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

Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in aWestern Amazonian Forest Using Airborne LiDAR

Mapping aboveground carbon density in tropical forests can support CO2 emissionmonitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accuracy and generality of calibrations of LiDAR-based aboveground carbon density (ACD) predictions with those obtained from field inventory techniques should be intensified in order to advance tropical forest carbon mapping. Here we present results from the application of a general ACD estimation model applied with small-footprint LiDAR data and field-based estimates of a 50-ha forest plot in Ecuador?s Yasuní National Park. Subplots used for calibration and validation of the general LiDAR equation were selected based on analysis of topographic position and spatial distribution of aboveground carbon stocks. The results showed that stratification of plot locations based on topography can improve the calibration and application of ACD estimation using airborne LiDAR (R2 = 0.94, RMSE = 5.81 Mg?C? ha?1, BIAS = 0.59). These results strongly suggest that a general LiDAR-based approach can be used for mapping aboveground carbon stocks in western lowland Amazonian forests

Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2 ) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots – six for each species – and three different fixed-radius designs (i.e. 7, 15, and 30 m) were estab lished within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.European Commission (EC) FP7-315165Ministerio de Economía, Industria y Competitividad QUERCUSAT (CLG2013-40790-R

Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

100022Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard.S

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

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Biomass forest modelling using UAV LiDAR data under fire effect

Mestrado em Engenharia Florestal e dos Recursos Naturais / Instituto Superior de Agronomia. Universidade de LisboaThe main goal of the study is to analyse the possibility of quantifying the loss of biomass in burned forest stands using Light Detection and Ranging (LiDAR) data. Since wildfires are not uncommon in Mediterranean areas, it is useful to quantify the magnitude of fire damage in forests. With the use of remote sensing, it is possible to plan post-fire recovery management and to quantify the losses of biomass and carbon stock. Mata Nacional de Leiria (MNL) was chosen, because, after the fire in October 2017, it showed areas with low and medium-high fire severity. MNL is divided in several rectangular management units (MU). To achieve our objective, it was necessary to find a MU with burned and unburned areas. In this selection process, we used Sentinel-2 images. The fire severity was estimated by deriving a spectral index related with the effects of fire and to compute the temporal difference (pre- minus post-fire) of this index, the delta normalized burn ratio (DNBR). Forest inventory was carried out in four plots installed in the selected MU. Allometric equations were used to estimate values of stand aboveground biomass. These values were used to fit a relationship with data extracted from LiDAR cloud metrics. The LiDAR data were acquired with a VLP-16 Velodyne LiDAR PUCK™ mounted on an Unmanned Aerial Vehicles (UAV) at an altitude of 60 m above the ground. The point clouds were then processed with the FUSION software until a cloud metrics was generated and then regression models were used to fit equations related to LiDAR-derived parameters. Two biomass equations were fit, one with the whole tree metrics having a R² = 0,95 and a second one only considering the tree crown metrics presenting a R² = 0,93. The state of the forest (unburned/burned) was significant on the final equationN/

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

ABSTRACT: Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government