This study summarizes the advances in mangrove species mapping based on multispectral
and hyperspectral imagery achieved over the last decade. The influence of species diversity …

Deep learning (DL) has shown promising performances in various remote sensing
applications as a powerful tool. To explore the great potential of DL in improving the …

With the wide availability of remotely sensed data from various sensors, fusion-based tree
species classification approaches have emerged as a prominent and ongoing research …

In this study, an object-oriented, decision-level fusion method is proposed for tree species
classification based on spectral, textural, and structural features derived from multi-spectral …

Abstract Urban development in South America has experienced significant growth and
transformation over the past few decades. South America's urban development and trees …

Considering current problems in the identifying, locating, and supervising of construction
waste, it is difficult to achieve real-time target judgement and identification in the short term …

To sustainably manage forest biodiversity and monitor changes in species patterning,
mapping the spatial distribution of tree species is indispensable. Remote sensing can …

Mapping forest composition using multiseasonal optical time series remains a challenge.
Highly contrasted results are reported from one study to another suggesting that drivers of …

Forest ecosystem detection and assessment usually requires accurate spatial distribution
information of forest tree species. Remote sensing technology has been confirmed as the …

For the classification of hyperspectral imagery (HSI), the convolutional neural network (CNN)
can learn the discriminative spatial-spectral information of the image better than the …