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

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.

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

“…However, in our study, the relative influence of TNDVI was far greater than other vegetation and soil radiometric indices. Hence, our results would have benefited if it includes other bioclimatic variables (Ahmed et al 2020). However, acquiring these variables at high resolution was difficult in the study area.…”

Alien species revises systematic status: integrative species delimitation of two similar taxa of Symbrenthia Hübner, [1819] (Lepidoptera, Nymphalidae)

“…The species distribution model (SDM) has been widely used as a tool to detect the potential invasive area of invasive species ( Wiens et al, 2010 ; Ahmed, Atzberger & Zewdie, 2020 ). Based on the niche conservatism of the invasive species, we are usually able to predict the invasive area based on areas in the invaded region with similar environment to the source area from which the species originated.…”

Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994–2022)

“…The development of robust and advanced non-parametric image classification algorithms represents a significant advancement in the field of mapping invasive species [31]. As satellite sensor technology continues to evolve, it is essential to explore the utilization of these advanced classifiers in conjunction with data from the latest generation of multispectral sensors, which offer improved spatial and spectral resolutions [42]. This approach is crucial for overcoming the challenges associated with invasive species classification using remote sensing.…”

“…This approach is crucial for overcoming the challenges associated with invasive species classification using remote sensing. One of the many difficulties is the spectral similarity between invasive and native species, making it hard to differentiate them accurately [42]. Additionally, the heterogeneous nature of the landscape and the varying growth stages of different species further complicate the classification process [31].…”