Critical loads and their exceedances at intensive forest monitoring sites in Europe

Forest and climate change in the inner-Alpine dry region of Visp Over the past decades, observed increases in temperature have been particularly pronounced in mountain regions. If this trend should continue in the 21st Century, frequency and intensity of droughts will increase, and will pose major challenges for forest management. Under current conditions drought-related tree mortality is already an important factor of forest ecosystems in dry inner-Alpine valleys. Here we assess the sensitivity of forest ecosystems to climate change and evaluate alternative forest management strategies in the Visp region. We integrate data from forest monitoring plots, field experiments and dynamic forests models to evaluate how the forest ecosystem services timber production, protection against natural hazards, carbon storage and biodiver-sity will be impacted. Our results suggest that at dry low elevation sites the drought tolerance of native tree species will be exceeded so that in the longer term a transition to more drought-adapted species should be considered. At medium elevations, drought and insect disturbances as by bark beetles are projected to be important for forest development, while at high elevations forests are projected to expand and grow better. All of the ecosystem services that we considered are projected to be impacted by changing forest conditions, with the specific impacts often being elevation-dependent. In the medium term, forest management that aims to increase the resilience of forests to drought can help maintain forest ecosystem services temporarily. However, our results suggest that relatively rigid management interventions are required to achieve significant effects. By using a combination of environmental monitoring, field experiments and modeling, we are able to gain insight into how forest ecosystem, and the services they provide, will respond to future changes.

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An introduction to critical loads

Environmental Pollution ◽

10.1016/0269-7491(92)90074-k ◽

1992 ◽

Vol 77 (2-3) ◽

pp. 173-176 ◽

Cited By ~ 26

Author(s):

K.R. Bull

Keyword(s):

Critical Loads

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Urban forest monitoring based on multiple features at the single tree scale by UAV

Urban Forestry & Urban Greening ◽

10.1016/j.ufug.2020.126958 ◽

2020 ◽

pp. 126958

Author(s):

Xiaofeng Wang ◽

Yi Wang ◽

Chaowei Zhou ◽

Lichang Yin ◽

Xiaoming Feng

Keyword(s):

Urban Forest ◽

Forest Monitoring ◽

Multiple Features ◽

Single Tree

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Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽

10.3390/f12020250 ◽

2021 ◽

Vol 12 (2) ◽

pp. 250

Author(s):

Wade T. Tinkham ◽

Neal C. Swayze

Keyword(s):

Structure From Motion ◽

Ponderosa Pine ◽

Point Cloud ◽

Image Resolution ◽

Canopy Height ◽

Computer Hardware ◽

Forest Monitoring ◽

Individual Tree ◽

Ponderosa Pine Forests ◽

Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽

10.3390/f12030327 ◽

2021 ◽

Vol 12 (3) ◽

pp. 327 ◽

Cited By ~ 1

Author(s):

Riccardo Dainelli ◽

Piero Toscano ◽

Salvatore Filippo Di Gennaro ◽

Alessandro Matese

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Relevant Information ◽

Forest Monitoring ◽

Machine Learning Techniques ◽

Planted Forests ◽

Systematic Analysis ◽

Aerial Photogrammetry ◽

Wide Range ◽

Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

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Investigation of Nanogrids for Improved Navy Installation Energy Resilience

Applied Sciences ◽

10.3390/app11094298 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4298

Author(s):

Alissa Kain ◽

Douglas L. Van Bossuyt ◽

Anthony Pollman

Keyword(s):

National Security ◽

Natural Disasters ◽

Systems Engineering ◽

Critical Loads ◽

Electrical Energy ◽

Terrorist Attacks ◽

Military Bases ◽

Specific Interest ◽

The Military

Military bases perform important national security missions. In order to perform these missions, specific electrical energy loads must have continuous, uninterrupted power even during terrorist attacks, adversary action, natural disasters, and other threats of specific interest to the military. While many global military bases have established microgrids that can maintain base operations and power critical loads during grid disconnect events where outside power is unavailable, many potential threats can cause microgrids to fail and shed critical loads. Nanogrids are of specific interest because they have the potential to protect individual critical loads in the event of microgrid failure. We present a systems engineering methodology that analyzes potential nanogrid configurations to understand which configurations may improve energy resilience and by how much for critical loads from a national security perspective. This then allows targeted deployment of nanogrids within existing microgrid infrastructures. A case study of a small military base with an existing microgrid is presented to demonstrate the potential of the methodology to help base energy managers understand which options are preferable and justify implementing nanogrids to improve energy resilience.

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Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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Microgrid Simulation Analysis of Critical Loads: Distribution Planning for Nonhomogenous EV Distributions

2021 IEEE International Electric Machines & Drives Conference (IEMDC) ◽

10.1109/iemdc47953.2021.9449554 ◽

2021 ◽

Author(s):

George Nail ◽

Olga Lavrova ◽

Jamal Mamkhezri ◽

Nataraj Pragallapati

Keyword(s):

Critical Loads ◽

Simulation Analysis ◽

Distribution Planning

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Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Remote Sensing ◽

10.3390/rs13122297 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2297

Author(s):

Jonathon J. Donager ◽

Andrew J. Sánchez Meador ◽

Ryan C. Blackburn

Keyword(s):

Ponderosa Pine ◽

Laser Scanning ◽

Canopy Cover ◽

Basal Area ◽

Tree Height ◽

Absolute Error ◽

Forest Monitoring ◽

Individual Tree ◽

Structural Configurations ◽

Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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