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

Terrestrial Laser Scanning to Detect Liana Impact on Forest Structure

Tropical forests are currently experiencing large-scale structural changes, including an increase in liana abundance and biomass. Higher liana abundance results in reduced tree growth and increased tree mortality, possibly playing an important role in the global carbon cycle. Despite the large amount of data currently available on lianas, there are not many quantitative studies on the influence of lianas on the vertical structure of the forest. We study the potential of terrestrial laser scanning (TLS) in detecting and quantifying changes in forest structure after liana cutting using a small scale removal experiment in two plots (removal plot and non-manipulated control plot) in a secondary forest in Panama. We assess the structural changes by comparing the vertical plant profiles and Canopy Height Models (CHMs) between pre-cut and post-cut scans in the removal plot. We show that TLS is able to detect the local structural changes in all the vertical strata of the plot caused by liana removal. Our study demonstrates the reproducibility of the TLS derived metrics for the same location confirming the applicability of TLS for continuous monitoring of liana removal plots to study the long-term impacts of lianas on forest structure. We therefore recommend to use TLS when implementing new large scale liana removal experiments, as the impact of lianas on forest structure will determine the aboveground competition for light between trees and lianas, which has important implications for the global carbon cycle

Forest Biomass and Land Cover Change Assessment of the Margalla Hills National Park in Pakistan Using a Remote Sensing Based Approach

Climate change is one of the greatest threats recently, of which the developing countries are facing most of the brunt. In the fight against climate change, forests can play an important role, since they hold a substantial amount of terrestrial carbon and can therefore affect the global carbon cycle. Forests are also an essential source of livelihood for a remarkably high proportion of people worldwide and a harbor for rich global biodiversity. Forests are however facing high deforestation rates. Deforestation is regarded as the most widespread process of land cover change (LCC), which is the conversion of one land cover type to the other land cover type. Most of this deforestation occurs in developing countries. Agricultural expansion has been reported as the most significant widespread driver of deforestation in Asia, Africa, and Latin America. This deforestation is altering the balance of forest carbon stocks and threatening biodiversity. Pakistan is also a low forest cover country and faces high deforestation rates at the same time, due to the high reliance of local communities on forests. Moreover, it is also the most adversely affected by climate change. Agricultural expansion and population growth have been regarded as the most common drivers of deforestation in Pakistan. Financial incentives such as ‘Reducing Emissions from Deforestation and Forest Degradation, and the Role of Conservation of Forest Carbon, Sustainable Management of Forests and Enhancement of Forest Carbon Stocks’ (REDD+) offer hope for developing countries for not only halting deforestation but also alleviating poverty. However, such initiatives require the estimation of biomass and carbon stocks of the forest ecosystems. Therefore, it becomes necessary that the biomass and carbon potentials of the forests are explored, as well as the LCCs are investigated for identifying the deforestation and forest degradation hit areas. Based on the aforementioned, the following research objectives/sub-objectives were investigated in the MHNP, which is adjoined with the capital city of Pakistan, Islamabad; A) Forest Biomass and Carbon Stock Assessment of Margalla Hills National Park (MHNP) A.1) Aboveground Biomass (AGB) and Aboveground Carbon (AGC) assessment of the Subtropical Chir Pine Forest (SCPF) and Subtropical Broadleaved Evergreen Forest (SBEF) using Field Inventorying Techniques A.2) Exploring linear regression relationship between Sentinel-1 (S1) and Sentinel-2 (S2) satellite data with the AGB of SCPF and SBEF A.3) AGB estimation combining remote sensing and machine learning approach B) LC Classification and Land Cover Change Detection (LCCD) of MHNP for the time-period between 1999 and 2019 B.1) LC Classification for the years 1999, 2009 and 2019 using Machine Learning Algorithm B.2) LCCD of MHNP between 1999 to 2019

Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review

Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB). New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses

Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms to sample the earth's vegetation while in orbit aboard the International Space Station. In this paper, we used airborne large-footprint (LF) lidar measurements to simulate GEDI observations from which we retrieved ground elevation, vegetation height, and aboveground biomass (AGB). GEDI-like product accuracy was then assessed by comparing them to similar products derived from airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and European Space Agency (ESA) AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density, and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE = 0.5 m, bias = 0.29 m) and maximum forest height (RMSE = 2.99 m, bias = 0.24 m) over the study area. The difference in the AGB estimated from both lidar instruments at the 1-ha spatial scale is small over the entire study area (RMSE = 6.34 Mg·ha-1, bias = 11.27 Mg·ha-1) and the bias is attributed to the impact of ground slopes greater than 10–20° on the LF lidar measurements of forest height. Our results support the ability of GEDILF lidar to measure the complex structure of humid tropical forests and provide AGB estimates comparable to SF-derived ones

Remote Sensing-Based Biomass Estimation

Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation

Estimating stand top height using freely distributed ICESat-2 LiDAR Data: a case study from multi-species forests in Artvin

Forest inventories require up-to-date data on dominant tree height and stand top height from forest sample plots. These data are used to characterize the vertical structure of forests, providing a baseline for volume and yield tables as well as many other biomass studies. Obtaining height information through ground measurement is laborious, costly, and time-consuming. The aim of this study is to estimate stand top heights of the Artvin-Hatila Valley’s forests using freely available laser scanning (LiDAR) data from the ICESat-2 satellite for the first time in Turkey. For this purpose, the dominant tree heights, traditionally measured by digital hypsometer in 52 sample plots, were evaluated by stand types and compared with the ICESat-2 canopy data. Then, two data sets were modeled using the Convolutional Neural Network (CNN) and simple regression methods. The model accuracies were evaluated with correlation (Pearson’s R), coefficient of determination (R2 ), and root mean squared error (RMSE) using ground-based data. The results showed that the CNN-based model performed better than the linear regression model in height estimation. Its R, R2 , and RMSE values were .82, .68, and 4.2 m, respectively. As for stand types, broadleaves-dominated, mature, and fully covered stands seem more appropriate for top height modeling with spaceborne LiDAR data. Degraded, coniferous, and young stands, as well as non-forest areas, barely allow accurate top height estimations due to their complex canopy surfaces and small openings among trees. Given the promising results, we conclude that satellite-based LiDAR systems provide opportunities to forest professionals as a free auxiliary data source for operational forest management in Turkey

Woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

International audienceIn 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community