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Universidade Federal de Santa Maria
REGET, Santa Maria, v 29, e84309, 2025
DOI: 10.5902/2236117084309
ISSN 2236-1170
Submitted: 02/07/2023 • Approved: 08/05/2025• Published: 06/06/2025
Environmental Technology
Applicability of remote sensing technologies in the detection and mapping of prosopis spp species: a systematic review
Aplicabilidade de tecnologias de sensoriamento remoto na detecção e mapeamento de espécies Prosopis spp: uma revisão sistemática
Charline ZangalliI
Eduardo Luiz Costa Tobias PintoI
Fabrício William de ÁvilaI
Fernando da Silva AlexandreII
Hannah Cristina Botelho Lima de FanolaI
Kátia Cylene LombardiI
Magna Soelma Beserra de Moura III
Maria Luíza Coelho CavalcantiII
Patrício Rinaldo dos SantosI
Renilson Pinto da Silva RamosIV
Sérgio Elias LibomboII
I Central-West State University ROR, Guarapuava, PR, Brazil
II Federal University of Pernambuco ROR , Recife, PE, Brazil
III Federal University of Campina Grande ROR , Campina Grande, PB, Brail
IV Federal University of Ceara ROR , Fortaleza, CE, Brazil
ABSTRACT
Remote sensing plays a crucial role in the detection of invasive alien species. This study aimed to explore and present the existing literature on the use of remote sensing technologies for the detection of invasive Prosopis spp canopies in semiarid environments over a long period. A total of fifty studies spanning twelve years were analyzed using the Google Scholar and Science Direct databases, with the help of the ResearchGate social network of researchers and Excel software for data visualization, including word clouds and other graphical representations. The research carried out between 2017 and 2021 witnessed a significant increase in the application of geotechnologies such as Sentinel, LiDAR and hyperspectral images. Vegetation indices, including the Normalized Difference Vegetation Index (NDVI), were widely used. Africa emerged as the continent with the highest level of concern regarding the species discussed, which is evident from the publication of 28 studies. The data revealed that Prosopis juliflora showed greater invasive potential in America and Asia. However, African countries, including Kenya, Ethiopia, Somalia and Sudan, faced a high risk of invasion. The terms found most frequently in the analysis were Prosopis and juliflora. It is essential to prioritize research focused on the application of remote sensing geotechnologies, especially in semiarid regions, to enable informed decision-making by government environmental agencies in relation to the management and control of these invasive species.
Keywords: Geotechnologies; Remote sensing; Invasive species; Mesquite
RESUMO
O sensoriamento remoto é bastante útil na identificação de espécies exóticas invasoras. O objetivo deste estudo foi investigar e relatar, por meio da literatura, o uso de tecnologias de sensoriamento remoto como instrumento na detecção de dosséis invasivos de espécies Prosopis spp em ambientes semiáridos ao longo do tempo. Foram utilizados 50 estudos no período espaço-temporal de 12 anos nas bases de dados Google Acadêmico e Science Direct, com auxílio da rede social de pesquisadores Researchgate, aliado ao uso do Software Excell, para confecção de gráficos, nuvem de palavras e outros arranjos. Foi constatado o aumento das pesquisas entre os anos 2017 e 2021, envolvendo a temática com uso de geotecnologias como o Sentinel, LiDAR e imagens hiperespectrais, Índices de Vegetação, como o Índice de Vegetação da Diferença Normalizada (NDVI), dentre outros. A África apresenta a comunidade mais aflita com as questões referentes às espécies do gênero Prosopis contando 28 publicações. Os dados apontam que Prosopis spp., se faz invasiva em maiores proporções na América e na Ásia. Porém, países africanos como Quênia, Etiópia, Somália e Sudão estão sob o alto risco de invasão. Os termos mais frequentes resultantes das análises foram Prosopis e juliflora. A aplicação de geotecnologias de sensoriamento remoto é imprescindível frente à invasão de Prosopis para tomada de decisões habéis em prol do manejo e controle pelos orgãos ambientais dos governos em regiões semiáridas do mundo.
Palavras-chave: Geotecnologias; Sensoriamento remoto; Espécies invasoras; Algaroba
Early detection and mapping of invasive species is crucial for formulating effective management strategies and preventing land invasions (Mbaabu et al., 2019). While field survey methods have been used to assess and monitor the plant invasions, remote sensing techniques provide a better estimation and mapping of the invasive species, especially over large spatial extents. Identifying the different invasive species presents a unique challenge as these species’ characteristics may be similar to those of native plants. However, the various satellite missions and sensors’ ability to acquire data in the spatial, spectral, and temporal resolutions enables detecting and assessing invasive species better (Huang; Asner 2009; Al-Wardy et al., 2021). In addition to large-scale analysis, remote sensing has the applicability of monitoring in space.
Among these species, taxa of the genus Prosopis (mesquites; Fabaceae) are commonly known as algaroba and are native to Africa, Asia, North America and South America (Aung; Koike, 2015). They comprise a total of 44 known species, distributed in arid and semiarid regions around the world, divided into five sections (Burkart, 1976). Since then, 13 additional species have been described (Schinini 1981; Earl and Lux 1991; Palacios 2006; Vásquez Núñez et al., 2009; De Mera et al., 2019; Hughes et al., 2022). Most Prosopis species are, however, native to the Americas (López et al., 2006).
Remote sensing offers a wider range of sensor systems, such as aerial photographs, multispectral scanners, low- and high-resolution satellite images and ground-based spectrometer measurements (Joshi; Leeuw; Duren, 2003; Amboka; Ngigi, 2015), for monitoring Prosopis. As anthropogenic activities evolve, facilitating the spread of Prosopis species in arid and semiarid environments, monitoring with sophisticated technologies such as remote sensing becomes indispensable in the face of changes in land use and land cover.
It is clear that there are few published review studies involving the applicability of remote sensing geotechnologies in the detection of invasive Prosopis canopies. It is important to note that there are literature review studies focusing on Prosopis juliflora pods as fodder resources for livestock (Sawal; Athan; Yadav, 2004), Woody growth of Prosopis L. in relation to hydrology in South America (Ambite et al., 2022), bioethanol production from algaroba (Sindhu; Gnanavel, 2016), pharmacological potentials of Prosopis juliflora (Ukande et al., 2019), distribution, impacts and control methods (Abdulahi; Ute; Regasa, 2017), among others. However, there is a scarcity of review studies focusing on the use of remote sensing geotechnologies in the Prosopis spp. invasion.
In this context, the guiding question of this research is: What research papers have been published in the last 12 years (2011-2022) on the applicability of remote sensing technologies in relation to the invasion of Prosopis species? This review aims to present relevant aspects of the importance of using these technologies and how this topic has been discussed in the literature.
Therefore, the aim of this study was to investigate and report, through the literature, the use of remote sensing technologies as a tool to detect and map invasive Prosopis spp canopies in semiarid environments over time.
This study is a systematic investigation (Tranfield; Denyer; Smart, 2003). Predefined systematic methods were employed to identify all relevant published and unpublished documents for the research question, and the quality of these articles was assessed, data extracted and results synthesized (Donato; Donato, 2019). This type of study provides professionals and policymakers with a reliable basis for decision-making and action (Tranfield; Denyer; Smart, 2003).
The literature search and coding were carried out between December 2021 and July 2022, with careful inspection during the first half of 2023. The search was carried out in the following databases: Google Scholar and Science Direct. In addition, the ResearchGate research network was used for data validation. Publications from a 12-year period (2011-2022) were considered.
The period selected (2011-2022) is justified by the fact that the risk of invasion by exotic Prosopis spp trees increases with temperature, soil alkalinity and clay fractions (Dakhil et al., 2021), as well as with the global increase in biological invasions in recent years. In addition, the invasion of Prosopis species is linked to the principles of protection, restoration and sustainable use of ecosystem services, inherent in the 15th Sustainable Development Goal (Un, 2015; Silva et al., 2022; Oliveira et al., 2020; Choudhary; Ramkumar, 2020).
Therefore, the process of carrying out this study followed the three stages proposed by Tranfield, Denyer and Smart (2003), namely: I. Planning the review; II. Carrying out the research; and III. Reporting and dissemination, as illustrated in the flowchart (Figure 1).
The articles selected included original research, technical reports and review studies focusing on the use of remote sensing to map Prosopis species. The articles were selected based on the inclusion or exclusion criteria of their titles and abstracts, and were read in full when the abstracts were not informative. Priority was given to publications that dealt exclusively with remote sensing in the detection of Prosopis species.
It is known that recently, based on genomic analyses that studied 997 nuclear genes, the genus Prosopis was reorganized and divided into five new genera: Anonychium, Indopiptadenia, Neltuma, Strombocarpa and Xerocladia (Hughes et al., 2022). After this split, only three species remained under the original classification of Prosopis (in the strict sense). In addition, the species Prosopis juliflora was reclassified and now belongs to the genus Neltuma, now called Neltuma juliflora (Hughes et al., 2022). In other words, for this systematic review study, we tried to include several species from the genus Prosopis spp. before the aforementioned segregation, covering the species that belonged to the genus before being redistributed into other genera, such as Neltuma and Strombocarpa, e.g.
Quantitative analysis of the sample was carried out using Excel, applying descriptive statistics. After processing the data, it was necessary to construct graphs and tables to present the results (Silva et al., 2022; Barreto, 2022). For the qualitative analysis, we used content analysis (Bardin, 2016) and Atlas.ti version 9 software to create word clouds to identify the most frequent terms in the publications. According to Silva Junior and Leão (2018), the software offers several features, including the ability to build state-of-the-art reports, perform multimedia analysis of images, audios and videos, perform statistical data processing, analyze research and code databases.
The frequency of words was determined using the word cloud generated from the textual analysis of a group of texts, corresponding to the titles. Lexical analysis was fundamental to understanding the word cloud (Ramos; Lima; Rosa, 2018; Silva et al., 2022).
Figure 1 – Methodological design used.
Source: The authors (2023)
The articles selected included original research, technical reports and review studies focusing on the use of remote sensing to map Prosopis species. The articles were selected based on the inclusion or exclusion criteria of their titles and abstracts, and were read in full when the abstracts were not informative. Priority was given to publications that dealt exclusively with remote sensing in the detection of Prosopis species.
It is known that recently, based on genomic analyses that studied 997 nuclear genes, the genus Prosopis was reorganized and divided into five new genera: Anonychium, Indopiptadenia, Neltuma, Strombocarpa and Xerocladia (Hughes et al., 2022). After this split, only three species remained under the original classification of Prosopis (in the strict sense). In addition, the species Prosopis juliflora was reclassified and now belongs to the genus Neltuma, now called Neltuma juliflora (Hughes et al., 2022). In other words, for this systematic review study, we tried to include several species from the genus Prosopis spp. before the aforementioned segregation, covering the species that belonged to the genus before being redistributed into other genera, such as Neltuma and Strombocarpa, e.g.
Quantitative analysis of the sample was carried out using Excel, applying descriptive statistics. After processing the data, it was necessary to construct graphs and tables to present the results (Silva et al., 2022; Barreto, 2022). For the qualitative analysis, we used content analysis (Bardin, 2016) and Atlas.ti version 9 software to create word clouds to identify the most frequent terms in the publications. According to Silva Junior and Leão (2018), the software offers several features, including the ability to build state-of-the-art reports, perform multimedia analysis of images, audios and videos, perform statistical data processing, analyze research and code databases.
The frequency of words was determined using the word cloud generated from the textual analysis of a group of texts, corresponding to the titles. Lexical analysis was fundamental to understanding the word cloud (Ramos; Lima; Rosa, 2018; Silva et al., 2022).
3.1 Quantification and classification of publications
The initial search identified 43 publications in Science Direct and 62 publications in Google Scholar, resulting in a total of 105 publications related to the applicability of geotechnologies in the discovery and mapping of invasive species in general. After excluding studies that did not fit the proposed time period or the specific topic of interest, only 50 studies remained.
This research comprised a final sample of 50 articles, a quantitatively significant number considering the spatial and temporal scope of 12 years and, above all, among the countries of the three continents invaded by the species mentioned. Through the analyses carried out during this period, it was observed that the number of scientific publications on the use of remote sensing algorithms in the invasion of Prosopis spp species increased significantly over the period studied, indicating a growing interest in this topic from the scientific community (Figure 2).
Figure 2 – Scientific publications on remote sensing applied to invasive species Prosopis spp.
Source: The authors (2023)
Likewise, in addition to progress, the current state and future opportunities of remote sensing for the research and management of plant invasions were considered. Since the 1970s, remote sensing has been used (Vaz et al., 2018) for trees such as Prosopis spp, which have high invasive potential in semiarid environments. Heshmati et al. (2019), e.g., using the environmental suitability algorithms species distribution modeling (SDM) and maximum entropy method (MaxEnt), modeled how future climate conditions may influence the advance and retreat of P. juliflora areas around the world, and verified stability in areas occupied by this species in northeastern Brazil. This is understandable because, despite the positive impacts provided by Prosopis, the species tends to form dense and impenetrable forests with unfavorable consequences (Pasiecznik et al., 2001; Adam; Mureriwa; Newete, 2017).
In the Brazilian semiarid region, more specifically in the Riacho do Navio River Basin (BHRN), located in the state of Pernambuco, Carvalho, Lopes and Silva (2019) worked with analysis and classification of historical rainfall series of more than 30 years combined with mapping of land use and occupation using satellite images, the TM/Landsat 8 sensor with 6R5G4B color composition for supervised classification, found a 4% higher growth for the riparian forest class, caused by the increase in algaroba (Prosopis juliflora) (Sw. ) DC (Carvalho; Lopes; Silva, 2019), similar to Coelho et al. (2014), in a study carried out in the municipality of Serra Talhada, Pernambuco and Santos et al. (2019) on the banks of the Apodi-Mossoró River, Rio Grande do Norte State. The P. juliflora species currently occupies large areas in all the states of Northeastern Brazil, with estimates of over 500,000 hectares (Lima, 2005; Carneiro-Junior et al., 2021).
The above procedure also makes it possible to differentiate Caatinga vegetation from some introduced flora communities, such as P. juliflora, which does not lose its leaves during the dry season because it is managed in areas close to bodies of water, such as rivers, which, even if their beds are dry, have an accumulation of groundwater (Silva; Cruz, 2018). In this segment, Cavalcanti (2014), later cited by Souza and Cruz (2018), reveals that this class would be defined as Hygrophilous Formation, and of all the classifications presented, this is the only one in which there is a class for introduced or cosmopolitan vegetation.
These authors also warn that the above example is not sufficient to account for all the complexity of the Caatinga, but is only an approximation of the use of the environmental dynamics inherent in the biome, in support of the classification of orbital remote sensing images (Silva; Cruz, 2018). Northeastern Brazil is subject to phytogeographic changes, which are enhanced by the presence of species that lack effective control or management. P. juliflora found a favorable environment for its development in the Caatinga (Nogueira Júnior et al., 2019). In this sense, these facts demonstrate how valuable the use of sensing technologies is for the purposes of detecting and mapping Prosopis in the Brazilian semiarid, taking into account the risks of future invasions in Caatinga, pasture, irrigated and dryland and degraded areas, respectively.
The concern is alarming, as there was an increase in the number of publications using remote sensing with Prosopis spp trees during the period from 2011 to 2016, especially between 2015 and 2016. Throughout the study period, the number of scientific papers aimed at mapping these highlighted species exceeded expected expectations, indicating a growing formation of scientific groups focused on this subject. In parallel, authors such as Pedreira, Lobão and Vasconcelos (2022), working with scientific productions, through bibliometrics of studies carried out with P. juliflora, also found a considerable increase in publications after the 2000s, mainly with a period of greater productivity occurring between 2010 and 2017, with a peak of sixteen productions in 2016.
This increase is noticeable in view of the emergence of various geoprocessing software such as ARQGIS and spectral indices, geospatial data visualizers such as Google Maps TM, EEFLux (Earth Engine Evapotranspiration Flux), LANDSAT satellites, Sentinel, UAVs (Unmanned Aerial Vehicles), and others, some of which are capable of demonstrating better accuracy of the spectral reflectance of the attributes of objects on the ground such as extracts of Prosopis tree canopies. According to Huylenbroeck et al. (2020), LiDAR seems to be the most commonly used technology to describe vegetation structure characteristics, with the exception of Large Woody Debris, landscape metrics and vegetation cover. However, LiDAR from unmanned aerial vehicles, known as UAVs or Unmanned Aerial Vehicles (UAVs), has proven to be an emerging technology, as it has robust attributes based on scans above and below the canopy (Liang et al., 2019; Nishiwaki et al., 2023), as did Ku and Popescu (2018) in Texas, USA, comparing three statistical methods and identifying significant predictor variables.
In addition, the use of the high-resolution GeoEye satellite to obtain hyperspectral images, employing two techniques - Derived Vegetation Index and Spectral Angle Mapper in Pakistan (Kazmi et al., 2021), vegetation indices such as the Normalized Difference Vegetation Index (NDVI) in the United Arab Emirates (UAE) (Howari et al., 2022) and other techniques have been employed to detect the aforementioned invasive.
Remote sensing plays a crucial role in mapping and monitoring invasive species. Its ability to provide multispectral and multitemporal coverage in a cost-effective manner has made it the number one choice for mapping changes in vegetation cover over a larger geographical area in reasonable timeframes (Stoms; Estes, 1993; Amboka; Ngigi, 2015).
In the first few years (2011 to 2014), the number of publications was limited, with only 10 articles. When evaluating the cumulative number of publications, a pattern is observed between 2018 and 2020, showing a significant increase compared to all other years. However, there is a notable expansion of research during the years 2017 and 2021.
3.2 Spatial classification of survey
It was found that, in the research carried out on the three continents where the tree invades, the five countries with the highest percentage of publications, including Brazil, account for more than 50% of the scientific production on the subject in question. These results are also due to the usefulness and lack of adoption of control methods by each of these countries.
The species P. juliflora, e.g. introduced in 1942 in Serra Talhada, Pernambuco, northeast Brazil, is native to the Piura region of Peru (Gomes, 1961; Lima, 1999; Alves et al., 2019). At that time, P. juliflora was introduced to feed cattle and reforest deforested areas (Santos; Diodato, 2015), without a thorough knowledge of the species, which ended up intensifying the levels of biological invasion in Caatinga areas, as evidenced by authors such as (Pegado; Andrade; Pereira, 2006; Andrade; Fabricante; Oliveira; 2009, 2010).
Therefore, in Brazil, it is clear that these facts have contributed significantly to the use of remote sensing technologies (Pereira et al., 2013; Santos; Diodato, 2015; Santos et al., 2019; Amador et al., 2011), whose purpose has been the detection, mapping and, subsequently, the management and control of Prosopis spp. These and other developments justify the amount of research revealed in this review study.
In terms of continents, Africa has the community most concerned with the issues of invasive species of the genus Prosopis spp. due to the various works carried out in these semiarid environments. Despite being considered poor countries with limited scientific knowledge, Ethiopia, e.g., has made significant investments in studies focused on the use of remote sensing to detect Prosopis spp, similar to India in Asia. According to Kazmi et al. (2022), invasive plant species have dominated urban habitats over the last century, especially in the megacities of South Asia. Most of these species are introduced as a result of planning and design, often for a special purpose (e.g. sand dune stabilization, erosion control, flood control, rapid green roofs, etc.).
This is due to the fact that large areas of semiarid regions in East Africa, e.g., are reportedly invaded by P. juliflora, with rapid expansion, especially in communal grazing areas (Dubow, 2011; Rembold et al., 2015). In addition, agricultural land affected by salts and left fallow is covered by P. juliflora, an exotic evergreen and thorny tree species in India (Bhagat; More, 2013). Consequently, it can be seen that the main research efforts are concentrated in Africa, with a total of 28 publications, mainly carried out in Ethiopia, Kenya and South Africa, respectively.
In order to draw up this scheme, journal articles were mainly taken into account. Priority was given to the countries with the highest number of published articles, namely: Ethiopia, India, Kenya, South Africa and Brazil. These studies partially or totally used remote sensing techniques to detect the Prosopis species considered in this research.
Table 1 shows significant variations in the types of satellites used to image the areas of the 25 studies listed. The most widely used satellite based on research evidence was Landsat, due to its high spatial resolution and radiometric accuracy.
Table 1 – Classification of scientific groups by geographic region and type of satellite used
|
Title of Publications |
Author and Year |
Satellite/sensor |
|
Performance of Species Distribution Modeling and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in the lower Awash River basin, Ethiopia |
Ahmed et al. (2022) |
MODIS |
|
Expansion of Prosopis juliflora and land use/land cover change in the Korahey zone of the regional state of Somalia, eastern Ethiopia |
Wudad and Abdulahi, (2021) |
Landsat |
|
Regional dynamics in the distribution of Prosopis juliflora under projected climate change in Africa |
Sintayehu et al. (2020) |
Species Distribution Modeling (SDM) |
|
Effects of Prosopis juliflora invasions on land use/land cover change in the South Afar region, Northeast Ethiopia |
Shiferaw et al. (2019) |
Google Earth Pro v. 7.1.5 |
|
Assessing the distribution and impacts of Prosopis juliflora through participatory approaches |
Wakie et al. (2016) |
Landsat |
|
Spatial mapping of Prosopis juliflora (Swartz) DC in Pudukkottai District, Tamil Nadu, India |
Hiregoudar et al. (2020) |
Resourcesat – 2 Linear Imagens Self Scanning LISS (IV) |
|
Mapping of invasive Prosopis juliflora in arid regions using high-resolution remote sensing data and biophysical parameters |
Vidhya et al. (2017) |
Landsat |
|
An Autonomous UAV-UGV System for Eradication of the Invasive Weed Prosopis juliflora |
Baskaran et al. (2017) |
Landsat, Resourcesat-1/IRS-P6 |
|
Use of LANDSAT ETM+ data for Prosopis juliflora detection in irrigated areas |
Bhagat and More (2013) |
Landsat |
|
Satellite image-based quantification of Prosopis juliflora invasion and patch dynamics in the Great Rann of Kachchh, Kachchh Biosphere Reserve, Gujarat, India |
Pasha et al. (2014) |
Resourcesat – 2 Linear, Imagens LISS (IV) |
|
Quantification of Prosopis juliflora invasive colonies using remote sensing and GIS techniques |
Ragavan and Johnny (2015) |
Landsat |
|
Utilizing Geo-information Tools for Mapping Spatio-temporal Changes in Population of Prosopis cineraria (khejri) in Agroforestry System of Arid Western Rajasthan |
Moharana et al. (2018) |
Google Earth e satélite IRS-LISS-III |
|
Mapping and Monitoring Spatial-Temporal Land Cover Change of Prosopis Species Colonization in Baringo Central, Kenya |
Kumar and Mathur (2014) |
Landsat |
|
Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland |
Ng et al. (2016) |
Landsat |
|
Assessing the Potential of Sentinel-2 and Pleiades Data for Prosopis and Vachellia spp. Detection in Kenya |
Ng et al. (2017) |
Sentinel-2 |
|
Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products: a case study of Prieska in the Northern Cape Province, South Africa |
Villiers et al. (2020) |
Landsat |
|
Mapping Prosopis glandulosa (mesquite) in the semi-arid environment of South Africa using high-resolution WorldView-2 imagery and machine learning classifiers |
Adam et al. (2017) |
WorldView-2 |
|
Mesquite Risk Mapping and Assessment in Tokar Delta-Eastern Sudan |
Suliman et al. (2015) |
Landsat |
|
Examining the spectral separability of Prosopis glandulosa from coexisting species using field spectral measurement and regularized random forest |
Mureriwa et al. (2016) |
Spectroscopy |
|
Spatio-temporal analysis of vegetation cover and Prosopis juliflora (SW) DC advancement in a Caatinga area |
Pereira et al. (2013) |
Landsat and CBERS |
|
Distribution and analysis of Prosopis tree dispersion processes in protected urban areas of the municipality of Mossoró/RN |
Santos et al. (2019) |
Shapefile layers with and Google Maps images |
|
Wide-area invasive species propagation mapping is possible using phenometric trends |
Landmann et al. (2020) |
MODIS |
|
Analysis of Prosopis juliflora (SW) invasion in the Caatinga, municipality of Fernando Pedroza, Rio Grande do Norte |
Santos and Diodato (2015) |
Landsat |
Source: The authors (2023)
In a study conducted by Al-Wardy et al. (2021) in the Salalah Plains, located in the southern part of Oman, using three different satellites: Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI), the classification accuracy of P. julifora was very high (over 90%). Ng et al. (2016) in Hargeisa, Somaliland, were able to classify five different classes of Prosopis using Landsat-8/OLI data in wet conditions and dry seasons. Other studies by Meroni et al. (2017), Rembold et al. (2015) and AlMaazmi and Al-Ruzouq (2021) have also shown the powerful capabilities of Landsat-8/OLI and common classification algorithms in the spatio-temporal identification of Prosopis distribution, as well as Pereira et al. (2013), who, in the Brazilian semiarid region, attempted to analyze vegetation cover and the expansion of algaroba over a twenty-year period.
The introduction of the Landsat system in 1972 provided a satellite system that covered most of the world. These systems have provided multi-date datasets that allow the monitoring of dynamic landscape features, thus making it possible to detect and quantify any significant change in vegetation cover in space and time. South Africa, India and Sudan are among the countries where remote sensing and GIS technology have been used to map and monitor the colonization of Prosopis species. The results showed the potential of remote sensing to detect any form of alien invasion (Lillesand; Kiefer, 1994; Amboka; Ngigi, 2015).
In this sense, it is known that several remote sensing technologies have already been proposed to detect and map invasive Prosopis strata. This shows the importance of remote sensing technologies, such as orbital satellites, for detecting and monitoring areas invaded by Prosopis species, as shown in (Figure 3).
Figure 3 – (a) Targeted Detecting Sensors (Song et al., 2015); (b) Schematic of proposed system by Baskaran et al. (2017); (c) Prosopis velutina along the riverine system of the Molopo River District in the North-West Province (NWP) of South Africa (Tiawoun et al., 2023). Schemes of how remote sensing can be employed for Prosopis species
Source: Song et al. (2015); Baskaran et al. (2017) and Tiawoun et al. (2023)
Figure 3 shows a proposal by Song et al. (2015) for an intelligent spraying system by introducing sensing techniques including machine vision, spectral reflectance and remote sensing to help consider a spraying system to address precision agriculture through weed control. Baskaran et al. (2017) developed an affordable and innovative aerial vehicle called an unmanned autonomous vehicle (UAV) and an unmanned ground vehicle (UGV) system. The UAV is capable of automatically identifying weed seedlings and sprouts. The UGV is a ground vehicle used to cut down the trees identified by the UAV.
Interest in employing multispectral remote sensing with high and very high spatial resolution of multitemporal data to study biological invasions in plant communities has grown considerably (Khare et al., 2019). Remote detection of invasive alien plants is often difficult, as it occurs in disturbed areas, “mixed pixels” and under the canopy (Bradley, 2014). Multispectral data contains several to hundreds of bands, and hyperspectral data contains hundreds to thousands of contiguous bands. The morphological and spectral characteristics of the image are more important (Song et al., 2015). Thus, (Figure 4) shows some of the countries whose research applied remote sensing to detect Prosopis invasion.
Figure 4 – Countries whose scientific publications on remote sensing were applied to invasive species Prosopis spp.
Source: The authors (2022).
Considering Figure 4, which refers to the areas of spread of species of the genus Prosopis spp in the world, based on the research carried out, it is clearer to see which countries have an invasion of these canopies. Tropical and subtropical moist and dry forests (TSMF and TSDF) have a moderate chance of being among the biomes at high risk of invasion. Flooded grasslands and savannas (FGS) and mangroves are less likely to be among the biomes at high risk of invasion (Dakhil et al., 2021) (Figure 5).
Figure 5 – (a) Number of some publications on the topic; (b) P. juliflora starting to invade Caatinga area (The authors, 2023); (c) Mesquite trees in the dam area being able to visualize significant soil erosion process, Monteiro, Paraíba, Brazil (Amador et al., 2011)
Source: The authors (2023) and Amador et al. (2011)
Figure 5 shows India’s significant spatial distribution in relation to the number of published studies involving the application of remote sensing for the detection and mapping of Prosopis as well as strata of the P. juliflora species in the semiarid region of northeastern Brazil.
Müllerová et al. (2013) indicate that automated and semi-automated remote sensing approaches are cost-effective solutions for regular monitoring of invasive alien species (IAS). Miao et al. (2011) found that hyperspectral aerial imagery with 227 bands and 1-meter resolution can be an alternative for detecting and mapping Prosopis glandulosa.
In addition, biophysical variables derived from satellite remote sensing data have a huge contribution to make in describing forest variables (Ahmed; Atzberger; Zewdie, 2021). The lack of up-to-date information on the Prosopis invasion has meant that current management and monitoring have been unsuccessful. The identification of Prosopis species, their location and rate of spread are key to providing reliable and accurate information on spatial and temporal spread and the level of invasion in the native ecological community (Adam; Mureriwa; Newete, 2017).
According to Choudhary and Ramkumar (2020), remote sensing detection using LANDSAT data and the application of classification algorithms can help monitor and control invasive species. However, it is worth noting that, considering the period studied, several studies have been implemented, including those employing different geotechnologies for the detection of Prosopis species in various environments, using different resolutions (Table 2).
Table 2 – Comparison of the most relevant studies of areas invaded by Prosopis
|
Author |
Location/Study Area |
Positive/Negative Impact |
Resolution |
|
Walter (2011) |
Southern Indian state |
Used in coal production, but extensively covered resources. |
Needs to eradicate and control further growth. |
|
Hoshino et al. (2012) |
Sudan, North Africa |
Invaded water resources, grazing roads |
Proposed remote sensing approach to locate the mesquite. |
|
Haregeweyn et al. (2013) |
Distant state, Ethiopia |
Aggressive invader and expected to cover 30% of total area by 2020. |
Management is needed to take significant measures to control growth. |
|
Van den Berg (2013) |
Northern Cape city, South Africa |
Occupied area increased from 4.13% in 1973 to 28.45% in 2007. |
Used remote sensing and geographic image sensing to track Prosopis invasions. |
|
Pasha et al. (2014) |
The Great Rann of Kachchh, Gujarat, India |
42.9% of total area invaded by Prosopis until 2011. Used for fencing and furniture production. |
Need for control measures to be taken by the government to control aggressive growth. |
|
Shackleton (2014) |
Global review of Prosopis |
Worldwide invasive mesquite. Good charcoal producer. |
Proposed strategies and plans necessary to control the growth of Prosopis juliflora. |
|
Meroni et al. (2016) |
Somaliland, Ethiopia |
9% of the total area has already been occupied. |
Random classifier applied to remote sensing data and achieved 84% accuracy in identifying the species. |
|
Vidhya et al. (2017) |
Ramanathapuram, Tamil Nadu, India |
Invasive mesquite. |
Applied support vector machine algorithm to classify Prosopis coverage. |
|
Baskaran et al. (2017) |
Tamil Nadu, India |
Invasive mesquite. |
Proposed UAV-UGV system to track and eradicate Prosopis juliflora. |
|
Mbaabu et al. (2019) |
Baringo, Kenya |
Prosopis invading land at a rate of 640 hectares per year |
Using random classifier achieved 98.5% accuracy in identifying Prosopis. |
Source: Choudhary and Ramkumar (2020)
As shown in Table 1, invasive stands of Prosopis are capable of occupying vast areas in a short period of time and, at the same time, generating positive impacts for communities, such as charcoal production. Therefore, the ability of remote sensors integrated with various satellites to demonstrate the actual presence or absence of the species’ invasion becomes relatively simple. In addition, during the analysis, this study also identified the most frequently studied species related to the topic discussed (Table 3).
Tabela 3 – Species Prosopis identified in this study.
|
Species |
Country |
Continent |
|
Prosopis glandulosa |
United Arab Emirates |
Asia |
|
Prosopis cineraria |
South Africa, Indian |
Africa, Asia |
|
Prosopis juliflora |
Saudi Arabia, Brazil |
Asia, America, Africa |
|
Prosopis chilensis |
Ethiopia |
Africa |
Source: The authors (2023).
Prosopis juliflora shows proportional invasiveness in environments in the Americas and Asia, while other species, such as Prosopis cineraria, tend to have greater adaptability in the arid regions of the African continent, mainly in East Africa. African countries are at high risk of invasion, such as Kenya, Ethiopia, Somalia, South Sudan and Sudan in general, where this species has been introduced for economic purposes, mainly for firewood, timber, fodder and other uses (Mod et al., 2016; Wang et al., 2019; Dakhil et al., 2021).
3.3 Word Frequency
Word clouds (WC) are graphic representations that show the frequency of words used in a text. Using algorithms, it is possible to build images made up of dozens of words, where the size of each word indicates its frequency or thematic relevance in hundreds or thousands of posts (Vasconcellos-Silva; Araújo-Jorge, 2019).
In this analysis, the words that featured most prominently in the textual review, forming the word cloud, were generated using Atlas.ti software, which allows the study of the words that appear most frequently in the articles analyzed (Sievers; Sprung; Santos, 2017) (Figure 6).
Figure 6 – Cloud Word
Source: The authors (2023)
Figure 6 above shows the 72 words and concepts used most frequently in the titles of research articles. The font size reflects their frequency, with a larger size indicating greater repetition. Words such as “Prosopis”, “juliflora”, “invasion” and “remote” indicate the rapid colonization of the tree, highlighting the need for measures focused on using remote technologies to identify it.
Terms such as “satellite”, “Landsat” and “Datum”, commonly used in the field of geotechnologies, geoprocessing and remote sensing, represent the native plants that are negatively affected by P. juliflora.
Research focusing on the applicability of remote sensing geotechnologies for the detection or discovery of invasive Prosopis spp. trees is still limited, mainly in terms of literature reviews. It is common to address the investigation of the harms and benefits associated with the use of these technologies, as well as to identify the most accurate methods for differentiating the target species from other existing endemic native species.
In Brazil, studies using remote sensing for Prosopis species are scarce, despite being one of the ways to monitor the spread of the invasive species in the dry environments of the Brazilian Northeast. It is essential to emphasize the importance of involving other stakeholders in this issue, such as biologists specializing in botany, agronomists and environmental engineers, among others.
Therefore, by forming interdisciplinary teams, it is possible to carry out a more thorough examination and analysis of the invasion strata of the aforementioned tree species, using remote sensing in conjunction with other technologies to identify any damage affecting population groups, especially those residing in rural environments. This collaborative approach will allow government agencies responsible for inspections, environmental monitoring and law enforcement to make informed decisions about the invasion and control of invasive Prosopis species.
The Coordination for the Improvement of Higher Education Personnel - CAPES and the Foundation for the Support of Science and Technology of the State of Pernambuco - FACEPE for the incentives granted for the development of Master’s and Doctorate research with the respective Postgraduate Programs of the following educational institutions:
1) Stricto Sensu Graduate Program in Forestry Sciences - PPGF, State University of the Midwest – UNICENTRO;
2) Stricto Sensu Graduate Program in Geography - PPGEO, Federal University of Ceará – UFC;
3) Graduate Program in Geography – PPGEO, Federal University of Pernambuco – UFPE and;
4) Graduate Program in Development and Environment – PRODEMA, Federal University of Pernambuco – UFPE.
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Authorship contribution
1 – Charline Zangalli
Forestry Engineer from the State University of Santa Catarina (2018), with a master’s degree in Forest Sciences from the State University of Santa Catarina (2020). She is currently a doctoral student in the Postgraduate Program in Forest Sciences at the State University of Centro-Oeste. She has experience in the area of Forest Restoration.
https://orcid.org/0000-0002-7667-3389 – charlineeng@gmail.com
Contribution: Writing – First Draft; Conceptualization
2 – Eduardo Luiz Costa Tobias Pinto
Master in Forestry Sciences from the State University of the Central-West - UNICENTRO
https://orcid.org/0009-0000-0290-1783 * eduardotobi@hotmail.com
Contribution: Writing, Editing, Proofreading and Publishing
3 – Fabrício William de Ávila
PhD in Soil Science from the Federal University of Lavras – UFLA
Associate Professor in the Department of Forestry Engineering at the State University of the Central-West – DEF/UNICENTRO
https://orcid.org/0000-0003-0301-2720 * fwavila@unicentro.br
Contribution: Formal Analysis, Fundraising, Project Administration, Investigation, Supervision, Validation, Visualization, Review and Editing
4 – Fernando da Silva Alexandre
Master’s student in the Postgraduate Program in Development and Environment at UFPE, Bachelor’s and laureate in the Bachelor’s Degree in Geography at UFP and Researcher at LaGMA - Laboratory of Geoprocessing and Environmental Modeling, UPE.
https://orcid.org/0000-0003-0896-9433 – fnando257@gmail.com
Contribution: Writing – First Draft; Conceptualization
5 – Hannah Cristina Botelho Lima de Fanola
PhD student in Forestry Sciences at the State University of Central-West - Unicentro. Specialist in Environmental Auditing and Expertise from the Pedagogical Institute of Minas Gerais.
https://orcid.org/0009-0003-9401-4867– hannah_fanola@hotmail.com
Contribution: Writing – First Draft; Conceptualization
6 – Kátia Cylene Lombardi
PhD in Chemistry from the Federal University of Paraná - UFPR
Associate Professor in the Department of Forestry Engineering at the State University of the Central-West – DEF/UNICENTRO.
https://orcid.org/0000-0003-2388-2985 * kclombardi@unicentro.br
Contribution: Formal Analysis, Fundraising, Project Administration, Investigation, Supervision, Validation, Visualization, Review and Editing
7 – Magna Soelma Beserra de Moura
PhD in Natural Resources from the Federal University of Campina Grande - UFCG Researcher, Embrapa Tropical Agroindustry
https://orcid.org/0000-0002-2844-1399 * magna.moura@embrapa.br
Contribution: Formal Analysis, Fundraising, Project Administration, Investigation, Supervision, Validation, Visualization, Review and Editing
8 – Maria Luíza Coelho Cavalcanti
Journal article – Composition And Horizontal Structure Of The Shrush-Tree Component Of A Seasonally Dry Tropical Forest, Acta Veterinaria Brasilica – Comparison of conventional and imaging methods to obtain morphometric measurements of the Nordestino breed horse and Journal of Hyperspectral Remote Sensing – Multi-temporal Analysis of Vegetation Coverage by Vegetation Index in the Municipality of Cabaceiras, Semi-Arid Region of Paraíba.
https://orcid.org/0000-0002-9840-4825 – maria.luiza@insa.gov.br
Contribution: Writing – First Draft; Conceptualization
9 –Patrício Rinaldo dos Santos
PhD student in Forest Sciences at the State University of Centro-Oeste - UNICENTRO (Class 2022.2), in the research line of soils and plant nutrition. Master in Development and Environment from the Federal University of Pernambuco - UFPE (2022) in the research line of Environmental Management and Technology.
https://orcid.org/0000-0002-7511-4788 – patriciorinaldo21@hotmail.com
Contribution: Writing – First Draft; Conceptualization
10 – Renilson Pinto da Silva Ramos
PhD student in Geography at the Federal University of Ceará since 2021. Master’s degree from the Postgraduate Program in Geography at UFPB (2020).
https://orcid.org/0000-0003-3209-514X – renilsonr5@hotmail.com
Contribution: Writing – First Draft; Conceptualization
11 – Sérgio Elias Libombo
Graduated in sociology from Eduardo Mondlane University, Mozambique (2012), Master in Territorial Development and Environment from the University of Araraquara - UNIARA (2017). PhD in Development and Environment from the Federal University of Pernambuco - UFPE (2023).
https://orcid.org/0000-0002-4682-3040 – sergio.libombo@ufpe.br
Contribution: Writing – First Draft; Conceptualization
Conflict of Interest
The authors have stated that there is no conflict of interest.
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Chief Editor
Cristiano Rodrigo Bohn Rhoden
How to quote this article
Zangalli, C.; Pinto, E. L. C. T.; Ávila, F. W. de; Alexandre, F. da S.; Fanola, H. C. B. L. de; Lombardi, K. C.; Moura, M. S. B. de; Cavalcanti, M. L. C.; Santos, P. R. dos; Ramos, R. P. da S.; Libombo, S. E. Applicability of remote sensing technologies in the detection and mapping of Prosopis spp species: a systematic review. Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental. Santa Maria, 2025. v.29, e84309. DOI: 10.5902/22361170 . Disponível em: https://doi.org/10.5902/2236117084309 .