scholarly journals Effects of Spatial Resolution on Burned Forest Classification With ICESat-2 Photon Counting Data

2021 ◽  
Vol 2 ◽  
Author(s):  
Meng Liu ◽  
Sorin Popescu ◽  
Lonesome Malambo
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Spatial Resolution ◽  
Temperate Forest ◽  
Canopy Structure ◽  
Segment Length ◽  
Retrieval Algorithm ◽  
Vegetation Height ◽  
Forest Classification ◽  
Burned Forest

Accurately monitoring forest fire activities is critical to understanding carbon dynamics and climate change. Three-dimensional (3D) canopy structure changes caused by fire make it possible to adopt Light Detection and Ranging (LiDAR) in burned forest classification. This study focuses on the effects of spatial resolution when using LiDAR data to differentiate burned and unburned forests. The National Aeronautics and Space Administration’s (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission provides LiDAR datasets such as the geolocated photon data (ATL03) and the land vegetation height product (ATL08), which were used in this study. The ATL03 data were filtered by two algorithms: the ATL08 algorithm (ILV) and the adaptive ground and canopy height retrieval algorithm (AGCH), producing classified canopy points and ground points. Six typical spatial resolutions: 10, 30, 60, 100, 200, and 250 m were employed to divide the classified photon points into separate segments along the track. Twenty-six canopy related metrics were derived from each segment. Sentinel-2 images were used to provide reference land cover maps. The Random Forest classification method was employed to classify burned and unburned segments in the temperate forest in California and the boreal forest in Alberta, respectively. Both weak beams and strong beams of ICESat-2 data were included in comparisons. Experiment results show that spatial resolution can significantly influence the canopy structures we detected. Classification accuracies increase along with coarser spatial resolutions and saturate at 100 m segment length, with overall accuracies being 79.43 and 92.13% in the temperate forest and the boreal forest, respectively. Classification accuracies based on strong beams are higher than those of using weak beams due to a larger point density in strong beams. The two filtering algorithms present comparable accuracies in burned forest classification. This study demonstrates that spatial resolution is a critical factor to consider when using spaceborne LiDAR for canopy structure characterization and classification, opening an avenue for improved measurement of forest structures and evaluation of terrestrial vegetation responses to climate change.

scholarly journals Distinct Climate Effects on Dahurian Larch Growth at an Asian Temperate-Boreal Forest Ecotone and Nearby Boreal Sites

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Enzai Du ◽  
Yang Tang
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Temperate Forest ◽  
Growing Season ◽  
Maximum Temperature ◽  
Dahurian Larch ◽  
Growth Responses ◽  
Significant Difference ◽  
Growing Season Precipitation ◽  
Forest Ecotone

Climate change is exerting profound impacts on the structure and function of global boreal forest. Compared with their northern counterparts, trees growing at the southern boreal forest and the temperate-boreal forest ecotone likely show distinct responses to climate change. Based on annual basal areal increment (BAI) of Dahurian larch (Larix gmelinii Rupr.) plantations with similar ages, tree densities and soil nutrient conditions, we investigated the tree growth responses to inter-annual climate variations at an Asian temperate-boreal forest ecotone and nearby boreal sites in northeast China. Annual BAI changed nonlinearly with cambial age in the form of a lognormal curve. The maximum annual BAI showed no significant difference between the two bioregions, while annual BAI peaked at an elder age at the boreal-temperate forest ecotone. After eliminating the age associated trend, conditional regression analyses indicate that residual BAI at the boreal sites increased significantly with higher growing-season mean nighttime minimum temperature and non-growing-season precipitation, but decreased significantly with higher growing-season mean daytime maximum temperature during the past three decades (1985–2015). In contrast, residual BAI at the boreal-temperate forest ecotone only showed a positive and weak response to inter-annual variations of growing-season precipitation. These findings suggest distinct effects of inter-annual climate variation on the growth of boreal trees at the temperate-boreal forest ecotone in comparison to the southern boreal regions, and highlight future efforts to elucidate the key factors that regulate the growth ofthe southernmost boreal trees.

scholarly journals Stand-level drivers most important in determining boreal forest response to climate change

2017 ◽  
Vol 106 (3) ◽  
pp. 977-990 ◽  
Author(s):  
Yan Boulanger ◽  
Anthony R. Taylor ◽  
David T. Price ◽  
Dominic Cyr ◽  
Guillaume Sainte-Marie
Keyword(s):  
Climate Change ◽  
Boreal Forest

Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

2021 ◽  
Author(s):  
Antonello Bonfante ◽  
Arturo Erbaggio ◽  
Eugenia Monaco ◽  
Rossella Albrizio ◽  
Pasquale Giorio ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
State Estimation ◽  
Spectral Data ◽  
Spatial Resolution ◽  
Spectral Region ◽  
Vegetation Indices ◽  
Plant Management ◽  
Spectral Vegetation Indices ◽  
Data Source

<p>Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.</p><p>The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.</p><p>The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of <2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across  400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.</p><p>The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).</p><p>The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.</p><p>The contribution has been realized within the CNR BIO-ECO project.</p>

scholarly journals Impact of climate change on hydropower production in the Upper Danube

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Transfer Functions ◽  
Danube River ◽  
Emission Scenarios ◽  
Hydropower Generation ◽  
Climate Change Projections ◽  
Impact Of Climate Change ◽  
The Future ◽  
The Impact

<p>In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.</p><p>A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810<sup></sup>km<sup>2</sup> with a mean annual discharge of 1923 m<sup>3</sup>/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the “Green Deal” initiative.</p><p>Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.</p><p>The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.</p><p> </p><p> </p><p>References:</p><p>Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987–999. https://doi.org/10.1016/j.jhydrol.2018.06.057</p><p> </p>

scholarly journals Spatial Variation in Canopy Structure across Forest Landscapes

2018 ◽  
Author(s):  
Brady S. Hardiman ◽  
Elizabeth A. LaRue ◽  
Jeff W. Atkins ◽  
Robert T. Fahey ◽  
Franklin W. Wagner ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Forest Canopy ◽  
Spatial Scales ◽  
Canopy Structure ◽  
Ecosystem Functions ◽  
Lidar System ◽  
Stable Point ◽  
Eastern Usa ◽  
Forested Landscapes ◽  
Patch Scale

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy lidar system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10’s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.

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