Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

Optical remote sensing for biomass estimation in the tropics: the case study of Uganda

This study investigates the capabilities and limitations of freely available optical satellite data at medium resolution to estimate aboveground biomass density of vegetation at national scales in the tropics, and compares this approach with existing methodologies to understand and quantify the sources of variability in the estimations. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset. As a result of this thesis, aboveground woody biomass for the year circa-2000 was mapped at national scale in Uganda at 30-m spatial resolution on the basis of Landsat ETM+ images, a national land cover dataset and field data using an object-oriented approach. A regression tree-based model (Random Forest) produced good results (cross-validated R² 0.81, RMSE 13 Mg/ha) when trained with a sufficient number of field plots representative of the vegetation variability. This study demonstrated that in certain contexts Landsat data can effectively spatialize field biomass measurements and produce accurate and detailed estimates of biomass distribution at national scale. This approach tended to provide conservative biomass estimates and its limitations were mainly related to the saturation of the optical signal at high biomass density and to the cloud cover. When compared with the Uganda national biomass dataset, the map produced in this study presented higher agreement than other five regional/global biomass maps. The comparative analysis showed strong disagreement between the products, with estimates of total biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change default values, and global land cover datasets strongly overestimated biomass stocks, while maps based on satellite data provided conservative estimates. The comparison of the maps predictions with field data confirmed the above findings

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

The High-Rise Resolution Carbon Geography of Peru

Vegetation is one of the most spatially and temporally dynamic reservoirs of carbon in the world. The amount of carbon stored in vegetation above ground in woody biomass is particularly variable, and is subject to rapid change via land uses that remove vegetation cover, causing carbon emissions. Reducing carbon emissions from deforestation and forest degradation, as well as from other non-forested ecosystems, is therefore a priority in both national and international strategies to conserve ecosystems and to reduce carbon dioxide build-up in the atmosphere.Perú harbors an enormous range of ecological conditions, from hot and humid lowland Amazonian forests to high-altitude Andean ecosystems and desert conditions on the Pacific coast. The diversity of environments in Perú greatly challenges efforts to measure, map and monitor carbon stocks throughout the country.We report the first high-resolution geographic study of aboveground carbon stocks throughout the more than 128 million hectares that comprise the country of Perú. This report communicates the development of our methodology and an extensive validation of the resulting high-resolution carbon map of Perú. It also provides the first quantitative analysis of the basic environmental factors determining the carbon geography of Peruvian ecosystems, political regions, and protected areas

Tropical Forest Canopy Height and Aboveground Biomass Estimation Using Airborne Lidar and Landsat-8 Data, a Sensitivity Study with Respect to Landsat-8 Data Temporal Availability, in Mai Ndombe Province, Democratic Republic of Congo

Tropical forests’ structure information, such as forest canopy height, is a key component in any estimate of carbon stock. Tropical rainforests constitute the most forested ecosystems that harbor the largest biodiversity on Earth and store more carbon (above and belowground biomass) than any other ecosystem in the world. However, estimates of forest canopy structure is lacking over most of the regions that host this ecosystem because of both the structure’s complexity of this ecosystems and the incomplete or lack of up-to-date national forest inventory data necessary to derive forest canopy height and aboveground biomass. This study explores the capability of Landsat-8 imagery to predict dominant forest canopy height and aboveground biomass in Mai Ndombe province, Democratic Republic of Congo – a country that host half of the Congo Basin forests – within the context of the temporal availability of Landsat-8 imagery. A random forest regression model was used to predict dominant forest canopy height at 30 m spatial resolution from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images. The accuracy of the random forest regression model was performed on test data (n=2639) resulting in a, for the best prediction when using both dates together, RMSE = 3.84 m, R2 = 0.47. The model was then applied to the study area to derive forest canopy height using predictor variables from (a) only the dry season, (b) only the wet season, and (c) both images. The allometry equation defined by Xu et al. (2017) was used to generate aboveground biomass maps from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images using the study area forest canopy height maps. Field plots of aboveground biomass measurements were compared to predicted aboveground biomass maps for validation purpose. Validation process revealed a better prediction of aboveground biomass (RMSE= 83.77 Mg.ha-1) when the forest canopy height maps derived with both images was used to estimate aboveground biomass

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p