The coastal environment is a natural and economic resource of extraordinary value, but it constantly modifying susceptible to climate change, human activities hazards. Remote sensing techniques have proved be excellent for area monitoring, the main issue detect borderline between water bodies (ocean, sea, lake or river) land. This research aims define rapid accurate methodological approach, bas...

In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of are shown to be effective in remote sensing data processing tasks, such as classification k-means clustering computing Normalized Difference Vegetation Index (NDVI). processed integration RStoolbox, terra,...

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of aerosol optical depth (AOD) great significance in climate research protection. Based on Landsat 8 Operational Land Imager (OLI) images MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved AODs Nanjing City from 2017 to 2018 using combined D...

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive active remote-sensing technologies can provide spatially explicit information of AGB by using limited number field samples, thus reducing the substantial budgetary cost inventories. The aim current study...

a r t i c l e i n f o The focus of the current study is to compare data fusion methods applied to sensors with medium-and high-spatial resolutions. Two documented methods are applied, the spatial and temporal adaptive reflectance fusion model (STARFM) and an unmixing-based method which proposes a Bayesian formulation to incorporate prior spectral information. Furthermore, the strengths of both ...

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, ...

Production and prediction of land-use/land cover changes (LULCC) map are among the significant issues regarding input of many environmental and hydrological models. Among various introduced methods, similarity-weighted instance-based machine learning algorithm (SimWeight) and Markov-chain with lower complexity and proper performnce are frequently used. The main aim of this study is utilizing Si...

Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial due to natural phenomena and/or human-induced activities. Mango a major fruit crop in India, but mango crops remains challenging task because reason many perennial similar reflectance profiles. Therefore, potent change technique required for different applications such as r...