Reconstruction of the disturbance history of a temperate coniferous forest through stand-level analysis of airborne LiDAR data

2019 ◽  
Author(s):  
Nuria Sanchez-Lopez ◽  
Luigi Boschetti ◽  
Andrew T Hudak
Keyword(s):  
Forest Canopy ◽  
Stand Structure ◽  
Explanatory Power ◽  
Coniferous Forest ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Disturbance History ◽  
History Of ◽  
Level Analysis

Abstract Spatially explicit information about stand-level Time Since the last stand-replacing Disturbance (TSD) is fundamental for modelling many forest ecosystem processes, but most of the current satellite remote sensing mapping approaches are based on change detection and time series analysis, and can detect only disturbances that have occurred since the start of the optical satellite data record. The spatial legacy of stand-replacing disturbances can however persist on the landscape for several decades to centuries, in the form of distinct horizontal and vertical stand structure features. We propose a new approach to reconstruct the long-term disturbance history of a forest, estimating TSD through stand-level analysis of LiDAR data, which are highly sensitive to the three-dimensional forest canopy structure. The study area is in the Nez Perce-Clearwater National Forest in north-central Idaho, where airborne LiDAR covering about 52,000 ha and ancillary TSD reference data for a period of more than 140 years were available. The root mean square difference (RSMD) between predicted and reference TSD was 17.5 years with a BIAS of 0.8 years; and on 72.8% of the stands the predicted TSD was less than 10 years apart from the reference TSD (78.2% of the stands when considering only disturbances occurred in the last 100 years). The results demonstrate that airborne LiDAR-derived data have enough explanatory power to reconstruct the long-term, stand-replacing disturbance history of temperate forested areas at regional scales.

scholarly journals Estimation of crown and canopy cover from airborne lidar data

2010 ◽  
Vol 52 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Mait Lang
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Stand Structure ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Silver Birch ◽  
Lidar Data ◽  
Crown Cover ◽  
Pine Trees ◽  
Airborne Lidar Data

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

scholarly journals Using ForeStereo and LIDAR data to assess fire and canopy structure-related risks in relict Abies pinsapo Boiss. forests

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10158
Author(s):  
Álvaro Cortés-Molino ◽  
Isabel Aulló-Maestro ◽  
Ismael Fernandez-Luque ◽  
Antonio Flores-Moya ◽  
José A. Carreira ◽  
...  
Keyword(s):  
Stand Structure ◽  
Secondary Growth ◽  
Canopy Structure ◽  
Airborne Lidar ◽  
Distribution Area ◽  
Lidar Data ◽  
Fire Simulation ◽  
Entire Area ◽  
Fuel Models ◽  
Abies Pinsapo

In this study we combine information from aerial LIDAR and hemispherical images taken in the field with ForeStereo—a forest inventory device—to assess the vulnerability and to design conservation strategies for endangered Mediterranean fir forests based on the mapping of fire risk and canopy structure spatial variability. We focused on the largest continuous remnant population of the endangered tree species Abies pinsapo Boiss. spanning 252 ha in Sierra de las Nieves National Park (South Andalusia, Spain). We established 49 sampling plots over the study area. Stand structure variables were derived from ForeStereo device, a proximal sensing technology for tree diameter, height and crown dimensions and stand crown cover and basal area retrieval from stereoscopic hemispherical images photogrammetry. With this information, we developed regression models with airborne LIDAR data (spatial resolution of 0.5 points∙m−2). Thereafter, six fuel models were fitted to the plots according to the UCO40 classification criteria, and then the entire area was classified using the Nearest Neighbor algorithm on Sentinel imagery (overall accuracy of 0.56 and a KIA-Kappa Coefficient of 0.46). FlamMap software was used for fire simulation scenarios based on fuel models, stand structure, and terrain data. Besides the fire simulation, we analyzed canopy structure to assess the status and vulnerability of this fir population. The assessment shows a secondary growth forest that has an increasing presence of fuel models with the potential for high fire spread rate fire and burn probability. Our methodological approach has the potential to be integrated as a support tool for the adaptive management and conservation of A. pinsapo across its whole distribution area (<4,000 ha), as well as for other endangered circum-Mediterranean fir forests, as A. numidica de Lannoy and A. pinsapo marocana Trab. in North Africa.

scholarly journals Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study

2020 ◽  
Vol 12 (21) ◽  
pp. 3506
Author(s):  
Nuria Sanchez-Lopez ◽  
Luigi Boschetti ◽  
Andrew T. Hudak ◽  
Steven Hancock ◽  
Laura I. Duncanson
Keyword(s):  
Data Fusion ◽  
Large Scale ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Training Dataset ◽  
Lidar Data ◽  
Landsat 8 ◽  
Image Objects ◽  
Airborne Lidar Data

Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.

scholarly journals Long-term field performance of a tunable diode laser absorption spectrometer for analysis of carbon isotopes of CO<sub>2</sub> in forest air

2008 ◽  
Vol 8 (17) ◽  
pp. 5263-5277 ◽  
Author(s):  
S. M. Schaeffer ◽  
J. B. Miller ◽  
B. H. Vaughn ◽  
J. W. C. White ◽  
D. R. Bowling
Keyword(s):  
Diode Laser ◽  
Forest Canopy ◽  
Coniferous Forest ◽  
Tunable Diode Laser ◽  
Entire Study ◽  
Laser Absorption ◽  
Instrument Performance ◽  
Diode Laser Absorption ◽  
Term Field

Abstract. Tunable diode laser absorption spectrometry (TDLAS) is gaining in popularity for measuring the mole fraction [CO2] and stable isotopic composition (δ13C) of carbon dioxide (CO2) in air in studies of biosphere-atmosphere gas exchange. Here we present a detailed examination of the performance of a commercially-available TDLAS located in a high-altitude subalpine coniferous forest (the Niwot Ridge AmeriFlux site), providing the first multi-year analysis of TDLAS instrument performance for measuring CO2 isotopes in the field. Air was sampled from five to nine vertical locations in and above the forest canopy every ten minutes for 2.4 years. A variety of methods were used to assess instrument performance. Measurement of two compressed air cylinders that were in place over the entire study establish the long-term field precision of 0.2 μmol mol−1 for [CO2] and 0.35‰ for δ13C, but after fixing several problems the isotope precision improved to 0.2‰ (over the last several months). The TDLAS provided detail on variability of δ13C of atmospheric CO2 that was not represented in weekly flask samples, as well as information regarding the influence of large-scale (regional) seasonal cycle and local forest processes on [CO2] and δ13C of CO2. There were also clear growing season and winter differences in the relative contributions of photosynthesis and respiration on the [CO2] and δ13C of forest air.

scholarly journals Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis

2016 ◽  
pp. 103 ◽  
Author(s):  
J. Guerra-Hernández ◽  
M. Tomé ◽  
E. González-Ferreiro
Keyword(s):  
Regression Models ◽  
Deciduous Forest ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Mediterranean Forest ◽  
Lidar Data ◽  
Low Density ◽  
Airborne Lidar Data

<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m<sup><span lang="EN-US">–2</span></sup>) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>

Tropical Forest Canopy Structure and Change Assessment Using Landsat, GEDI, and Airborne Lidar Data

2021 ◽  
Author(s):  
Peter Potapov ◽  
Xinyuan Li ◽  
Andres Hernandez-Serna ◽  
Svetlana Turubanova ◽  
Alexandra Tyukavina ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Structure ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Change Assessment ◽  
Airborne Lidar Data ◽  
Tropical Forest Canopy

The Impact of Secularity on Attitudes of Gender Equality: A Multi-Level Analysis of 49 Nations

2016 ◽  
Vol 15 (4) ◽  
pp. 508-530 ◽  
Author(s):  
Christine A. Wernet
Keyword(s):  
Gender Equality ◽  
Explanatory Power ◽  
World Values Survey ◽  
Linear Regression Models ◽  
History Of ◽  
Micro Level ◽  
Multi Level ◽  
The Impact ◽  
The Relationship ◽  
Level Analysis

This research uses a series of hierarchical linear regression models fitted to data from the 2014 World Values Survey (wvs) and national statistics for 49 countries to specify the relationship between variables at the macro, meso, and micro level with attitudes of gender equality. In addition to the development of an updated and more robust Gender Equality Scale, the findings show that economic development increases support for gender equality, in line with Inglehart’s postmaterialist hypothesis. A history of communist rule and income inequality also increase attitudes of gender equality. Secularity has the greatest explanatory power in the equation; the results show that being educated, female, and less religious significantly increases one’s likelihood to support gender equality.

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