Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods

Hassan, Quazi K.; Bourque, Charles P.‐A.

doi:10.3390/rs1030393

Cited by 20 publications

(17 citation statements)

References 32 publications

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“…The area covers 24,240 km 2 , its boundaries being: north latitude: 46:45:00N, south latitude: 45:36:50N, west longitude: 10:06:33E, and east longitude: 12:46:30E. It is divided by several dominant wide river valleys, for example the territory of the Autonomous Provinces of Trento and Bolzano is bisected from north to south by the Adige river valley, following the south-eastern drainage pattern in this area.…”

Section: Study Regionmentioning

confidence: 99%

“…The application of traditional geospatial interpolation methods in complex terrain remains challenging [1] and difficult to optimize. Remote sensing is an alternative data source [2] since remotely sensed imagery is to air temperatures, which are measured at 2 m above ground, it can be assumed that the general temperature profile/patterns are very similar [21].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

2010

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Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained.

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Section: Study Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

2010

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“…More recently, there is an increasing trend in using remote sensing information based, e.g., on various spectral indices recorded from airborne or space-borne sensors as predictor variables in species distribution modeling [22][23][24][25][26]. A few studies have shown that niche models developed by incorporating remotely-sensed predictors are more robust; in particular, these data can improve the prediction accuracy and tend to refine mapped distribution of species and habitats, compared with climatic/topographical variables-only models [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

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Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

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“…임목생장에 영향을 미치는 온도의 효과를 생장도일 의 포물선 함수에 의해 지수화한 것이 온도효과지수이 다 (Botkin et al, 1972;Lim, 1998;Hassan and Bourque, 2009 , 수문지표와 온열지표 (Choi, 2010), 그리고 기온감율을 적용한 공간보간기법 (Lee et al, 2011) (Sykes and Prentice, 1996;Bourque and Hassan, 2008;Hassan and Bourque, 2009;Bourque et al, 2010). (Lim, 1998;Choi et al, 2010;Lee et al, 2011;National Institute of Forest Science, 2015).…”

Section: 서 론unclassified

Effect of Yearly Changes in Growing Degree Days on the Potential Distribution and Growth of Quercus mongolica in Korea

Lim¹,

Park²,

Shin³

2016

Korean Journal of Agricultural and Forest Meteorology

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This study was conducted to analyze the effect of yearly changes in growing degree days (GDD) on the potential distribution and growth of Quercus mongolica in Korea. Annual tree-ring growth data of Quercus mongolica collected by the 5 th National Forest Inventory were first organized to identify the range of current distribution for the species. Yearly GDD was calculated based on daily mean temperature data from 1951 to 2010 for counties with current distribution of Q. monglica. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, seven clusters were identified. Yearly GDD based on daily mean temperature data of each county were calculated for each of the cluster to predict the change of potential distribution. Temperature effect indices were estimated to predict the effect of GDD on the growth patterns. In addition, RCP 4.5 and RCP 8.5 of climate change scenarios were adopted to estimate yearly GDD and temperature effect indices from 2011 to 2100. The results indicate that the areas with low latitude and elevation exceed the upper threshold of GDD for the species due to the increase of mean temperature with climate change. It was also predicted that the steep increase of temperature will have negative influences on tree-ring growth, and will move the potential distribution of the species to areas with higher latitude or higher elevation, especially after the year of 2050. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics and for predicting changes in the potential distribution of Q. mongolica caused by climate change.

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Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods

Cited by 20 publications

References 32 publications

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Effect of Yearly Changes in Growing Degree Days on the Potential Distribution and Growth of Quercus mongolica in Korea

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