PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2019
467
PHOTOGRAMME TR I C ENG I NE ER I NG & REMOT E SENS I NG
The official journal for imaging and geospatial information science and technology
July 2019 Volume 85 Number 7
FEATURES
PEER-REVIEWED ARTICLES
See the Cover Description on Page
COLUMNS
The Column of the Student Advisory Council.
This month we look at the Democratic and
Popular Republic of Algeria.
ANNOUNCEMENTS
Join us in welcoming our newest members to
ASPRS.
DEPARTMENTS
Kaichang Di, Mengna Jia, Xin Xin, Jia Wang, Bin Liu, Jian Li, Jianfeng
Xie, Zhaoqin Liu, Man Peng, Zongyu Yue, Jia Liu, Ruilin Chen,
and
Changlu Zhang
This study introduces a systematic method for large-area seamless digital orthophoto
map production. The result generated using over 700 Lunar Reconnaissance Orbiter
Camera (LROC) Narrow Angle Camera (NAC) images has a resolution of 1.5 m and covers
a large area of 20° in longitude and 4° in latitude.
Fernando Montes, Álvaro Rubio-Cuadrado, Mariola Sánchez-González,
Isabel Aulló-Maestro, Miguel Cabrera,
and
Cristina Gómez
Proximal sensing technologies under fixed-point sampling inhibits detection of some
trees. This study evaluates various approaches to eliminate problems in fixed point
sampling due to tree occlusions and instrument bias estimates.
Qiang Zhou, Shuguang Liu,
and
Michael Hill
In this study, the authors developed a frequency decomposition method to separate
woody and herbaceous vegetation components using time series for modeling inter-
relationships between trees and grasses, and monitoring fuel loads and biomass for
livestock. They examined the spatial and temporal patterns of the decomposed NDVI,
where woody and herbaceous NDVI showed different responses to precipitation.
Yingbin Deng
and
Changshan Wu
While spectral transformation has been used to compute spectral variability, there is no
study that addresses the necessity and applicability of transformed models. This paper
answers two questions: 1) whether significantly different results will be generated
through applying a spectral transformation, and 2) which spectral transformation per-
forms better in urban environments.
