Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest, California

This study deals with the biomass estimation of the understory species of Pinus halepensis forests in the Kassandra peninsula, Chalkidiki (North Greece). These species are: Quercus coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus unedo, Erica arborea, Erica manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis, Fraxinus ornus. A sample of 30 shrubs per species was taken and the dry and fresh weights and the moisture content of every component of each species were measured, all of which were processed for aboveground biomass data. Then several regression equations were examined to determine the key words.

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Exploring the Use of Ecosystem Services Conceptual Models to Account for the Benefits of Public Lands: An Example from National Forest Planning in the United States

Forests ◽

10.3390/f12030267 ◽

2021 ◽

Vol 12 (3) ◽

pp. 267

Author(s):

Lydia Olander ◽

Katie Warnell ◽

Travis Warziniack ◽

Zoe Ghali ◽

Chris Miller ◽

...

Keyword(s):

Ecosystem Services ◽

Ecosystem Service ◽

Management Strategies ◽

Public Lands ◽

The United States ◽

Forest Planning ◽

National Forest ◽

Planning Team ◽

National Forest Planning

A shared understanding of the benefits and tradeoffs to people from alternative land management strategies is critical to successful decision-making for managing public lands and fostering shared stewardship. This study describes an approach for identifying and monitoring the types of resource benefits and tradeoffs considered in National Forest planning in the United States under the 2012 Planning Rule and demonstrates the use of tools for conceptualizing the production of ecosystem services and benefits from alternative land management strategies. Efforts to apply these tools through workshops and engagement exercises provide opportunities to explore and highlight measures, indicators, and data sources for characterizing benefits and tradeoffs in collaborative environments involving interdisciplinary planning teams. Conceptual modeling tools are applied to a case study examining the social and economic benefits of recreation on the Ashley National Forest. The case study illustrates how these types of tools facilitate dialog for planning teams to discuss alternatives and key ecosystem service outcomes, create easy to interpret visuals that map details in plans, and provide a basis for selecting ecosystem service (socio-economic) metrics. These metrics can be used to enhance environmental impact analysis, and help satisfy the goals of the National Environmental Policy Act (NEPA), the 2012 Planning Rule, and shared stewardship initiatives. The systematic consideration of ecosystem services outcomes and metrics supported by this approach enhanced dialog between members of the Forest planning team, allowed for a more transparent process in identification of key linkages and outcomes, and identified impacts and outcomes that may not have been apparent to the sociologist who is lacking the resource specific expertise of these participants. As a result, the use of the Ecosystem Service Conceptual Model (ESCM) process may result in reduced time for internal reviews and greater comprehension of anticipated outcomes and impacts of proposed management in the plan revision Environmental Impact Statement amongst the planning team.

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Lead (Pb) Contamination Along a Hillslope in the Tonto National Forest, AZ: A Case Study of Recreational Shooting Using GIS Analysis

Bulletin of Environmental Contamination and Toxicology ◽

10.1007/s00128-021-03154-2 ◽

2021 ◽

Author(s):

Abeer Hamdan

Keyword(s):

National Forest ◽

Gis Analysis ◽

Pb Contamination

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Contrasting Competitor and Predator Responses to a Short-Lived Southern Pine Beetle Outbreak: A Case Study

Forest Science ◽

10.1093/forsci/fxaa041 ◽

2020 ◽

Author(s):

Stephen R Clarke ◽

Jessica Hartshorn

Keyword(s):

Natural Enemy ◽

Bark Beetles ◽

Southern Pine Beetle ◽

National Forest ◽

Population Fluctuations ◽

Thanasimus Dubius ◽

Southern Pine ◽

Widespread Application ◽

Pine Beetle

Abstract The southern pine beetle (SPB) Dendroctonus frontalis Zimmermann, is the most important insect pest of pines in the southeastern United States, with outbreaks often resulting in thousands of hectares of pine mortality. Natural enemies and competitors have been cited as significant regulators of SPB populations and, therefore, outbreaks. A recent outbreak on the Homochitto National Forest (NF) in Mississippi provided an opportunity to undertake a case study comparing population fluctuations of SPB, its major predator Thanasimus dubius, and its competitors, Ips bark beetles. Trap catches of all three were tracked through the course of the outbreak on the Homochitto NF as well as in two other forests with low or no SPB activity. The number of predators collected initially increased on the Homochitto NF in response to the SPB outbreak, but their impact on reducing infestation numbers was unclear. Numbers of Ips trapped were similar across all three forests, indicating that other factors were regulating SPB populations. The outbreak only lasted a single year, and its brevity likely limited the availability of host resources for natural enemy and competitor populations. Additional studies are warranted to explore the mechanisms affecting the extent and duration of SPB outbreaks, such as active forest management. Study Implications: The widespread application of cut-and-leave treatments during a short duration southern pine beetle (SPB) outbreak on the Homochitto National Forest did not result in immediate increased collections of Ips bark beetles, competitors of SPB. Similar population trends of Ips bark beetles in areas with and without SPB infestations suggest that other factors, such as climate, regulate population numbers. Low numbers of the main SPB predator, Thanasimus dubius, immediately preceded an outbreak, and trap collections increased after outbreak onset, reaffirming the importance of this natural enemy in SPB population fluctuations.

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Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Remote Sensing ◽

10.3390/rs13081595 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1595

Author(s):

Chunhua Li ◽

Lizhi Zhou ◽

Wenbin Xu

Keyword(s):

Aboveground Biomass ◽

Carbon Accounting ◽

Dynamic Monitoring ◽

Gradient Boosting ◽

Biomass Estimation ◽

Floodplain Wetlands ◽

Red Edge ◽

Extreme Gradient Boosting ◽

Ensemble Algorithms ◽

Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Remote Sensing ◽

10.3390/rs13040581 ◽

2021 ◽

Vol 13 (4) ◽

pp. 581 ◽

Cited By ~ 2

Author(s):

Yuanyuan Fu ◽

Guijun Yang ◽

Xiaoyu Song ◽

Zhenhong Li ◽

Xingang Xu ◽

...

Keyword(s):

Winter Wheat ◽

Least Squares ◽

Aboveground Biomass ◽

Regression Models ◽

Digital Images ◽

Predictive Performance ◽

Estimation Accuracy ◽

Support Vector ◽

Biomass Estimation ◽

High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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