IEEE Geoscience and Remote Sensing Magazine - March 2021 - 233

Digital Object Identifier 10.1109/MGRS.2020.3032713
Date of current version: 24 December 2020

MARCH 2021

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

Passive Optical
Remote Sensing

r En
ergy

Active Laser Scanning

Outgoing
Pulse
Laser
Beam
Return
Signal
Time

OVERVIEW
Remote sensing has been recognized as an effective technique for describing and analyzing the status of ecosystems
from individual plots to the entire biosphere [1], [2]. Data
sets acquired by near-surface, airborne, and spaceborne
platforms are widely used by ecologists to characterize and
model ecosystem processes and to understand the fundamental theories that shape our ecosystems [3], [4]. The accumulation of time-series remotely sensed imagery from
the Landsat set of sensors, the Sentinel set of sensors, and
Moderate-Resolution Imaging Spectrometer (MODIS) further advances our capability to monitor ecosystem changes
and predict their responses to the changing climate [5]-[8].
Using this type of optical, passive remotely sensed data,
our ecological interpretation depends upon the observed
land surface reflectance, which is a function of the sun
and sensor viewing geometry and of the optical attributes
of the land surface objects (Figure 1) [9], [10]. When these
interpretations are linked to vegetation, they are mostly
limited to the canopy surface since the observed withincanopy and ground reflectance is a function of vertical
plant area densities [11]. However, the vertical information

and internal structure of vegetation are often neglected or
represented by simple geometric models in these types of
ecological studies [12], [13]. Although a wide body of work
is devoted to deriving vegetation structure information
from these passive optical images [14], the accuracy of the
derived vegetation structural attributes has generally been
low [15], [16] and progressed slowly until the emergence of
lidar technology.
Unlike optical remote sensing, which measures land surface reflectance, lidar is an active remote sensing technique
that measures the distance from the sensor to targeted objects by recording the time of flight of emitted laser pulses
(Figure 1). The distance measurements are then converted to
accurate 3D coordinates through the aid of positioning techniques (i.e., integrated navigation algorithms and simultaneous localization and mapping algorithms) [17]-[19].
Because short-wavelength laser pulses can penetrate vegetation canopies through gaps in leaves and branches, lidar can
accurately depict the internal structure of vegetation canopies and shows early potential in overcoming the aforementioned obstacles of optical remote sensing [20]-[23]. Over
the last three decades, lidar has been successfully used to
estimate 3D vegetation attributes [e.g., vegetation height,
leaf area density (LAD), and branching architecture] from
plot to global scales and from individual to group levels
[24], [25], enabling progress toward tackling 3D ecological
problems [26]-[28].

Sola

T

he advent of lidar has revolutionized the way we observe and measure vegetation structure from the ground
and from above and represents a major advance toward the
quantification of 3D ecological observations. Developments in lidar hardware systems and data processing algorithms have greatly improved the accessibility and ease of
use of lidar observations in ecological studies. A wide range
of studies has been devoted to accurately measuring and
modeling vegetation structural and functional attributes
from lidar data across a range of spatial scales (from individual organs to global scales) and ecosystem types (e.g.,
forest, agricultural, grassland, and urban ecosystems).
As lidar technologies and applications advance, there
is an increasing recognition of the importance of studying
3D ecosystem structures. Research has demonstrated that
lidar observations can be effectively used to calibrate and
improve ecological models and produce more detailed and
accurate results, bringing new ecological insights that challenge our existing knowledge. Nevertheless, we argue that
the incorporation of 3D lidar observations into ecological
models remains in its infancy, and the potential of fusing
3D lidar observations with multisource remote sensing
data for contributing to a new understanding of ecological
processes has not yet been fully explored. The acquisition of
3D ecological observations should continue embracing the
era of multidimensional big remote sensing data, bringing
both new challenges and opportunities. Exploring the potential of multitemporal and multiplatform remote sensing
data through data fusion will benefit the next generation of
ecological models.

FIGURE 1. The principles of passive optical remote sensing and
active laser scanning.

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IEEE Geoscience and Remote Sensing Magazine - March 2021

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