Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

Microtopography mediates the climate-growth relationship and growth resilience to drought of Pinus tabulaeformis plantation in the hilly site

Understanding the factors affecting the growth of plantation forests can reduce the loss of economic and ecological values caused by plantation forest subhealth. Plantation forests are widely distributed in hilly areas with microtopographic features. Microtopography influences climatic factors associated with plant growth, during not only general time but also extreme events like droughts. However, little research has been conducted on the effects of microtopography on the plantation forest growth. In this paper, we selected Pinus tabulaeformis planted in a hilly site, and studied the effect of microtopography on the climate-growth relationship and drought response of a typical plantation in Northeast China using dendroecological methods. We found: 1) Between hill positions, temperature caused a climatic growth difference. Compared to the hilltop, the correlation of annual growth on the hillside with monthly temperature was more negative in July-August and less positive in January-April. 2) Between aspects, precipitation intensities caused a climatic growth difference. Compared to the sunny slope, the correlation of annual growth on the shady slope with monthly total precipitation below 10 mm/day was less positive (May-June) or more negative (March-April and July), while that with monthly total precipitation above 10 mm/day was more positive in most months.3) Drought response varied significantly based on hill position and aspect. There was no significant difference in resistance between hill positions, while recovery and resilience on the hilltop were greater than those on the hillside.Resistance, recovery, and resilience were all lower on the sunny slope than those on the shady slope. Overall, microtopography exists the effects on the growth of plantation forests, both in terms of climate-growth relationships in general climate and in response to drought when extreme events. Meanwhile, the climatic factors that caused the difference in growth of plantation forests between hill positions and aspects differed. The difference in growth between hill positions was caused by temperature, while that between aspects was caused by precipitation intensity. Drought response difference reflected the legacy effect of drought on plantation growth, which could lead to subsequent changes in climate-growth relationships. These findings demonstrate that strengthening the research of forest trees on microtopography is necessary for accurate carbon sink assessment and precise forest management.

A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass

Understanding the biomass, characteristics, and carbon sequestration of urban forests is crucial for maintaining and improving the quality of life and ensuring sustainable urban planning. Approaches to urban forest management have been incorporated into interdisciplinary, multifunctional, and technical efforts. In this review, we evaluate recent developments in urban forest research methods, compare the accuracy and efficiency of different methods, and identify emerging themes in urban forest assessment. This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly benefited the study of forest dynamics. Included in the review are light detection and ranging-based techniques for estimating urban forest biomass, deep learning algorithms that can extract tree crowns and identify tree species, methods for measuring large canopies using unmanned aerial vehicles to estimate forest structure, and approaches for capturing street tree information using street view images. Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics. There is an urgent need to combine multi-scale and spatiotemporal methods to improve urban forest detection at different scales.

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

Establishment of the Baseline for the IWRM in the Ecuadorian Andean Basins: Land Use Change, Water Recharge, Meteorological Forecast and Hydrological Modeling

This study was conducted in the Zamora Huayco (ZH) river basin, located in the inter-Andean region of southern Ecuador. The objective was to describe, through land use/land cover change (LUCC), the natural physical processes under current conditions and to project them to 2029. Moreover, temperature and precipitation forecasts were estimated to detail possible effects of climate change. Using remote sensing techniques, satellite images were processed to prepare a projection to 2029. Water recharge was estimated considering the effects of slope, groundcover, and soil texture. Flash floods were estimated using lumped models, concatenating the information to HEC RAS. Water availability was estimated with a semi-distributed hydrological model (SWAT). Precipitation and temperature data were forecasted using autoregressive and exponential smoothing models. Under the forecast, forest and shrub covers show a growth of 6.6%, water recharge projects an increase of 7.16%. Flood flows suffer a reduction of up to 16.54%, and the flow regime with a 90% of probability of exceedance is 1.85% (7.72 l/s) higher for 2029 than for the 2019 scenario, so an improvement in flow regulation is evident. Forecasts show an increase in average temperature of 0.11 °C and 15.63% in extreme rainfall by 2029. Therefore, intervention strategies in Andean basins should be supported by prospective studies that use these key variables of the system for an integrated management of water resources.

Effects of Land-Use Change on the Community Structure of the Dung Beetle (Scarabaeinae) in an Altered Ecosystem in Southern Ecuador

Simple Summary

This study analyzed the abundance and diversity of dung beetle communities at several disturbed sites in a tropical dry forest ecosystem in southern Ecuador. Dung beetle community diversity indices with different land uses were related to environmental variables (altitude, temperature), soil physicochemical properties, and food supply (manure). The results indicated that the species Canthon balteatus, Dichotomius problematicus, and Onthophagus confusus are abundant in disturbed sites, where soils are generally more compact and less fertile but contain a greater food supply. These findings can help decision makers to identify disturbed areas and to implement adequate policies for sustainable environmental management.

Abstract

This study evaluated the effects of land-use change (L-UCH) on dung beetle community structure (Scarabaeinae) in a disturbed dry ecosystem in southern Ecuador. Five different L-UCH classes were analyzed by capturing the dung beetle species at each site using 120 pitfall traps in total. To determine dung beetle abundance and diversity at each L-UCH, a general linear model (GLM) and a redundancy analysis (RDA) were applied, which correlated environmental and edaphic conditions to the community structure. Furthermore, changes in dung-producing vertebrate fauna were examined, which varied significantly between the different L-UCH classes due to the specific anthropogenic use or level of ecosystem disturbance. The results indicated that soil organic matter, pH, potassium, and phosphorus (RDA: component 1), as well as temperature and altitude (RDA: component 2) significantly affect the abundance of beetles (GLM: p value < 0.001), besides the food availability (dung). The highest abundance and diversity (Simpson’s index > 0.4, Shannon-Wiener index > 1.10) was found in highly disturbed sites, where soils were generally more compacted, but with a greater food supply due to the introduced farm animals. At highly disturbed sites, the species Canthon balteatus, Dichotomius problematicus, and Onthphagus confuses were found specifically, which makes them useful as bio-indicators for disturbed dry forest ecosystems in southern Ecuador.

Functional Diversity Changes after Selective Thinning in a Tropical Mountain Forest in Southern Ecuador

Background: The impact of selective thinning on forest diversity has been extensively studied in temperate and boreal regions. However, in the tropics, knowledge is still poor regarding the impacts of this silvicultural treatment on functional diversity, especially in tropical mountain forests, which are considered to be highly biodiverse ecosystems and also endangered by human activities. By evaluating the changes on functional diversity by using different indicators, hypothesizing that selective thinning significantly affects (directly or indirectly) tropical mountain forests, this work promotes sustainable ecosystem use. Methods: A total of 52 permanent plots of 2500 m2 each were installed in a primary mountain forest in the San Francisco Biological Reserve to assess the impact of this silvicultural treatment. Selective thinning can be defined as a controlled process, in which trees that compete with ecologically and/or valuable timber species are progressively removed to stimulate the development of profitable ones, called potential crop trees (PCT). In doing so, the best specimens remain in the forest stand until their final harvest. After PCT selection, 30 plots were chosen for the intervention, while 22 plots served as control plots. The thinning intensity fluctuated between 4 and 56 trees ha−1 (average 18.8 ± 12.1 stems ha−1). Functional Diversity (FD) indices, including the community weighted mean (CWM), were determined based on six traits using the FD package implemented in R software. The difference between initial and final conditions of functional richness (FRic), functional divergence (FDiv), functional evenness (FEve), functional dispersion (FDis), and Rao quadratic entropy (RaoQ) was modeled using linear mixed models (LMM). As fixed factors, we used all the predictors inherent to structural and ecological forest conditions before and after the selective thinning and as a random variable, we used the membership to nested sampling units. Results: Functional Richness (FRic) showed significant changes after selective thinning, the other indexes (FEve, FDis, FDiv, RaoQ) were only influenced by predictors related to ecological conditions and characteristics of the community.

Water Balance and Soil Moisture Deficit of Different Vegetation Units under Semiarid Conditions in the Andes of Southern Ecuador

Water availability in semiarid regions is endangered, which is not only due to changing climate conditions, but also to anthropogenic land use changes. The present study analyzed the annual and monthly water balance (WBc) and the soil moisture deficit (Ds) for different vegetation units under semiarid conditions in the Andes of southern Ecuador, based on limited meteorological station data and field measurements (soil samples). To calculate crop evapotranspiration (ETc) the Blaney–Criddle method was applied, and the specific crop factor (Kc) included, because only temperature (T) and precipitation (P) data were available. By means of the soil samples the water retention capacity (RC) of the different soil types present in the study area were estimated, which, in combination with WBc, provided reliable results respective to water surpluses or deficits for the different vegetation units. The results indicated highest Ds for cultivated areas, particularly for corn and sugarcane plantations, where annual deficits up to −1377.5 mm ha−1 and monthly deficits up to −181.1 mm ha−1 were calculated. Natural vegetation cover (scrubland, forest and paramo), especially at higher elevations, did not show any deficit throughout the year (annual surpluses up to 1279.6 mm ha−1; monthly surpluses up to 280.1 mm ha−1). Hence, it could be concluded that the prevailing climate conditions in semiarid regions cannot provide the necessary water for agricultural practices, for which reason irrigation is required. The necessary water can be supplied by areas coved by natural vegetation, but these areas are endangered due to population growth and the associated land use changes.

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow but also a basis for assessing the ecological function of urban forest and ecology. In this study, field observations and Sentinel-2A image data were used to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou. Results show that (1) Sentinel-2A data can be used for urban vegetation biomass estimation; (2) compared with the Boruta based multiple linear regression models, the stepwise regression models—also multiple linear regression models—achieve better estimations (RMSE = 7.99 t/hm2 for low vegetation, 45.66 t/hm2 for broadleaved forest, and 6.89 t/hm2 for coniferous forest); (3) the models for specific vegetation types are superior to the models for all-type vegetation; and (4) vegetation biomass is generally lowest in September and highest in January and December. Our study demonstrates the potential of the free Sentinel-2A images for urban ecosystem studies and provides useful insights on urban vegetation biomass estimation with such satellite remote sensing data.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data

The prediction of river discharge using hydrological models (HMs) is of utmost importance, especially in basins that provide drinking water or serve as recreation areas, to mitigate damage to civil structures and to prevent the loss of human lives. Therefore, different HMs must be tested to determine their accuracy and usefulness as early warning tools, especially for extreme precipitation events. This study simulated the river discharge in an Andean watershed, for which the distributed HM Runoff Prediction Model (RPM) and the semi-distributed HM Hydrologic Modelling System (HEC-HMS) were applied. As precipitation input data for the RPM model, high-resolution radar observations were used, whereas the HEC-HMS model used the available meteorological station data. The obtained simulations were compared to measured discharges at the outlet of the watershed. The results highlighted the advantages of distributed HM (RPM) in combination with high-resolution radar images, which estimated accurately the discharges in magnitude and time. The statistical analysis showed good to very good accordance between observed and simulated discharge for the RPM model (R2: 0.85–0.92; NSE: 0.77–0.82), whereas for the HEC-HMS model accuracies were lower (R2: 0.68–0.86; NSE: 0.26–0.78). This was not only due to the application of means values for the watershed (HEC-HMS), but also to limited rain gauge information. Generally, station network density in tropical mountain regions is poor, for which reason the high spatiotemporal precipitation variability cannot be detected. For hydrological simulation and forecasting flash floods, as well as for environmental investigations and water resource management, meteorological radars are the better choice. The greater availability of cost-effective systems at the present time also reduces implementation and maintenance costs of dense meteorological station networks.

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.

AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale

Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.