scholarly journals Machine learning with remote sensing data to locate uncontacted indigenous villages in Amazonia

2018 ◽  
Author(s):  
Robert S Walker ◽  
Marcus J Hamilton
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Indigenous Populations ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Small Scale ◽  
Sensing Data ◽  
External Threats

Background. The world’s last uncontacted indigenous societies in Amazonia have only intermittent and often hostile interactions with the outside world. Knowledge of their locations is essential for urgent protection efforts, but their extreme isolation, small populations, and semi-nomadic lifestyles make this a challenging task. Methods. Remote sensing technology with Landsat satellite sensors is a non-invasive methodology to track isolated indigenous populations through time. However, the small-scale nature of the deforestation signature left by uncontacted populations clearing villages and gardens has similarities to those made by contacted indigenous villages. Both contacted and uncontacted indigenous populations often live in proximity to one another making it difficult to distinguish the two in satellite imagery. Here we use machine learning techniques applied to remote sensing data with a training dataset of 500 contacted and 25 uncontacted villages. Results. Uncontacted villages generally have smaller cleared areas, reside at higher elevations, and are farther from populated places and satellite-detected lights at night. A random forest algorithm with an optimally-tuned detection cutoff has a leave-one-out cross-validated sensitivity and specificity of over 98%. A grid search around known uncontacted villages led us to identify 3 previously-unknown villages using predictions from the random forest model. Our efforts can improve policies toward isolated populations by providing better near real-time knowledge of their locations and movements in relation to encroaching loggers, settlers, and other external threats to their survival.

scholarly journals Machine learning with remote sensing data to locate uncontacted indigenous villages in Amazonia

2018 ◽  
Author(s):  
Robert S Walker ◽  
Marcus J Hamilton
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Indigenous Populations ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Small Scale ◽  
Sensing Data ◽  
External Threats

Background. The world’s last uncontacted indigenous societies in Amazonia have only intermittent and often hostile interactions with the outside world. Knowledge of their locations is essential for urgent protection efforts, but their extreme isolation, small populations, and semi-nomadic lifestyles make this a challenging task. Methods. Remote sensing technology with Landsat satellite sensors is a non-invasive methodology to track isolated indigenous populations through time. However, the small-scale nature of the deforestation signature left by uncontacted populations clearing villages and gardens has similarities to those made by contacted indigenous villages. Both contacted and uncontacted indigenous populations often live in proximity to one another making it difficult to distinguish the two in satellite imagery. Here we use machine learning techniques applied to remote sensing data with a training dataset of 500 contacted and 25 uncontacted villages. Results. Uncontacted villages generally have smaller cleared areas, reside at higher elevations, and are farther from populated places and satellite-detected lights at night. A random forest algorithm with an optimally-tuned detection cutoff has a leave-one-out cross-validated sensitivity and specificity of over 98%. A grid search around known uncontacted villages led us to identify 3 previously-unknown villages using predictions from the random forest model. Our efforts can improve policies toward isolated populations by providing better near real-time knowledge of their locations and movements in relation to encroaching loggers, settlers, and other external threats to their survival.

scholarly journals Machine learning with remote sensing data to locate uncontacted indigenous villages in Amazonia

2019 ◽  
Vol 5 ◽  
pp. e170 ◽  
Author(s):  
Robert S. Walker ◽  
Marcus J. Hamilton
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Indigenous Populations ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Small Scale ◽  
Sensing Data ◽  
External Threats

Background The world’s last uncontacted indigenous societies in Amazonia have only intermittent and often hostile interactions with the outside world. Knowledge of their locations is essential for urgent protection efforts, but their extreme isolation, small populations, and semi-nomadic lifestyles make this a challenging task. Methods Remote sensing technology with Landsat satellite sensors is a non-invasive methodology to track isolated indigenous populations through time. However, the small-scale nature of the deforestation signature left by uncontacted populations clearing villages and gardens has similarities to those made by contacted indigenous villages. Both contacted and uncontacted indigenous populations often live in proximity to one another making it difficult to distinguish the two in satellite imagery. Here we use machine learning techniques applied to remote sensing data with a training dataset of 500 contacted and 25 uncontacted villages. Results Uncontacted villages generally have smaller cleared areas, reside at higher elevations, and are farther from populated places and satellite-detected lights at night. A random forest algorithm with an optimally-tuned detection cutoff has a leave-one-out cross-validated sensitivity and specificity of over 98%. A grid search around known uncontacted villages led us to identify three previously-unknown villages using predictions from the random forest model. Our efforts can improve policies toward isolated populations by providing better near real-time knowledge of their locations and movements in relation to encroaching loggers, settlers, and other external threats to their survival.

scholarly journals Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 21 ◽  
Author(s):  
Francisco Rodríguez-Puerta ◽  
Rafael Alonso Ponce ◽  
Fernando Pérez-Rodríguez ◽  
Beatriz Águeda ◽  
Saray Martín-García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Lidar Data ◽  
Sensing Data ◽  
Sentinel 2

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

Remote sensing data fusion and machine learning techniques for mineral exploration

2020 ◽  
Author(s):  
Priscilla Addison ◽  
Stephen Alwon ◽  
Alex Janevski ◽  
Kristopher Purens ◽  
Clyde Wheeler
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Data Fusion ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Sensing Data ◽  
Learning Techniques

scholarly journals Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2078
Author(s):  
Leandro Sosa ◽  
Ana Justel ◽  
Íñigo Molina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Sensing Data ◽  
Hail Damage ◽  
Efficient Detection

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

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