scholarly journals Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers

2021 ◽  
Vol 13 (8) ◽  
pp. 1494
Author(s):  
James M. Muthoka ◽  
Edward E. Salakpi ◽  
Edward Ouko ◽  
Zhuang-Fang Yi ◽  
Alexander S. Antonarakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Invasive Plant ◽  
Rangeland Management ◽  
Machine Learning Classifiers ◽  
Spectral Bands ◽  
Learning Classifiers ◽  
Ensemble Machine Learning ◽  
Study Results ◽  
Semi Arid ◽  
Sentinel 2

Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems.

scholarly journals Prediction of plant lncRNA by ensemble machine learning classifiers

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Caitlin M. A. Simopoulos ◽  
Elizabeth A. Weretilnyk ◽  
G. Brian Golding
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning

scholarly journals EVALUATION OF MACHINE LEARNING CLASSIFIERS FOR MAPPING FALCATA PLANTATIONS IN SENTINEL-2 IMAGE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 103-108
Author(s):  
J. R. Santillan ◽  
J. L. E. Gesta
Keyword(s):  
Machine Learning ◽  
Maximum Likelihood ◽  
Spatial Resolution ◽  
The Other ◽  
Support Vector ◽  
Limited Information ◽  
Plantation Forest ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Sentinel 2

Abstract. Efficient and accurate mapping of forest and other industrial tree plantations (ITPs) is essential to ensure better monitoring and sustainable management of these plantations. In Caraga Region, Mindanao, Philippines, ITPs planted with Falcata (Paraserianthes falcataria (L.) Nielsen) are widespread and has contributed to more than 50% of the nationwide log production. At present, there is limited information on the location and extent of existing plantations. This provides an opportunity to evaluate satellite remote sensing approaches for mapping these plantations from images, particularly those provided by the Sentinel-2 mission. The objective of this study is to evaluate machine learning classifiers for mapping Falcata plantations in Sentinel-2 image using a 9 × 9 km2 study area in Caraga Region. It also aims to find the best classifier that can provide acceptable levels of accuracy by utilizing only the four bands of the 10-m spatial resolution and the 9 bands of the 20-m spatial resolution Level 2A Sentinel-2 image, respectively. The following classifiers and their variants were evaluated: Linear Support Vector Machine (SVM), Polynomial SVM, Radial Basis Function (RBF) SVM, Artificial Neural Network (Neural Net), Random Forest (RF), and Maximum Likelihood (ML). One or more of these classifiers have been successfully used in in natural and plantation forest mapping, including tree species classification from remotely sensed images. However, their performance and accuracy in detecting and discriminating Falcata plantations is yet to be evaluated. Results of the evaluation showed that the ML classifier has the highest overall accuracy (OA) of 90.90% and has more consistent values for Producer’s Accuracy (PA), and User’s Accuracy (UA) for Falcata and Non-Falcata classes, and hence, provides better Falcata classification results than the other classifiers when the 10-m spatial resolution Sentinel-2 image was used. The accuracy assessment of the 20-m subset classification provides relative different results from that of the 10-m subset, perhaps due to the inclusion of more bands. The highest OA was obtained by the Linear and RBF SVM classifiers at 92.05% each. The SVM classifiers have consistent performance and produce more accurate classification results than the other classifiers (i.e., more than 90% OA, PA, and UA). From these results, it can be concluded that Maximum Likelihood classifier is best to use for Falcata mapping using the 10-m spatial resolution Sentinel-2 image. For the 20-m resolution image, any of the two SVMs (linear or RBF) is more appropriate to use. However, it should be noted that these results are based on classifications where default parameters were used. Improvement in the classification accuracy may be achieved if these parameters were optimized.

Predicting Opuntia stricta (Haw.) in arid and semi-arid environment of Kenya using Sentinel imagery and ensemble machine learning classifiers

2020 ◽  
Author(s):  
James Muthoka ◽  
Pedram Rowhani ◽  
Alexander Antonarakis
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Spatial Resolution ◽  
Regional Scale ◽  
Easy Access ◽  
Alien Plant ◽  
Ensemble Machine Learning ◽  
Pasture Degradation ◽  
Semi Arid ◽  
Sentinel 2

<p>To ensure effective management of Alien plant species especially the invasive demands for knowledge of their spatial availability. The use of satellite remote sensing tools has increasingly provided potential ways to assess spatial availability as compared to the traditional ways that are inadequate to provide similar information in a detailed way. The Copernicus Sentinel satellite images with a high spatial resolution and easy access at no charge provides an opportunity for mapping the spatial variability at a regional scale and in a detailed manner. In this study, we assess the potential of Sentinel 2 images vegetation indices and using ensemble machine learning techniques, map the spatial variability of invasive species (Opuntia stricta) in an arid and semi-arid region of Kenya. To actualize this, we use Sentinel 2 bands and thirty-one vegetation and elevation indices for classification. Field data collected is divided into two (training & validation) and used to get the best model to classify Opuntia stricta and eight other control classes. The best performing model and the highest contributing features are selected for final Opuntia stricta estimation. The random forest algorithm yields the highest accuracy 89% hence is used to classify Opuntia stricta species. Our observation of the overall results indicates that Sentinels in combination with the indices characterized by spatial resolution provide an importance that can be used to discriminate Opuntia stricta species hence providing an opportunity for long term monitoring and management at a fairly acceptable accuracy hence ensuring limited pasture degradation. Therefore, future research should focus on exploring Sentinel time-series images for estimating Opuntia stricta species at a temporal variability.</p>

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