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Paliwal A, Mhelezi M, Galgallo D, Banerjee R, Malicha W, Whitbread A. Utilizing Artificial Intelligence and Remote Sensing to Detect Prosopis juliflora Invasion: Environmental Drivers and Community Insights in Rangelands of Kenya. PLANTS (BASEL, SWITZERLAND) 2024; 13:1868. [PMID: 38999708 PMCID: PMC11244349 DOI: 10.3390/plants13131868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 05/31/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024]
Abstract
The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems.
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Affiliation(s)
- Ambica Paliwal
- International Livestock Research Institute, P.O. Box 30709, Nairobi 00100, Kenya; (D.G.); (R.B.); (W.M.)
| | - Magdalena Mhelezi
- International Livestock Research Institute, c/o IITA, Mwenge Coca-Coal Road, Dar es Salam 34441, Tanzania; (M.M.); (A.W.)
| | - Diba Galgallo
- International Livestock Research Institute, P.O. Box 30709, Nairobi 00100, Kenya; (D.G.); (R.B.); (W.M.)
| | - Rupsha Banerjee
- International Livestock Research Institute, P.O. Box 30709, Nairobi 00100, Kenya; (D.G.); (R.B.); (W.M.)
| | - Wario Malicha
- International Livestock Research Institute, P.O. Box 30709, Nairobi 00100, Kenya; (D.G.); (R.B.); (W.M.)
| | - Anthony Whitbread
- International Livestock Research Institute, c/o IITA, Mwenge Coca-Coal Road, Dar es Salam 34441, Tanzania; (M.M.); (A.W.)
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Incorporating satellite remote sensing for improving potential habitat simulation of Prosopis cineraria (L.) Druce in United Arab Emirates. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Dagamac NHA, Bauer B, Woyzichovski J, Shchepin ON, Novozhilov YK, Schnittler M. Where do nivicolous myxomycetes occur? – Modeling the potential worldwide distribution of Physarum albescens. FUNGAL ECOL 2021. [DOI: 10.1016/j.funeco.2021.101079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Eppinga MB, Baudena M, Haber EA, Rietkerk M, Wassen MJ, Santos MJ. Spatially explicit removal strategies increase the efficiency of invasive plant species control. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02257. [PMID: 33159346 PMCID: PMC8047905 DOI: 10.1002/eap.2257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 07/15/2020] [Accepted: 08/16/2020] [Indexed: 06/11/2023]
Abstract
Effective management strategies are needed to control expansion of invasive alien plant species and attenuate economic and ecological impacts. While previous theoretical studies have assessed optimal control strategies that balance economic costs and ecological benefits, less attention has been paid to the ways in which the spatial characteristics of individual patches may mediate the effectiveness of management strategies. We developed a spatially explicit cellular automaton model for invasive species spread, and compared the effectiveness of seven control strategies. These control strategies used different criteria to prioritize the removal of invasive species patches from the landscape. The different criteria were related to patch size, patch geometry, and patch position within the landscape. Effectiveness of strategies was assessed for both seed dispersing and clonally expanding plant species. We found that, for seed-dispersing species, removal of small patches and removal of patches that are isolated within the landscape comprised relatively effective control strategies. For clonally expanding species, removal of patches based on their degree of isolation and their geometrical properties comprised relatively effective control strategies. Subsequently, we parameterized the model to mimic the observed spatial distribution of the invasive species Antigonon leptopus on St. Eustatius (northern Caribbean). This species expands clonally and also disperses via seeds, and model simulations showed that removal strategies focusing on smaller patches that are more isolated in the landscape would be most effective and could increase the effectiveness of a 10-yr control strategy by 30-90%, as compared to random removal of patches. Our study emphasizes the potential for invasive plant species management to utilize recent advances in remote sensing, which enable mapping of invasive species at the high spatial resolution needed to quantify patch geometries. The presented results highlight how this spatial information can be used in the design of more effective invasive species control strategies.
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Affiliation(s)
- Maarten B. Eppinga
- Department of GeographyUniversity of ZurichZurichSwitzerland
- URPP Global Change and BiodiversityUniversity of ZurichZurichSwitzerland
| | - Mara Baudena
- Copernicus Institute of Sustainable DevelopmentUtrecht UniversityUtrechtThe Netherlands
| | - Elizabeth A. Haber
- Copernicus Institute of Sustainable DevelopmentUtrecht UniversityUtrechtThe Netherlands
| | - Max Rietkerk
- Copernicus Institute of Sustainable DevelopmentUtrecht UniversityUtrechtThe Netherlands
| | - Martin J. Wassen
- Copernicus Institute of Sustainable DevelopmentUtrecht UniversityUtrechtThe Netherlands
| | - Maria J. Santos
- Department of GeographyUniversity of ZurichZurichSwitzerland
- URPP Global Change and BiodiversityUniversity of ZurichZurichSwitzerland
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A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents. REMOTE SENSING 2020. [DOI: 10.3390/rs12244021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.
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Huang Y, Dong Y, Huang W, Ren B, Deng Q, Shi Y, Bai J, Ren Y, Geng Y, Ma H. Overwintering Distribution of Fall Armyworm ( Spodoptera frugiperda) in Yunnan, China, and Influencing Environmental Factors. INSECTS 2020; 11:insects11110805. [PMID: 33203176 PMCID: PMC7696661 DOI: 10.3390/insects11110805] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/22/2020] [Accepted: 11/13/2020] [Indexed: 11/16/2022]
Abstract
Simple Summary The fall armyworm (Spodoptera frugiperda) is a nondiapausing insect pest capable of causing large reductions in the yield of crops, especially maize. Every year, the new generation of fall armyworms from Southeast Asia flies to East Asia via Yunnan, and some of them will grow, develop and reproduce in Yunnan since the geographical location and environmental conditions of Yunnan are very beneficial for the colonization of fall armyworms. This study explored the potential overwintering distribution of fall armyworms in Yunnan and the influence of environmental factors on its distribution. These results provide a basis for the precise prevention and control of fall armyworms by guiding management and decision-making and may facilitate meaningful reductions in pesticide application. Abstract The first fall armyworm (FAW; Spodoptera frugiperda) attack in Yunnan, China, occurred in January 2019. Because FAW lacks diapause ability, its population outbreaks largely depend on environmental conditions experienced during the overwinter months. Thus, there is an urgent need to make short-term predictions regarding the potential overwintering distribution of FAW to prevent outbreaks. In this study, we selected the MaxEnt model with the optimal parameter combination to predict the potential overwintering distribution of FAW in Yunnan. Remote sensing data were used in the prediction to provide real-time surface conditions. The results predict variation in the severity and geographic distribution of suitability. The high potential distribution shows a concentration in southwestern Yunnan that suitability continues to increase from January to March, gradually extending to eastern Yunnan and a small part of the northern areas. The monthly independent contributions of meteorological, vegetation, and soil factors were 30.6%, 16.5%, and 3.4%, respectively, indicating that the suitability of conditions for FAW was not solely dominated by the weather and that ground surface conditions also played a decisive role. These results provide a basis for the precise prevention and control of fall armyworms by guiding management and decision-making and may facilitate meaningful reductions in pesticide application.
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Affiliation(s)
- Yanru Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Yingying Dong
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Correspondence: (Y.D.); (W.H.)
| | - Wenjiang Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Correspondence: (Y.D.); (W.H.)
| | - Binyuan Ren
- National Agricultural Technology Extension and Service Center, Beijing 100125, China;
| | - Qiaoyu Deng
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China;
- Key Lab. of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Yue Shi
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
| | - Jie Bai
- University of Chinese Academy of Sciences, Beijing 100049, China;
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu Ren
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Yun Geng
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Huiqin Ma
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (Y.H.); (Y.R.); (Y.G.); (H.M.)
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Abstract
Weeds can impact many ecosystems, including natural, urban and agricultural environments. This paper discusses core weed biosecurity program concepts and considerations for urban and peri-urban areas from a remote sensing perspective and reviews the contribution of remote sensing to weed detection and management in these environments. Urban and peri-urban landscapes are typically heterogenous ecosystems with a variety of vectors for invasive weed species introduction and dispersal. This diversity requires agile systems to support landscape-scale detection and monitoring, while accommodating more site-specific management and eradication goals. The integration of remote sensing technologies within biosecurity programs presents an opportunity to improve weed detection rates, the timeliness of surveillance, distribution and monitoring data availability, and the cost-effectiveness of surveillance and eradication efforts. A framework (the Weed Aerial Surveillance Program) is presented to support a structured approach to integrating multiple remote sensing technologies into urban and peri-urban weed biosecurity and invasive species management efforts. It is designed to support the translation of remote sensing science into operational management outcomes and promote more effective use of remote sensing technologies within biosecurity programs.
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Vaz AS, Alcaraz-Segura D, Campos JC, Vicente JR, Honrado JP. Managing plant invasions through the lens of remote sensing: A review of progress and the way forward. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 642:1328-1339. [PMID: 30045513 DOI: 10.1016/j.scitotenv.2018.06.134] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 06/10/2018] [Accepted: 06/11/2018] [Indexed: 06/08/2023]
Abstract
Biological invasions are a challenging driver of global environmental change and a fingerprint of the Anthropocene. Remote sensing has gradually become a fundamental tool for understanding invasion patterns, processes and impacts. Nevertheless, a quantitative overview of the progress and extent of remote sensing applications to the management of plant invasions is lacking. This overview is particularly necessary to support the development of more operational frameworks based on remote sensing that can effectively improve the management of invasions. Here, we evaluate and discuss the progress, current state and future opportunities of remote sensing for the research and management of plant invasions. Supported on a systematic literature review, our study shows that, since the 1970s, remote sensing was mainly used to map and identify invasive plants, evolving, around the mid-2000s, towards a tool for assessing invasion impacts. Although remote sensing studies often focus on detecting plant invaders at advanced invasion stages, they can also contribute to the prediction of early invasion stages and to the assessment of their impacts. Despite the growing awareness of technical limitations, remote sensing offers many opportunities to further improve the management of plant invasions. These opportunities relate to the capacity of remote sensing to: (a) detect and evaluate the extent of invasions, assisting on any management option aiming at mitigating plant invasions and their impacts; (b) consider modelling frameworks that anticipate future invasions, supporting the prevention and eradication at early invasion stages and protecting ecosystems and the services they provide; and (c) monitor changes in invasion dominance, as well as the resulting impacts, supporting mitigation, restoration and adaptation actions. Finally, we discuss the way forward to make remote sensing more effective in the scope of invasion management, considering current and future Earth observation missions.
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Affiliation(s)
- Ana Sofia Vaz
- Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO), Campus Agrário de Vairão, Rua Padre Armando Quintas, PT4485-661 Vairão, Portugal; Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, s/n, PT4169-007 Porto, Portugal.
| | - Domingo Alcaraz-Segura
- Departamento de Botánica, Facultad de Ciencias, Av. Fuentenueva, Universidad de Granada, 18071 Granada, Spain; iecolab. Interuniversitary Institute for Earth System Research (IISTA), Universidad de Granada, Av. del Mediterráneo, 18006 Granada, Spain; Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), Universidad de Almería, Crta. San Urbano, 04120 Almería, Spain.
| | - João C Campos
- Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO), Campus Agrário de Vairão, Rua Padre Armando Quintas, PT4485-661 Vairão, Portugal.
| | - Joana R Vicente
- Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO), Campus Agrário de Vairão, Rua Padre Armando Quintas, PT4485-661 Vairão, Portugal; Laboratory of Applied Ecology, CITAB - Centre for the Research and Technology of Agro-Environment and Biological Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal.
| | - João P Honrado
- Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO), Campus Agrário de Vairão, Rua Padre Armando Quintas, PT4485-661 Vairão, Portugal; Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, s/n, PT4169-007 Porto, Portugal.
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BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine. REMOTE SENSING 2018. [DOI: 10.3390/rs10091455] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales.
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Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China. SUSTAINABILITY 2018. [DOI: 10.3390/su10041081] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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