Mapping Prosopis glandulosa (mesquite) in the semi-arid environment of South Africa using high-resolution WorldView-2 imagery and machine learning classifiers

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments in comparison with the popularly applied machine learning classifiers. This study seeks to explore the feasibility for using a U-Net deep learning architecture to classify bi-temporal high resolution county scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019 covering Beaufort County, South Carolina. The U-net CNN classification results were compared with two machine learning classifiers, the Random Trees (RT) and the Support Vector Machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%) as opposed to the SVM (81.6%) and RT (75.7%) classifiers for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible nad powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR, multispectral data) to enhance classification accuracy.

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Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Remote Sensing ◽

10.3390/rs70100153 ◽

2014 ◽

Vol 7 (1) ◽

pp. 153-168 ◽

Cited By ~ 135

Author(s):

Yuguo Qian ◽

Weiqi Zhou ◽

Jingli Yan ◽

Weifeng Li ◽

Lijian Han

Keyword(s):

Machine Learning ◽

High Resolution ◽

Land Cover ◽

Land Cover Classification ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

High Resolution Imagery ◽

Object Based ◽

Very High Resolution Imagery ◽

Very High

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

Remote Sensing ◽

10.3390/rs13081494 ◽

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.

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FIRST INVESTIGATION OF MEDITERRANEAN OAK TREE VITALITY WITH HIGH-RESOLUTION WORLDVIEW-3 SATELLITE DATA: COMPARING TEN VEGETATION INDICES AND THREE MACHINE LEARNING CLASSIFIERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1069-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1069-1076

Author(s):

N. Tilly ◽

F. Reddig ◽

U. Lussem ◽

G. Bareth

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

High Resolution ◽

Satellite Data ◽

Vegetation Indices ◽

Ground Truth ◽

Support Vector ◽

Tree Vitality ◽

Machine Learning Classifiers ◽

Learning Classifiers

Abstract. Oak trees are the primary component in Mediterranean agro-silvopastoral systems. Since the second half of the 20th century, however, a severe oak decline has been observed. Climate change reinforces this problem, which is consistent with worldwide observable tree dieback. As the trees have significant ecological and socio-economic functions, their observation and assessment of vitality are increasingly researched. Satellite remote sensing is very well suitable for large-scale surveys of the extensive and sometimes hardly accessible areas. This study investigates the usability of high-resolution WorldView-3 data for the classification of tree vitality. The ground truth was collected on an Andalusian dehesa at the end of September 2019, timely corresponding with the satellite data acquisition. After customary post-processing of the WorldView-3 data, 10 vegetation indices (ARVI, CIgreen, CSI, DPI, EVI, GNDVI, NDVI, PSRI, RENDVI, and RGI) were calculated from the multispectral image. Three machine learning classifiers (Maximum Likelihood, Random Forest, and Support Vector Machine) were then used for a supervised image classification with three vitality classes (healthy, sick, and dead). Independent ground truth data were used for the validation. The best results were achieved with the red edge normalized difference vegetation index (RENDVI) and the Support Vector Machine classifier (F1 scores between 0.27 and 0.72). A maximal overall accuracy of around 0.6 is, however, improvable. Further studies should focus on other classification methods, more reliable ground truth, and combined analyses of spectral and structural data.

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