A new synergistic approach for crop discrimination in a semi-arid region using Sentinel-2 time series and the multiple combination of machine learning classifiers

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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Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.06.016 ◽

2018 ◽

Vol 73 ◽

pp. 179-190 ◽

Cited By ~ 4

Author(s):

Hilton Luís Ferraz da Silveira ◽

Lênio Soares Galvão ◽

Ieda Del’Arco Sanches ◽

Iedo Bezerra de Sá ◽

Tatiana Ayako Taura

Keyword(s):

Arid Region ◽

Airborne Lidar ◽

Lidar Data ◽

Soil Attributes ◽

Semi Arid Region ◽

Airborne Lidar Data ◽

Semi Arid ◽

Sentinel 2

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Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs10121953 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1953 ◽

Cited By ~ 31

Author(s):

Safa Bousbih ◽

Mehrez Zribi ◽

Mohammad El Hajj ◽

Nicolas Baghdadi ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Soil Moisture ◽

Arid Region ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Remotely Sensed Data ◽

Semi Arid Region ◽

Semi Arid ◽

Sentinel 2 ◽

Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

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Sentinel-1 And Sentinel-2 Data For The Characterisation Of The States Of Continental Surface Over A Semi-Arid Region En Tunisia

2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) ◽

10.1109/m2garss47143.2020.9105158 ◽

2020 ◽

Author(s):

Safa Bousbih ◽

Mehrez Zribi ◽

Zohra Lili-Chabaane ◽

Bernard Mougenot ◽

Charlotte Pelletier ◽

...

Keyword(s):

Arid Region ◽

Semi Arid Region ◽

Semi Arid ◽

Sentinel 2

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Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

Journal of Biomechanical Engineering ◽

10.1115/1.4043449 ◽

2019 ◽

Vol 141 (8) ◽

Cited By ~ 4

Author(s):

Tae Hyong Kim ◽

Ahnryul Choi ◽

Hyun Mu Heo ◽

Kyungran Kim ◽

Kyungsuk Lee ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Continuous Time ◽

Classification Accuracy ◽

Fall Detection ◽

Series Data ◽

Machine Learning Classifiers ◽

Detection Model ◽

Learning Classifiers ◽

Proposed Model

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

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