Estimation of forest structure parameters in Mexico by integration of remote sensing and forest inventory data

Diese Arbeit liefert Einblicke in die aktuellen Möglichkeiten und Einschränkungen der Verwendung von Fernerkundungsdaten zur Kartierung der Waldstruktur auf lokaler, regionaler und nationaler Ebene in Mexiko. Insbesondere wurden die Auswirkungen von multisensorischen und multitemporalen Fernerkundungsdaten auf die Kartierungsgenauigkeit von Waldstrukturparametern (d.h., oberirdische Biomasse (AGB) und Vegetationshöhe) untersucht. Darüber hinaus wurde der Einfluss von Qualität, Anzahl und räumlicher Verteilung von Referenzdaten auf die Modellierungsgenauigkeit untersucht. Um diese Fragen zu beantworten, wurden optische und L-Band Synthetic Aperture Radar (SAR) Fernerkundungsdaten zusammen mit nationalen Waldinventurdaten sowie flugzeuggestützten Light Detection and Ranging (LiDAR) Daten unter Verwendung von verschiedene Modellierungsszenarien integriert

Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Accurate measurements of terrain elevation are crucial for many ecological applications. In this study, we sought to assess new global three-dimensional Earth observation data acquired by the spaceborne Light Detection and Ranging (LiDAR) missions Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). For this, we examined the “ATLAS/ICESat-2 L3A Land and Vegetation Height”, version 5 (20 × 14 m and 100 × 14 m segments) and the “GEDI Level 2A Footprint Elevation and Height Metrics”, version 2 (25 m circle). We conducted our analysis across four land cover classes (bare soil, herbaceous, forest, savanna), and six forest types (temperate broad-leaved, temperate needle-leaved, temperate mixed, tropical upland, tropical floodplain, and tropical secondary forest). For assessment of terrain elevation estimates from spaceborne LiDAR data we used high resolution airborne data. Our results indicate that both LiDAR missions provide accurate terrain elevation estimates across different land cover classes and forest types with mean error less than 1 m, except in tropical forests. However, using a GEDI algorithm with a lower signal end threshold (e.g., algorithm 5) can improve the accuracy of terrain elevation estimates for tropical upland forests. Specific environmental parameters (terrain slope, canopy height and canopy cover) and sensor parameters (GEDI degrade flags, terrain estimation algorithm; ICESat-2 number of terrain photons, terrain uncertainty) can be applied to improve the accuracy of ICESat-2 and GEDI-based terrain estimates. Although the goodness-of-fit statistics from the two spaceborne LiDARs are not directly comparable since they possess different footprint sizes (100 × 14 m segment or 20 × 14 m segment vs. 25 m circle), we observed similar trends on the impact of terrain slope, canopy cover and canopy height for both sensors. Terrain slope strongly impacts the accuracy of both ICESat-2 and GEDI terrain elevation estimates for both forested and non-forested areas. In the case of GEDI the impact of slope is, however, partly caused by horizontal geolocation error. Moreover, dense canopies (i.e., canopy cover higher than 90%) affect the accuracy of spaceborne LiDAR terrain estimates, while canopy height does not, when considering samples over flat terrains. Our analysis of the accuracy and precision of current versions of spaceborne LiDAR products for different vegetation types and environmental conditions provides insights on parameter selection and estimated uncertainty to inform users of these key global datasets

On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of 'transparency, accuracy, completeness, consistency and comparability'. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks and C-emissions as well as to develop sustainable forest management strategies. In this study we used Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data to improve AGB estimations over two study areas in southern Mexico. We used Mexican National Forest Inventory (INFyS) data collected between 2005 and 2011 to calibrate AGB models as well as to validate the derived AGB products. We applied MODIS NDVI time series data analysis to exclude field plots in which abrupt changes were detected. For this, we used Breaks For Additive Seasonal and Trend analysis (BFAST). We modelled AGB using an original field dataset and BFAST-filtered data. The results show higher accuracies of AGB estimations using BFAST-filtered data than using original field data in terms of R2 and root mean square error (RMSE) for both dry and humid tropical forests of southern Mexico. The best results were found in areas with high deforestation rates where the AGB models based on the BFAST-filtered data substantially outperformed those based on original field data (R2BFAST = 0.62 vs. R2orig = 0.45; RMSEBFAST = 28.4 t/ha vs. RMSEorig = 33.8 t/ha). We conclude that the presented method shows great potential to improve AGB estimations and can be easily and automatically implemented over large areas

Potential of Sentinel-1 time series for deforestation and forest degradation mapping in temperate and tropical forests

In this study we investigated the potential of dense synthetic aperture radar (SAR) time series collected by the ESA’s Sentinel-1 satellites to detect deforestation and forest degradation areas. Since SAR data are affected by speckle, it is crucial to filter speckle before the time series analysis. Accordingly, we explored the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high frequency components are corresponding to speckle, these effects can be isolated and removed. Since the EMD approach operates in the time domain only, it fully preserves the geometric resolution, which is required to detect small scale changes (e.g., forest degradation). We assessed the speckle filtering performance of the EMD approach. The results over forested areas showed similar statistics compared to the multi-temporal Quegan speckle filter in terms of speckle suppression (based on Equivalent Number of Looks) and an improved edge preservation. In the next step, we analyzed EMD filtered Sentinel-1 data for detection of deforestation and forest degradation areas. For this, we first selected forested, deforested and degraded areas based on visual interpretation of multi-temporal very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. Further, we plotted EMD filtered Sentinel-1 time series for the three reference classes and were able to determine the time frame of deforestation and forest degradation. The initial analyses showed promising results regarding the separation of forest and forest-change classes with EMD-filtered Sentinel-1 data in contrast to original SAR backscatter images. Furthermore, we present preliminary deforestation maps for study sites in Mexico and South Africa based on Bayesian probability approach and EMD-filtered Sentinel-1 time series backscatter. This study is supported by DLR in the Sentinel4REDD project (FKZ:50EE1540) to develop new remote sensing based methods using Sentinel-1 and Sentinel-2 data to support UNFCC (United Nations Framework Convention on Climate Change) REDD+ MRV (Measurement, Reporting and Verification) Systems

Potential of Sentinel-1 time series for deforestation and forest degradation mapping in temperate and tropical forests

We want to present results of the Sentinel4REDD project. The overall aim of the Sentinel4REDD-Project is the development of new remote sensing based methods using Sentinel-1 and Sentinel-2 data to support UNFCC (United Nations Framework Convention on Climate Change) REDD+ MRV (Measurement, Reporting and Verification) Systems. We show results on the example of two testsites located in Mexico, one is over deciduous and evergreen forests in Hidalgo and the other is over tropical dry forests in Yucatan. For a signature analysis, we first selected forested, deforested and degraded areas based on visual interpretation of multi-temporal very high resolution (1 m) optical imagery. We developed a new speckle filter which retains the spatial resolution by using the temporal domain. We plotted filtered Sentinel-1 time series for the three reference classes and were able to determine the time frame of deforestation and forest degradation. The initial analyses showed promising results regarding the separation of forest and forest-change classes with filtered Sentinel-1 data in contrast to original SAR backscatter images. To generate REDD+ products (forest/non-forest, deforestation, forest degradation, reforestation maps), we apply two approaches using dense time series from Synthetic Aperture Radar (SAR) and optical sensors. The first method is based on multi-temporal metrics, e.g., mean, standard deviation and different percentiles of the SAR backscatter and surface reflectances for different time periods. The second is based on different seasonalities of land cover classes. For this, the time series of every pixel is decomposed into subsignals with differing temporal frequencies. From these subsignals different statistics are calculated. These statistics can then be used to derive forest/non-forest maps for different time periods to then obtain resulting deforestation and reforestation maps or to directly gain deforestation and degradation maps via the application of machine learning techniques. Furthermore, we present preliminary deforestation maps for study sites in Mexico and South Africa based on Bayesian probability approach and filtered Sentinel-1 backscatter time series

Potential of recurrence quantification analysis of Sentinel-1 time series for deforestation mapping

The UNFCCC REDD+ framework increases the need for highly accurate maps of deforestation and degradation in the tropics. Operational forest/non-forest maps are commonly based on optical imagery. However, especially in the tropics optical images are frequently degraded by the presence of clouds. Therefore, we investigated the potential of hyper-temporal Sentinel-1 synthetic aperture radar (SAR) data to derive forest/non-forest and deforestation maps. Feature selection has been used, to decrease the amount of data and to enhance the signal to noise ratio. This is especially relevant for the use of machine learning, because it is one way to deal with the curse of dimensionality. In this study we compared the use of recurrence quantification analysis (RQA) with traditional multi-temporal metrics for feature extraction from dense Sentinel-1 time series. Recurrence quantification analysis (RQA) is a non-linear time series analysis technique. It quantifies the patterns of recurrences in time series. By means of RQA a number of metrics can be calculated (e.g., determinism, recurrence Rate, laminarity), which describe the complex behaviour of dynamic systems. In contrast to traditional multi-temporal metrics (e.g., mean, median, quartiles, standard deviation), RQA considers the temporal order of the images of the time series. After calculating RQA and traditional multi-temporal metrics from the Sentinel-1 image time stacks, we performed a signature analysis. For this, we selected forested and deforested areas based on visual interpretation of annual very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. The signature analysis of the traditional and RQA metrics showed promising results for the classification of deforestation. Obviously the consideration of the temporal order of time series provides additional information compared to traditional multi-temporal statistics. Therefore RQA can enhance the accuracies of forest/non-forest and deforestation maps. In the future we plan to combine RQA metrics and multi-temporal metrics in order to further improve the map accuracies

Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations