution of scatterers. A Shuttle Radar Topography Mission X-band DEM data set and an ALS data set from a spruce-dominated forest area are used. A 3-D grid of voxels is made from the spatial distribution of ALS first echoes. The slant angle penetration rate of the SAR microwaves (PSAR) is simulated to be a function of the vertical ALS penetration rate (PALS), i.e., PSAR = PALS^4. The InSAR DEM and heights above the ground are fairly well reproduced by the simulator. A total least squares regression model between the simulated and measured InSAR DEMs has an R^2 value of 0.99 and a slope of 1 : 1. By subtracting the ALS-based terrain heights (digital terrain model), we obtained InSAR heights, which were reproduced with an R^2 value of 0.78, a slope of 0.96, and a root-mean-square error of 2.3 m. With the simulator, it was demonstrated how a disturbance event would affect the InSAR height. Unfortunately, the relationship was curvilinear and concave, which means that the method is not very sensitive to weak disturbances. This might be partly overcome by using a more vertical incidence angle of the SAR microwaves. The simulator might be used for validation or ground truthing of the InSAR data, as well as gaining understanding of how vegetation changes affect the InSAR data. [ABSTRACT FROM AUTHOR]

The study compares the accuracy of timber volume prediction based on four different three-dimensional remote sensing data sets in one study area in southern Norway: airborne laser scanning (ALS), stereo aerial photogrammetry (AP), satellite interferometric synthetic aperture radar (InSAR) based on the TanDEM-X mission, and satellite radargrammetry based on the TerraSAR-X mission. We fitted linear mixed effects models with vegetation height and density metrics obtained from the remote sensing data sets as explanatory variables. The cross-validated root mean squared error (RMSE) relative to the observed mean was used as the measure of goodness-of-fit. ALS provided the most accurate prediction at plot level with RMSE = 19%, followed by AP (31%), InSAR (42%), and radargrammetry (44%). At stand level the methods' performances were equally ordered, with RMSE values of 12–23%. Including the variables terrain slope and aspect in the models improved the accuracy of AP, InSAR, and radargrammetry slightly. [ABSTRACT FROM AUTHOR]

Abstract: There is a need for accurate inventory methods that produce relevant and timely information on the forest resources and carbon stocks for forest management planning and for implementation of national strategies under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). Such methods should produce information that is consistent across various geographical scales. Airborne scanning Light Detection and Ranging (LiDAR) is among the most promising remote sensing technologies for estimation of forest resource information such as timber volume and biomass, while acquisition of three dimensional data with Interferometric Synthetic Aperture Radar (InSAR) from space is seen as a relevant option for inventory in the tropics because of its ability to “see through the clouds” and its potential for frequent updates at low costs. Based on a stratified probability sample of 201 field survey plots collected in a 960km2 boreal forest area in Norway, we demonstrate how total above-ground biomass (AGB) can be estimated at three distinct geographical levels in such a way that the estimates at a smaller level always sum up to the estimate at a larger level. The three levels are (1) a district (the entire study area), (2) a village, local community or estate level, and (3) a stand or patch level. The LiDAR and InSAR data were treated as auxiliary information in the estimation. At the two largest geographical levels model-assisted estimators were employed. A model-based estimation was conducted at the smallest level. Estimates of AGB and corresponding error estimates based on (1) the field sample survey were compared with estimates obtained by using (2) LiDAR and (3) InSAR data as auxiliary information. For the entire study area, the estimates of AGB were 116.0, 101.2, and 111.3Mgha−1, respectively. Corresponding standard error estimates were 3.7, 1.6, and 3.2Mgha−1. At the smallest geographical level (stand) an independent validation on 35 large field plots was carried out. RMSE values of 17.1–17.3Mgha−1 and 42.6–53.2Mgha−1 were found for LiDAR and InSAR, respectively. A time lag of six years between acquisition of InSAR data and field inventory has introduced some errors. Significant differences between estimates and reference values were found, illustrating the risk of using pure model-based methods in the estimation when there is a lack of fit in the models. We conclude that the examined remote sensing techniques can provide biomass estimates with smaller estimated errors than a field-based sample survey. The improvement can be highly significant, especially for LiDAR. [Copyright &y& Elsevier]

Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radar (InSAR) can greatly improve the precision of estimates of forest resource parameters such as mean biomass and biomass change per unit area. Field plots are typically used to construct models that relate the variable of interest to explanatory variables derived from the remotely sensed data. The models may then be used in combination with the field plots to provide estimates for a geographical area of interest with corresponding estimates of precision using model-assisted estimators. Previous studies have shown that field plot sizes found suitable for pure field surveys may be sub-optimal for use in combination with remotely sensed data. Plot boundary effects, co-registration problems, and misalignment problems favor larger plots because the relative impact of these effects on the models of relationships may decline by increasing plot size. In a case study in a small boreal forest area in southeastern Norway (852.6 ha) a probability sample of 145 field plots was measured twice over an 11 year period (1998/1999 and 2010). For each plot, field measurements were recorded for two plot sizes (200 m 2 and 300/400 m 2 ). Corresponding multitemporal ALS (1999 and 2010) and InSAR data (2000 and 2011) were also available. Biomass for each of the two measurement dates as well as biomass change were modeled for all plot sizes separately using explanatory variables from the ALS and InSAR data, respectively. Biomass change was estimated using model-assisted estimators. Separate estimates were obtained for different methods for estimation of change, like the indirect method (difference between predictions of biomass for each of the two measurement dates) and the direct method (direct prediction of change). Relative efficiency (RE) was calculated by dividing the variance obtained for a pure field-based change estimate by the variance of a corresponding estimate using the model-assisted approach. For ALS, the RE values ranged between 7.5 and 15.0, indicating that approximately 7.5–15.0 as many field plots would be required for a pure field-based estimate to provide the same precision as an ALS-assisted estimate. For InSAR, RE ranged between 1.8 and 2.5. The direct estimation method showed greater REs than the indirect method for both remote sensing technologies. There was clearly a trend of improved RE of the model-assisted estimates by increasing plot size. For ALS and the direct estimation method RE increased from 9.8 for 200 m 2 plots to 15.0 for 400 m 2 plots. Similar trends of increasing RE with plot size were observed for InSAR. ALS showed on average 3.2–6.0 times greater RE values than InSAR. Because remote sensing can contribute to improved precision of estimates, sample plot size is a prominent design issue in future sample surveys which should be considered with due attention to the great benefits that can be achieved when using remote sensing if the plot size reflects the specific challenges arising from use of remote sensing in the estimation. That is especially the case in the tropics where field resources may be scarce and inaccessibility and poor infrastructure hamper field work. [ABSTRACT FROM AUTHOR]