Forest Resource Mapping Research Articles

Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate.

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 ​m ​× ​16 ​m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 ​m3⋅ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.

Improving Detection of Changepoints in Short and Noisy Time Series with Local Correlations: Connecting the Events in Pixel Neighbourhoods

AbstractDetecting changepoints in time series becomes difficult when the series are short and the observation variance is high. In the context of time series of environmental resource maps, it is often safe to assume that the abrupt events are spatially continuous, and so are the changepoints. We propose to utilise this assumption by means of hierarchical models where the changepoints are modelled using a spatial model. We demonstrate utility of the approach by constructing a Bayesian model based on the Potts model, with additional assumptions relevant to changepoint detection in national multi-source forest inventory maps. We discuss implementation issues and demonstrate the idea’s performance using a simulation study. We then apply the model to forest resource maps in order to detect felling events.

НЕЙРОСЕТЕВАЯ МОДЕЛЬ ДЛЯ СЕГМЕНТАЦИИ КОСМИЧЕСКИХ СНИМКОВ В МОНИТОРИНГЕ ФАКТОРОВ ОБЕЗЛЕСЕНИЯ ТЕРРИТОРИИ

The article deals with the important problem of applying neural network models in the tasks of monitoring the condition of forest areas using optical satellite images. Objective. The objective of this research was to develop a neural network model of forest felling suitable for automation of decoding multispectral Sentinel-2 satellite images in forest monitoring tasks in the example of the Khanty-Mansi Autonomous Okrug. Materials and Methods. The basis of the developed model was the forest harvesting satellite images segmentation procedure based on the convolutional neural network of deep learning. Sentinel-2 data interpretation was done using the modern QGIS geoinformation system. Over 990 pairs of multi-temporal space images of the forest territories of Khanty-Mansi Autonomous Okrug in winter (snow) period of 2018–2022 were processed to prepare the training data set. More than 70 000 images of the training data set and corresponding masks of forest cutting contours were generated with these images. Results. As a result of this work, a neural network model of forest felling was created, which implements efficient segmentation of space images, which allowed automating the labor-intensive procedure of interpretation of multispectral Sentinel-2 images in order to identify forest logging contours. The set of training data totally 70 000 frames was divided into the training, validation (test) and test (control) samples, the amount of which for the deve¬lopment of the neural network model of forest felling was 58 000, 10 000 and 3600 frames respectively. The novelty of the model is determined, on the one hand, by the use of winter (snow) satellite images for training the neural network and, on the other hand, by the use of pairs of images acquired before and after forest felling. Recall, Precision and F-measure metrics were used as the criteria to evaluate the accuracy of the trained neural network model, and the values were calculated from the test sample. The calculated accuracy of felling detection using the proposed model on the test sample of data on different metrics reaches 85–87%. Conclusion. The developed neural network model of forest felling can be used for monitoring and mapping of forest resources of the northern forestry territories of Russia using multispectral Sentinel-2 satellite images.

Mapping of Teak (Tectona grandis) Growing Stock Volume based on Sentinel-2 Imagery Classification

Remote sensing utilization for plantation forests management in Java was not yet widely used, whereas the ability of remote sensing data for land cover monitoring and forest resource mapping has been developed, ranging from low resolution imagery for global areas to moderate and high resolution for small scale areas. Data availability and human resources often become obstacles in the application. The emergence of Sentinel satellite images becomes an alternative because the dataset is free access and has moderate spatial resolution and high temporal resolution. This study aims to map the growing stock volume in UGM Educational and Training Forest using Sentinel-2 imagery. Three kinds of classification method based on machine learning algorithms i.e. Random Forest, K-NN and SVM were compared for land cover classification. An NDVI algorithm was also used for mapping the spectral value distribution. Moreover, a stand age distribution which obtained from KHDTK UGM manager were also map. A stand classification map based on land cover types, NDVI value and stand age distribution was created as a basis of growing stock volume estimation. The analysis show that the growing stock volume can be estimated using these method with RMSE 177,8 m3 and MAPE 21,9 m3.

Comparative analysis of methods applied in vegetation cover delimitation using Landsat 8 images

There is a wide availability of methods and techniques for classification of data from remote sensing images. However, one of the biggest challenges is to identify whether the applied method is really effective for the thematic mapping the terrain features. Thus, the aim of this work was to provide a comparative analysis involving data classification methods for mapping forest cover using orbital images from the Landsat 8 satellite. The applied method consisted of pre-processing the images, calculating the NDVI image, performing the infrared image composition and principal component analysis (PCA). The maximum likelihood classification method (MAXVER) was used to delimit the vegetation cover applied to the three types of databases. To validate the classification results, field data, Kappa analysis and pixel-by-pixel analysis were applied. The results pointed out that the NDVI method showed the least general similarity regarding to the reference data used for validation from the MAPBIOMAS project. It was possible to identify similar results in relation to the delimitation of forest cover. The results allowed identifying that the several methodologies available for classification of vegetation are of great value for the thematic mapping of forest resources. In addition, we conclude that the PCA showed the best capacity for delimiting the vegetation cover in the study region, closely followed by infrared composition, and the NDVI was the least accurate.

Predicting biomass dynamics at the national extent from digital aerial photogrammetry

The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R2 = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (< 2 years) between image capture and field-based measurements, ranging between 0.76 and 0.34. A close temporal match between field surveys and remote sensing data acquisition is thus key to reliable mapping and monitoring of AGB dynamics, especially in areas where forest management and natural disturbances trigger relatively fast canopy dynamics. We show that VHMs derived from repeat DAP capture constitute a cost effective and reliable approach to map and monitor changes in woody AGB at a national extent and can provide an important information source for national carbon accounting and monitoring of ecosystem service provisioning.

Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations

It is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from multiple sources. We defined four vegetation classes: natural forest (NF), planted eucalyptus forest (PF), agriculture (A) and pasture (P), and sampled 410,251 pixels from 100 polygons of each class. Classification experiments were performed by using a random forest algorithm with images from Landsat-8, Sentinel-1, and SRTM. We considered four texture features (energy, contrast, correlation, and entropy) and NDVI. We used F1-score, overall accuracy and total disagreement metrics, to assess the classification performance, and Jeffries–Matusita (JM) distance to measure the spectral separability. Overall accuracy for Landsat-8 bands alone was 88.29%. A combination of Landsat-8 with Sentinel-1 bands resulted in a 3% overall accuracy increase and this band combination also improved the F1-score of NF, PF, P and A in 2.22%, 2.9%, 3.71%, and 8.01%, respectively. The total disagreement decreased from 11.71% to 8.71%. The increase in the statistical separability corroborates such improvement and is mainly observed between NF-PF (11.98%) and A-P (45.12%). We conclude that combining optical and radar remote sensing data increased the classification accuracy of natural and planted forests and may serve as a basis for large-scale semi-automatic mapping of forest resources.

Reducing error in small-area estimates of multi-source forest inventory by multi-temporal data fusion

Abstract Since the 1990s, forest resource maps and forest variable estimates for small areas have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data. A non-parametric $k$-NN method has frequently been employed. In Finland, such multi-source NFI (MS-NFI) forest variable estimates for municipalities have been produced eight times. A relatively large variation has been observed between subsequent estimates. In this study, a large-scale evaluation of small-area estimates from an MS-NFI conducted in 2013 was carried out in comparison with pure NFI field data-based estimates and error estimates. The proportion of municipalities with significant differences was larger than expected, e.g. over 10% for the mean volume, which indicates systematic error in the small-area estimates. A multi-temporal data fusion combining MS-NFI estimators from three time points—2011, 2013 and 2015—was tested as a means to improve single time point MS-NFI estimates of the mean volumes of growing stock and of tree species groups. A generalized least squares (GLS) technique and unweighted averaging were tested. The improvement was small but consistent when validated against the NFI field data-based estimates for the municipalities. The unweighted averaging worked nearly as well as a GLS estimator.

Assessing local trends in indicators of ecosystem services with a time series of forest resource maps

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric -nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m pixel size and units of 240 × 240 m, 1200 × 1200 m and 12 000 × 12 000 m aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12 000 × 12 000 m. The multiple testing corrections decreased the amount of significant -values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.k22222p

Remote Sensing and GIS Application on Forest Resource Mapping and Monitoring in Bulolo District, Morobe Province

Forest resources monitoring are particularly challenging for tropical forest due to their diverse composition and structure and a wide range of stakeholder’s expectations and requirement. New monitoring approaches and control policies directions are required to meet these different challenges. For the past decades, much of the focus of formal forest monitoring and management policy in Papua New Guinea (PNG) has been on large scale conventional harvesting to meet national requirements for economic development, with little attention given to community or small area forest management and monitoring. The current management is considered to be unsustainable and, as forest resources from primary forests are exhausted. This has resulted in extensive cutover forest areas being left to degrade over time. Forest reserve has suffered seriously and if the present trend of deforestation continues; it is just a matter of time when the whole reserve would have been converted to a bare ground. This study therefore examined the integration of remote sensing (RS) and geographic information system (GIS) application on forest resource mapping and monitoring in Bulolo district, Morobe province. Landsat satellite imageries for 1992, 2002 and 2014 were used to classify and identify forest changes through change detection techniques. A GIS database of land use categories and their location within 24 years (1992-2014) were generated and analysed with the aid of GIS analytical functions. This function includes area calculation, overlay, and image differencing, supervised classifications, cross tabulations and map representation. The result shows that population growth (anthropogenic) factors among communities around the natural forest imposes a lot of pressure on the natural forest resources. This should also include consideration of the future usage capacity of the forest resources as well as development of the capacity of local forest owner communities to participate in small scale forest management and utilization.

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

BackgroundRemote sensing-based mapping of forest Ecosystem Service (ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.MethodsThis study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.ResultsPrediction models based on Airborne Laser Scanning (ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses (berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97% of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.ConclusionsDespite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

Using Multi Criteria Evaluation in Forest resource conservation in Ghana: Spatially identifying vulnerable areas

The importance of forest resources to the socio-economic development of countries cannot be over-emphasized. A lot of research has been conducted in areas relating to forest management and protection in Sub-Saharan African. This paper discussed and illustrated the use of Spatial Multicriteria Decision Support System (SMDSS) to identify factors that make forest and game reserves vulnerable to rampant human induced depletion of forest resources by assessing risk factors of slope, roads and settlement data in Ghana. The model structure is aimed at understanding the critical vulnerable factors that threatens the survival of forest resources to deforestation, and draws on data from the Forestry Commission, CERSGIS and the Ghana Statistical Service. This paper incorporates multiple criteria and rank risk factors. In the end spatial maps are produced that identifies vulnerable areas for each ecological zone, so that specific policy interventions can be targeted by government to safeguard same. The research attempts to propose technology in managing scarce forest resources through the use of GIS techniques. It contributes to the discourse on forest management, ecological mapping and inventory of forest resources in Ghana. It provides an information base to tackle the threat of deforestation on a location by location basis. We conclude that spatial information is critical to forest resource management although spatial data is virtually unavailable in most developing countries. There is therefore the need by government to ensure acquisition and access to spatial data if forest resources are to be managed effectively.Keywords—Forest, Conservation, Vulnerability, GIS and RS, SMDSS, Ghana

Lentinula edodes based GIS mapping, biometabolites and antiinflamatory activity of wild edible mushrooms from tropical ‘sacred grove’ forests of Meghalaya, India.

The biodiversity rich state of Meghalaya, India located in the realms of mega-biodiversity hotspots, is home to numerous species of wild edible macrofungi that are used extensively by the mycophillic ethnic population, as a part of their traditional cuisine and medicine systems. However, habitat loss, due to deforestation and climate change, is destroying the natural population of these mushrooms, depleting their availability to the local communities. In the present investigation, a GIS guided habitat search, using Lentinula edodes as a representative species, was used in mapping the habitats of wild edible macrofungi of the study region. Sampling of around 4 000 specimens per distinct morphological type available in the traditional markets and “sacred grove” forests indicated presence of ten common genera, belonging to nine different families of wild edible mushrooms. Nutritional profiling of the representative species Lentinula edodes was carried out by evaluation of its moisture, total fat, crude protein and carbohydrates contents by standard methods. Similarly, bioactive components determination was performed by estimation of total phenols, flavonoids, ascorbic acid, β-carotene and lycopenes. Bioactivity of the mushrooms extracts was studied using the DPPH radical scavenging and Human Red Blood Cell (HRBC) membrane stabilization assays. The present investigation successfully attempted to explore remote sensing technologies and GIS (Geographic Information System) based system to predict the natural habitats of wild edible mushrooms of Meghalaya, India which we believe will lead to the generation of a mushroom specific non-wood forest resource mapping system in the near future. Results of nutritional profiling and biological activity studies on the representative species of wild edible mushrooms from the studied region revealed that it is a rich source of essential nutrients and antioxidants.

Moving from Measuring, Reporting, Verification (MRV) of Forest Carbon to Community Mapping, Measuring, Monitoring (MMM): Perspectives from Mexico.

There have been many calls for community participation in MRV (measuring, reporting, verification) for REDD+. This paper examines whether community involvement in MRV is a requirement, why it appears desirable to REDD+ agencies and external actors, and under what conditions communities might be interested in participating. It asks What’s in it for communities? What might communities gain from such an involvement? What could they lose? It embraces a broader approach which we call community MMM which involves mapping, measuring and monitoring of forest and other natural resources for issues which are of interest to the community itself. We focus on cases in México because the country has an unusually high proportion of forests under community communal ownership. In particular, we refer to a recent REDD+ initiative—CONAFOR-LAIF, in which local communities select and approve local people to participate in community-based monitoring activities. From these local initiatives we identify the specific and the general drivers for communities to be involved in mapping, measuring and monitoring of their own territories and their natural resources. We present evidence that communities are more interested in this wider approach than in a narrow focus on carbon monitoring. Finally we review what the challenges to reconciling MMM with MRV requirements are likely to be.

LU/LC Change Detection and Forest Degradation Analysis in Dalma Wildlife Sanctuary Using 3S Technology: A Case Study in Jamshedpur-India

Geo-informatics technology has dynamic role in mapping, monitoring and management of forest resources. The transformation of forest cover and its analysis on the earth’s surface are essential for understanding the associations and interactions between natural phenomena and living organism, especially human. Also deforestation is a major reason for global warming and one of the origin keys for the enhancement of greenhouse effect and climate change. The present study is grounded on the 3S technology in assessment of LU/LC changes within the forest cover area in Dalma Wildlife Sanctuary (DWLS) Jamshedpur-Jharkhand, India. The movement of forest-cover variation over the years 2009, 2011, 2013, 2015 and 2016 is precisely studied using high resolution Satellite Data (SD). It is noted that, due to shifting cultivation, forest fire, and conversion of forest cover into crop land/bare land and settlement encroachments in forest region by villagers are rapidly increasing; as a result deforestation is taking place. It is predicted that the study would demonstrate the effectiveness of 3S technology in forest renovation, planning and management.

Analyzing Field-Derived Arboreal Spectral Signatures

Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec® Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.

A Comparison of Open-Source LiDAR Filtering Algorithms in a Mediterranean Forest Environment

Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them.

Comparison of interferometric and stereo-radargrammetric 3D metrics in mapping of forest resources

Abstract. Accurate forest resources maps are needed in diverse applications ranging from the local forest management to the global climate change research. In particular, it is important to have tools to map changes in forest resources, which helps us to understand the significance of the forest biomass changes in the global carbon cycle. In the task of mapping changes in forest resources for wide areas, Earth Observing satellites could play the key role. In 2013, an EU/FP7-Space funded project “Advanced_SAR” was started with the main objective to develop novel forest resources mapping methods based on the fusion of satellite based 3D measurements and in-situ field measurements of forests. During the summer 2014, an extensive field surveying campaign was carried out in the Evo test site, Southern Finland. Forest inventory attributes of mean tree height, basal area, mean stem diameter, stem volume, and biomass, were determined for 91 test plots having the size of 32 by 32 meters (1024 m2). Simultaneously, a comprehensive set of satellite and airborne data was collected. Satellite data also included a set of TanDEM-X (TDX) and TerraSAR-X (TSX) X-band synthetic aperture radar (SAR) images, suitable for interferometric and stereo-radargrammetric processing to extract 3D elevation data representing the forest canopy. In the present study, we compared the accuracy of TDX InSAR and TSX stereo-radargrammetric derived 3D metrics in forest inventory attribute prediction. First, 3D data were extracted from TDX and TSX images. Then, 3D data were processed as elevations above the ground surface (forest canopy height values) using an accurate Digital Terrain Model (DTM) based on airborne laser scanning survey. Finally, 3D metrics were calculated from the canopy height values for each test plot and the 3D metrics were compared with the field reference data. The Random Forest method was used in the forest inventory attributes prediction. Based on the results InSAR showed slightly better performance in forest attribute (i.e. mean tree height, basal area, mean stem diameter, stem volume, and biomass) prediction than stereo-radargrammetry. The results were 20.1% and 28.6% in relative root mean square error (RMSE) for biomass prediction, for TDX and TSX respectively.

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P- and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer’s accuracy of 96% and user’s accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.