Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

2020 ◽  
Author(s):  
Da-Ju Wang ◽  
Hai-Yan Wei ◽  
Xu-Hui Zhang ◽  
Ya-Qin Fang ◽  
Wei Gu
Keyword(s):  
Remote Sensing ◽  
Habitat Suitability ◽  
Land Surface ◽  
Environmental Variables ◽  
Large Scale ◽  
Time Synchronization ◽  
Ambrosia Artemisiifolia ◽  
Real World Data ◽  
Habitat Suitability Modeling ◽  
Temperature And Precipitation

Abstract Aims Remote sensing (RS) is a technical method for effectively capturing real-world data on a large scale. We aimed to (i) realize the time synchronization of species and environmental variables, and extract variables related to the actual growth of species based on RS in habitat suitability modeling, and (ii) provide a reference for species management. Methods Taking invasive species Ambrosia artemisiifolia in China as an example for habitat suitability modeling. Temperature and precipitation variables were calculated from the land surface temperature (LST) provided by the moderate-resolution imaging spectroradiometer (MODIS), and climate station data, respectively. Besides, other variables that directly affect the growth or reproduction of A. artemisiifolia were also included, such as the relative humidity of the previous year's flowering period (RHPFP), and the effective UV irradiance reaching the Earth's surface (UVI). The Random Forest (RF) method was selected to model the habitat suitability. The environmental variables and samples were divided into four-time periods (i.e. 1990-2000, 2001-2005, 2006-2010, and 2011-2016) based on sampling time. Variables from the long-time series of RS (1990-2016) and WorldClim (1960-1990) were also modeled. Important Findings It was feasible to extract environmental variables from RS for habitat suitability modeling, and was more accurate than that based on the variables from WorldClim. The potential distribution of A. artemisiifolia in 1990-2000 and 2006-2010 was smaller than that in 2001-2005 and 2011-2016. The precipitation of driest months (bio14), precipitation coefficient of variation (bio15), RHPFP, and UVI were the important environmental variables that affect the growth and reproduction of A. artemisiifolia. The results indicated that the time synchronization of species and environmental variables improved the prediction accuracy of A. artemisiifolia, which should be considered in habitat suitability modeling (especially for annual species). This study can provide an important reference for the management and prevention of the spread of A. artemisiifolia.

Other Techniques Related to Habitat Analysis

2021 ◽  
pp. 193-208
Author(s):  
Joseph A. Veech
Keyword(s):  
Habitat Suitability ◽  
Environmental Variables ◽  
Resource Selection ◽  
Compositional Data ◽  
Initial Step ◽  
Compositional Data Analysis ◽  
Habitat Suitability Modeling ◽  
Species Response ◽  
Habitat Analysis ◽  
Active Choice

Habitat analysis is strictly defined as a statistical examination to identify the environmental variables that a species associates with, wherein association is taken as some form of correspondence between a species response variable (e.g., presence–absence or abundance) and the environmental variables. There are other statistical techniques and empirical goals that extend this basic framework. These techniques often rely on a habitat analysis having been conducted as an initial step. Resource selection functions quantify an individual’s and a species’ use of a resource based upon the properties of the resource. Resource is broadly defined and can include particular types of habitat. Selectivity and preference indices are used to assess an individual’s preference and active choice of different resource types. Compositional data analysis is a statistical method for examining the composition of an individual’s territory or home range with regard to different habitat types that may be represented. Habitat suitability modeling and species distribution modeling are closely related techniques designed to map the spatial distribution of a species’ habitat and sometimes the species itself based upon its habitat requirements and other factors.

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

scholarly journals Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1470 ◽  
Author(s):  
Nan Xia ◽  
Liang Cheng ◽  
ManChun Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Human Life ◽  
Human Movement ◽  
Night Time ◽  
Urban Mapping ◽  
The City

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

scholarly journals An observation-constrained assessment of the climate sensitivity and future trajectories of wetland methane emissions

2020 ◽  
Vol 6 (15) ◽  
pp. eaay4444 ◽  
Author(s):  
Ernest N. Koffi ◽  
Peter Bergamaschi ◽  
Romain Alkama ◽  
Alessandro Cescatti
Keyword(s):  
Global Warming ◽  
Land Surface ◽  
Large Scale ◽  
Methane Emissions ◽  
Climate Feedbacks ◽  
Global Estimate ◽  
Temperature And Precipitation ◽  
Surface Models ◽  
Natural Wetlands

Wetlands are a major source of methane (CH4) and contribute between 30 and 40% to the total CH4 emissions. Wetland CH4 emissions depend on temperature, water table depth, and both the quantity and quality of organic matter. Global warming will affect these three drivers of methanogenesis, raising questions about the feedbacks between natural methane production and climate change. Until present the large-scale response of wetland CH4 emissions to climate has been investigated with land-surface models that have produced contrasting results. Here, we produce a novel global estimate of wetland methane emissions based on atmospheric inverse modeling of CH4 fluxes and observed temperature and precipitation. Our data-driven model suggests that by 2100, current emissions may increase by 50% to 80%, which is within the range of 50% and 150% reported in previous studies. This finding highlights the importance of limiting global warming below 2°C to avoid substantial climate feedbacks driven by methane emissions from natural wetlands.

The calibration-free complementary relationship (CR) approach aids large-scale ET estimation

2020 ◽  
Author(s):  
Ning Ma ◽  
Jozsef Szilagyi ◽  
Yinsheng Zhang
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Eddy Covariance ◽  
Land Surface ◽  
Large Scale ◽  
Relative Bias ◽  
Dew Point ◽  
Surface Model ◽  
Complementary Relationship ◽  
Calibration Free

<p>Having recognized the limitations in spatial representativeness and/or temporal coverage of (i) current ground evapotranspiration (ET<sub>a</sub>) observations, and; (ii) land surface model (LSM) and remote sensing (RS) based ET<sub>a</sub> estimates due to uncertainties in soil and vegetation parameters, a calibration-free nonlinear complementary relationship (CR) model is employed with inputs of air and dew-point temperature, wind speed, and net radiation to estimate monthly ET<sub>a</sub> over conterminous United States during 1979–2015. Similar estimates of three land surface models (Noah, VIC, Mosaic), two reanalysis products (NCEP-II, ERA-Interim), two remote-sensing-based (GLEAM, PML) algorithms, and the spatially upscaled eddy-covariance ET<sub>a</sub> measurements of FLUXNET-MTE plus this new result from CR were validated against water-balance-derived results. We found that the CR outperforms all other methods in the multiyear mean annual HUC2-averaged ET<sub>a</sub> estimates with RMSE = 51 mm yr<sup>−1</sup>, R = 0.98, relative bias of −1 %, and NSE = 0.94, respectively. Inclusion of the GRACE data into the annual water balances for the considerably shorter 2003–2015 period does not have much effect on model performance. Besides, the CR outperforms all other models for the linear trends in annual ET rates over the HUC2 basins. Over the significantly smaller HUC6 basins where the water-balance validation is more uncertain, the CR still outperforms all other models except FLUXNET-MTE, which has the advantage of possible local ET<sub>a</sub> measurements, a benefit that clearly diminishes at the HUC2 scale.</p><p>   Because the employed CR method is calibration-free and requires only very few meteorological inputs, yet it yields superior ET performance at the regional scale, we further employed this method to develop a new 34-year (1982-2015) ET<sub>a</sub> product for China. The new Chinese ET<sub>a</sub> product was first validated against 13 eddy-covariance measurements in China, producing NSE values in the range of 0.72–0.95. On the basin scale, the modeled ET<sub>a</sub> values yielded a relative bias of 6%, and an NSE value of 0.80 in comparison with water-balance-derived evapotranspiration rates across ten major river basins in China, indicating the CR-simulated ET<sub>a</sub> rates reliable over China. Further evaluations suggest that the CR-based ET<sub>a</sub> product is more accurate than seven other mainstream ET<sub>a</sub> products. During last three decades, our new ET<sub>a</sub> product showed that annual ET<sub>a</sub> increased significantly over most parts of western and northeastern China, but decreased significantly in many regions of the North China Plain as well as in the eastern and southern coastal regions of China. This new CR-derived ET<sub>a</sub> product would benefit the community for long-term large-scale hydroclimatological studies.</p>

scholarly journals Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

2021 ◽  
Vol 13 (8) ◽  
pp. 1541
Author(s):  
Marco Piragnolo ◽  
Francesco Pirotti ◽  
Carlo Zanrosso ◽  
Emanuele Lingua ◽  
Stefano Grigolato
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Surface ◽  
Large Scale ◽  
Learning Algorithm ◽  
Web Gis ◽  
Forest Damage ◽  
Strong Negative Correlation ◽  
Ground Truth Data ◽  
Severity Prediction

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation.

scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Author(s):  
Shawn D Taylor ◽  
Dawn M Browning ◽  
Ruben A Baca ◽  
Feng Gao
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Scale Effects ◽  
Simulated Data ◽  
Area Index ◽  
Land Surface Phenology

Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

Intelligently Gapfilling Earth Observations: Towards a coherent observational view of Land Hydrology

2021 ◽  
Author(s):  
Verena Bessenbacher ◽  
Lukas Gudmundsson ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Large Scale ◽  
Missing Values ◽  
Extreme Values ◽  
Climate Change Impacts ◽  
Environmental Research ◽  
Earth System ◽  
Flexible Tool ◽  
Earth Observations

&lt;p&gt;Earth observations have many missing values. Their complex patterns of missingness can be a significant hurdle for studying Earth system dynamics and climate change impacts. To overcome this issue, missing values are regularly imputed, i.e. infilled, using techniques such as interpolation. However, the common practice to do this for each variable separately can negatively affect the covariance between different data products, resulting in biased estimates. Moreover, relying solely on interpolation for infilling missing values makes only inefficient use of information that may be available from other variables at the same location in space and time.&lt;/p&gt;&lt;p&gt;Here we propose a modular gap-filling algorithm that exploits the multivariate nature of Earth system observations and builds upon the notion that if a value is missing, it is likely that some other variables will be observed at the same location and time and their relationship can be learned. To this end, the algorithm expands upon simple interpolation by additionally applying a statistical imputation method that is designed to account for covariance across variables.&lt;/p&gt;&lt;p&gt;The algorithm is tested using gap-free reanalysis data of relevant variables to land surface processes: ground temperature, precipitation, terrestrial water storage and soil moisture. These variables were masked to match missingness patterns of remote sensing observations. Subsequently, the gap fill estimates can then be compared to the original reanalysis values to assess the merit of the gap fill.&lt;/p&gt;&lt;p&gt;Overall, estimates of the proposed algorithm have lower bias and higher correlation compared to simple interpolation. Furthermore, we demonstrate that the multivariate core of the algorithm improves the physical consistency across the considered variables. In case studies focussing on large-scale droughts, extreme values are correctly reconstructed even in cases of high fraction of missing values. The algorithm can thus be used as a flexible tool for gapfilling remote sensing and in-situ observations commonly used in climate and environmental research and create a coherent observational dataset of a flexible set of observational products.&lt;/p&gt;

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