Island Invasion: The Silent Crisis in Hawaii

Keeping out invasive species may, upon first review, seem like a trivial environmental cry from ecologists and deep environmentalists; a belated wish to return to an undeveloped world where nature was pristine. However invasive species create problems that impact all of us and can have far more severe consequences than changing a stunning landscape. These problems are heightened in islands like Hawaii, where the fragile ecosystems have developed over centuries of evolution and adaptation. The introduction of a disease-carrying mosquito can put the people of Hawaii at risk to many vector-born illnesses and create an epidemic, taking human life. The introduction of invasive plants that outcompete native plants for water can reduce the water available for homes and businesses, as well as restrict the flow in native streams, putting indigenous fish at risk. The loss of coral reef from alien algae or the arrival of snakes may lead to significant drops in tourism, which for Hawaii is one of the largest facets of the economy. Introduced non-native species tend to outcompete the relatively sheltered island species, therefore leading to a decline in biodiversity. In fact, this process of decimation and change in many locations has already begun, with the native forests confined to small swathes of isolated upper slopes of the volcanoes. Rates of extinction are increasing, costs to eradicate invasive species are rising, and still the silent invasion continues, with an onslaught of species accompanying each boat or plane. Because the presence of these unwanted species can significantly damage and alter the ecology of the Hawaiian Islands, there needs to be a targeted effort to mitigate the worst effects. 1 Invasive species is defined throughout this thesis as defined by Bill Clinton in Executive Order 13112 in 1999: Invasive species means an alien species whose introduction does or is likely to cause economic or environmental harm or harm to human health. 11 It is important to outline the ways in which invasive species can harm Hawaii, as well as provide a detailed study of the scale of the problem. This thesis will examine several research questions surrounding best practices for invasive species management policy in Hawaii.2 The ensuing chapters will seek to answer a variety of questions. What gaps exist in this policy, if any? What measures can be taken to prevent damages from invasive species in Hawaii? What role can government agencies play in preventing the negative consequences of these alien species on the people and land of Hawaii? What should the government role look like? What pathways introduce harmful nonnative species? What are the best practices for managing invasive species internationally

KELAYAKAN KREDIT BANK MENGGUNAKAN C4.5 BERBASIS PSO

Abstract— Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 algorithm and the neural network. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy value of C4.5. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show that the accuracy of C4.5 increased from 72.3% to 75.50% after being combined with PSO. Keywords: Credit, German Credit Data, C4.5-PSO. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVM

KELAYAKAN KREDIT BANK MENGGUNAKAN C4.5 BERBASIS PSO

Abstract— Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 algorithm and the neural network. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy value of C4.5. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show that the accuracy of C4.5 increased from 72.3% to 75.50% after being combined with PSO. Keywords: Credit, German Credit Data, C4.5-PSO. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVMÂ

Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

Funding Information: This work is part of EU Interreg SMARTGREEN project (2017-2021). We would like to thank all the growers (UK & EU), for providing the data. Their valuable feedback, suggestions and comments are highly appreciated to increase the overall quality of this work.Postprin

Invasive Species in Forests and Rangelands of the United States

This open access book describes the serious threat of invasive species to native ecosystems. Invasive species have caused and will continue to cause enormous ecological and economic damage with ever increasing world trade. This multi-disciplinary book, written by over 100 national experts, presents the latest research on a wide range of natural science and social science fields that explore the ecology, impacts, and practical tools for management of invasive species. It covers species of all taxonomic groups from insects and pathogens, to plants, vertebrates, and aquatic organisms that impact a diversity of habitats in forests, rangelands and grasslands of the United States. It is well-illustrated, provides summaries of the most important invasive species and issues impacting all regions of the country, and includes a comprehensive primary reference list for each topic. This scientific synthesis provides the cultural, economic, scientific and social context for addressing environmental challenges posed by invasive species and will be a valuable resource for scholars, policy makers, natural resource managers and practitioners

Pembobotan Atribut Pso Untuk Optimasi Svm Dalam Kasus Kelayakan Kredit Bank

Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 and SVM algorithms. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy of SVM. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show SVM accuracy increased from 74.6% to 76.50% after combined with PSO

Pembobotan Atribut Pso Untuk Optimasi Svm Dalam Kasus Kelayakan Kredit Bank

Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 and SVM algorithms. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy of SVM. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show SVM accuracy increased from 74.6% to 76.50% after combined with PSO

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

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

[EN] Invasive plants are a serious problem in island ecosystems and are the main cause of the extinction of endemic species. Cuba is located within one of the hotspots of global biodiversity, which, coupled with high endemism and the impacts caused by various disturbances, makes it a region particularly sensitive to potential damage by invasive plants like Dichrostachys cinerea (L.) Wight & Arn. (marabú). However, there is a lack of timely information for monitoring this species, as well as about the land use and land cover (LULC) classes most significantly impacted by this invasion in the last few decades and their spatial distribution. The main objective of this study, carried out in Central Cuba, was to detect and monitor the spread of marabú over a 28-year period. The land covers for the years 1994 and 2022 were classified using Landsat 5 TM and 8 OLI images with three different classification algorithms: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). The results obtained showed that RF outperformed the other classifiers, achieving AUC values of 0.92 for 1994 and 0.97 for 2022. It was confirmed that the area covered by marabú increased by 29,555 ha, from 61,977.59 ha in 1994 to 91,533.47 ha in 2022 (by around 48%), affecting key land covers like woodlands, mangroves, and rainfed croplands. These changes in the area covered by marabú were associated, principally, with changes in land uses and tenure and not with other factors, such as rainfall or relief in the province. The use of other free multispectral imagery, such as Sentinel 2 data, with higher temporal and spatial resolution, could further refine the model’s accuracy.S