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Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were compared to assess the value of cooling services of three forest types at a local scale. Using the window searching strategy, land surface temperature and sensible heat flux differences between forest and open land were compared. The average cooling temperature of broad-leaved forests was found to be 0.229 °C, significantly higher than that of coniferous forests, at 0.205 °C, while mixed coniferous–broad-leaved forests were not significantly different to the other two types. The average sensible heat flux differences in broad-leaved, coniferous, and coniferous–broad-leaved forests were found to be 0.23, 0.079, and 0.11 MJ/m2/day, respectively. According to the correlation analysis, the sensible heat flux was significantly correlated with the cooling degree (R = 0.33, p = 0.05), suggesting consistency between the two approaches. However, the total cooling value calculated with the energy balance method was CNY 0.51 billion, significantly higher than the temperature difference method at CNY 0.11 billion. The main reason for the differences between the two approaches is the uncertainty in cooling volume and cooling time for the temperature difference method and energy balance method, respectively. The impact of vegetation on the microclimate depends on the vegetation type, topography, local climate, and other factors. It is also important to note that cooling services are not required at all times of the day, and energy differences can hardly be calculated based on the hour. However, surface radiation and evapotranspiration generally occur during the daytime, which is also when the surface temperature is high. Therefore, there is a certain coincidence with the time when cooling is needed. The energy balance method presented herein provides a novel alternative approach to assessing the cooling services of local-scale forests, offering advantages over the commonly used temperature difference approach, which is associated with large uncertainty. [ABSTRACT FROM AUTHOR]

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus. [ABSTRACT FROM AUTHOR]

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian's Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies. [ABSTRACT FROM AUTHOR]

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus.

The awareness of spatial and temporal variations in site-specific crop parameters, such as aboveground biomass (total dry weight: (TDW), plant length (PL) and leaf area index (LAI), help in formulating appropriate management decisions. However, conventional monitoring methods rely on time-consuming manual field operations. In this study, the feasibility of using an unmanned aerial vehicle (UAV)-based remote sensing approach for monitoring growth in rice was evaluated using a digital surface model (DSM). Approximately 160 images of paddy fields were captured during each UAV survey campaign over two vegetation seasons. The canopy surface model (CSM) was developed based on the differences observed between each DSM and the first DSM after transplanting. Mean canopy height (CH) was used as a variable for the estimation models of LAI and TDW. The mean CSM of the mesh covering several hills was sufficient to explain the PL (R2 = 0.947). TDW and LAI prediction accuracy of the model were high (relative RMSE of 20.8% and 28.7%, and RMSE of 0.76 m2 m−2 and 141.4 g m−2, respectively) in the rice varieties studied (R2 = 0.937 (Basmati370), 0.837 (Nipponbare and IR64) for TDW, and 0.894 (Basmati370), 0.866 (Nipponbare and IR64) for LAI). The results of this study support the assertion of the benefits of DSM-derived CH for predicting biomass development. In addition, LAI and TDW could be estimated temporally and spatially using the UAV-based CSM, which is not easily affected by weather conditions. [ABSTRACT FROM AUTHOR]

The use of individual tree detection methods to support forest management inventories has been a research topic for over two decades, but a formal assessment of these methods to produce stand-level and region-level predictions of forest attributes and measures of error is lacking. We employed model-based estimation methods in conjunction with the semi-individual tree crown approach (s-ITC) to produce predictions and measures of error for tree volume (VOL), basal area (BA), stem density (DEN), and quadratic mean diameter (QMD) at the scale of forest stands and the entire study region. We compared the s-ITC approach against the area-based approach (ABA) for predictions of region-level and stand-level attributes via model-based root mean squared errors (RMSEs). The study was conducted at the Panther Creek watershed in Oregon, USA using a set of 78 field plots and aerial lidar information. For region-level attributes, s-ITC RMSEs demonstrated changes between −31% and 17% relative to ABA models. At the stand level, median s-ITC RMSEs generally increased, with changes between −29% and 414% relative to ABA models, but demonstrated important reductions in stands where segmentation provided large increases in sample size and was less prone to extrapolation than ABA models. The ABA demonstrated smaller RMSEs in stands without sampled population units for all variables. Our findings motivate further research into niche applications where s-ITC models may consistently outperform ABA models. [ABSTRACT FROM AUTHOR]

The planned launch of a spaceborne P-band radar mission and the availability of C- and L-band data from several spaceborne missions suggest investigating the complementarity of C-, L-, and P-band backscatter with respect to the retrieval of forest above-ground biomass. Existing studies on the retrieval of biomass with multi-frequency backscatter relied on single observations of the backscatter and were thus not able to demonstrate the potential of multi-temporal C- and L-band data that are now available from spaceborne missions. Based on spaceborne C- and L-band and airborne P-band images acquired over a forest site in southern Sweden, we investigated whether C- and L-band backscatter may complement retrievals of above-ground biomass from P-band. To this end, a retrieval framework was adopted that utilizes a semi-empirical model for C- and L-bands and an empirical parametric model for P-band. Estimates of above-ground biomass were validated with the aid of 20 m-diameter plots and a LiDAR-derived biomass map with 100 m × 100 m pixel size. The highest retrieval accuracy when not combining frequencies was obtained for P-band with a relative root mean square error (RMSE) of 30% at the hectare scale. The retrieval with multi-temporal L- and C-bands produced errors of the order of 40% and 50%, respectively. The P-band retrieval could be improved for 4% when using P-, L-, and C-bands jointly. The combination of C- and L-bands allowed for retrieval accuracies close to those achieved with P-band. A crucial requirement for achieving an error of 30% with C- and L-bands was the use of multi-temporal observations, which was highlighted by the fact that the retrieval with the best individual L-band image was associated with an error of 61%. The results of this study reconfirmed that P-band is the most suited frequency for the retrieval of above-ground biomass of boreal forests based on backscatter, but also highlight the potential of multi-temporal C- and L-band imagery for mapping above-ground biomass, for instance in areas where the planned ESA BIOMASS P-band mission will not be allowed to acquire data. [ABSTRACT FROM AUTHOR]