A5074268083- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Species Distribution and Climate Change
- Land Use and Ecosystem Services
- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Climate variability and models
- Precipitation Measurement and Analysis
- Environmental Changes in China
- Conservation, Biodiversity, and Resource Management
Universitat Politècnica de Catalunya
2021
Center For Remote Sensing (United States)
2021
University of Coimbra
2018-2020
National University of San Marcos
2018-2020
This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the series, we reduced seasonality Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single multi-temporal filters were used to reduce speckle noise Synthetic Aperture Radar (SAR) images (i.e., ALOS PALSAR Sentinel-1B) before fusing them with optical through Principal Component Analysis (PCA). We detected only one change two PV a...
In recent years, a growing body of space-borne and drone imagery has become available with increasing spatial temporal resolutions.This remotely sensed data enabled researchers to address tackle broader range challenges effectively by using novel tools data.However, analysts spend an important amount time finding the adequate libraries read process data.
The popularization of cloud computing platforms, combined with the availability large amounts open access remote sensing data, has increased processing capacities land use and cover mapping. At same time, this development can also lead to a reduced performance procedures if these tools are not optimally used. objective paper, for first is evaluate key variables mapping obtained from optical radar data feed deep learning classifiers. Therefore, three different regions world landscapes were...
This letter focuses on the mapping of deforestation between 2008 and 2018 in a small region Brazil through time series. Vegetation indices as variables that are strongly influenced by seasonality were used. Whereas to reduce series, dense series fraction obtained from physical spectral mixture analysis (SMA) model Both Landsat images. Then, changes detected non-seasonal detection approach (called PVts- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...