scholarly journals Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nurhussen Ahmed ◽  
Clement Atzberger ◽  
Worku Zewdie
Keyword(s):  
Remote Sensing ◽  
Invasive Species ◽  
Temporal Resolution ◽  
Species Distribution ◽  
Future Research ◽  
Remotely Sensed Data ◽  
Selection Of Variables ◽  
Bioclimatic Variables ◽  
Challenges And Opportunities ◽  
Selection Of

Abstract Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.

Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling

2014 ◽  
Vol 5 (10) ◽  
pp. 1033-1042 ◽  
Author(s):  
Eric Waltari ◽  
Ronny Schroeder ◽  
Kyle McDonald ◽  
Robert P. Anderson ◽  
Ana Carnaval
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling ◽  
Bioclimatic Variables

scholarly journals A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1014
Author(s):  
Alba Estrada ◽  
Raimundo Real
Keyword(s):  
Species Distribution ◽  
High Efficiency ◽  
Model Misspecification ◽  
Species Distribution Modelling ◽  
Biological Species ◽  
Fuzzy Entropy ◽  
Distribution Modelling ◽  
Logistic Regression Models ◽  
Selection Of Variables ◽  
Selection Of

Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency.

scholarly journals Potential of remote sensing to predict species invasions

2015 ◽  
Vol 39 (3) ◽  
pp. 283-309 ◽  
Author(s):  
Duccio Rocchini ◽  
Veronica Andreo ◽  
Michael Förster ◽  
Carol Ximena Garzon-Lopez ◽  
Andrew Paul Gutierrez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Invasive Species ◽  
Biological Invasion ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Predictive Modelling ◽  
Distribution Models ◽  
Species Invasions ◽  
Remote Sensing Techniques ◽  
The Impact

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

scholarly journals Development of an Agricultural Soil Information System

2003 ◽  
pp. 60-63
Author(s):  
Dénes Dorka
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Problem Solving ◽  
Temporal Resolution ◽  
Research Program ◽  
Agricultural Soil ◽  
Target Area ◽  
Remotely Sensed Data ◽  
Recent Advances ◽  
Soil Information

Since the development of remote sensing nearly 60 years ago, there have been many applications for agriculture. Some have proved effective, while others have not succeeded in assisting farmers with problem solving. Recent advances in the spatial, spectral and temporal resolution of remote sensing as well as potential positive changes in cost and availability of remotely sensed data may make it a profitable tool for more farmers. The target area of my research program is the fields cultivated by Kasz-Coop Ltd. considering that this firm is one of the main agricultural firms in the region and its cultivated fields are quite heterogeneous.

scholarly journals Performance Analysis of Different Classifier for Remote Sensing Application

2019 ◽  
Vol 9 (1) ◽  
pp. 7153-7158
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Remotely Sensed ◽  
Processing Technique ◽  
Good Choice ◽  
Support Vector ◽  
Remotely Sensed Data ◽  
Classification Technique ◽  
Selection Of

The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of today’s remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data.

scholarly journals An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2443 ◽  
Author(s):  
Wenjie Liu ◽  
Yongnian Zeng ◽  
Songnian Li ◽  
Xinyu Pi ◽  
Wei Huang
Keyword(s):  
Temporal Resolution ◽  
Correlation Coefficients ◽  
Fusion Model ◽  
Remotely Sensed Data ◽  
Biophysical Parameters ◽  
Sensor Performance ◽  
Adaptive Fusion ◽  
Spectral Mixture ◽  
Spectral Mixing ◽  
Selection Of

High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is difficult to obtain remote sensing images with both high spatial and temporal resolution. The spatiotemporal fusion model is a crucial method to solve this problem. The spatial and temporal adaptive reflectivity fusion model (STARFM) and its improved models are the most widely used spatiotemporal adaptive fusion models. However, the existing spatiotemporal adaptive reflectivity fusion model and its improved models have great uncertainty in selecting neighboring similar pixels, especially in spatially heterogeneous areas. Therefore, it is difficult to effectively search and determine neighboring spectrally similar pixels in STARFM-like models, resulting in a decrease of imagery fusion accuracy. In this research, we modify the procedure of neighboring similar pixel selection of ESTARFM method and propose an improved ESTARFM method (I-ESTARFM). Based on the land cover endmember types and its fraction values obtained by spectral mixing analysis, the neighboring similar pixels can be effectively selected. The experimental results indicate that the I-ESTARFM method selects neighboring spectrally similar pixels more accurately than STARFM and ESTARFM models. Compared with the STARFM and ESTARFM, the correlation coefficients of the image fused by the I-ESTARFM with that of the actual image are increased and the mean square error is decreased, especially in spatially heterogeneous areas. The uncertainty of spectral similar neighborhood pixel selection is reduced and the precision of spatial-temporal fusion is improved.

scholarly journals A Remote Sensing and GIS-Based Model of Habitat as a Predictor of Biodiversity

1994 ◽  
Vol 18 ◽  
pp. 3-16
Author(s):  
Diane Debinski ◽  
Kelly Kindscher
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Species Distribution ◽  
Animal Species ◽  
Habitat Type ◽  
Distribution Patterns ◽  
Remotely Sensed ◽  
Forest Types ◽  
Remotely Sensed Data ◽  
Habitat Specificity

Conservation biologists need better methods for predicting species diversity. This research investigated some new methods to analyze biodiversity patterns through the use of Geographic Information Systems and remote sensing technologies. We tested the correlation between remotely sensed habitat types and species distributions. The goal was not to do away with ground-based fieldwork, but rather to optimize and focus fieldwork by using GIS and remotely sensed data as tools for making the work more accurate and specific. Our research was conducted at a fine (30 x30 m) landscape scale using on-the ground locations of birds, butterflies, and plants in the northwest portion of the Greater Yellowstone Ecosystem. Three remotely sensed forest types (distinguished by species density and coverage) and six remotely sensed meadow types (ranging from xeric to hydric) were surveyed and coverage data were collected for grasses, shrubs, forbs and trees. Presence/absence data were collected for birds and butterflies. The objectives of this research were: 1) to determine the extent of the correlation between spectral reflectance patterns and plant or animal species distribution patterns, and 2) to test the spatial correspondence of species diversity "hotspots" among taxonomic groups. Field surveys in 1993 and 1994 validated the vegetation density, cover, and moisture gradients expected from satellite data interpretation. Both tree species composition and diameter at breast height were significant in discriminating among forest types. Twenty-two species of grasses and forbs were significant in distinguishing among meadow types. However, a smaller percentage of the animal species was significantly correlated with one habitat type. In order to find a strong correlation between species distribution patterns and remotely sensed data, a species must be moderately common and show some habitat specificity. Hotspots of species diversity coincided for shrubs, grasses, forbs, birds, and butterflies and were found in mesic meadows.

Challenges and Opportunities for Neuroimaging in Young Patients with Traumatic Brain Injury: a Coordinated Effort towards Advancing Discovery from the ENIGMA Pediatric Moderate/Severe TBI Group

2019 ◽  
Author(s):  
Emily L. Dennis ◽  
Karen Caeyenberghs ◽  
Robert F. Asarnow ◽  
Talin Babikian ◽  
Brenda Bartnik-Olson ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Meta Analysis ◽  
Young Patients ◽  
Future Research ◽  
Childhood And Adolescence ◽  
Developmental Issues ◽  
Challenges And Opportunities ◽  
Severe Tbi ◽  
Advance Research

Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population; however, research in this population lags behind research in adults. This may be due, in part, to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. Specific developmental issues also warrant attention in studies of children, and the ever-changing context of childhood and adolescence may require larger sample sizes than are commonly available to adequately address remaining questions related to TBI. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate-Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis. In this paper we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. We conclude with recommendations for future research in this field of study.

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