460
For example, the relationship between stable ecosystem states and rainfall can be
inferred from the global probability density of forests extracted from remotely
sensed forest cover and its relation to rainfall (Verbesselt et al. 2016). Temporal
autocorrelation of NDVI time series and vegetation optical depth from radar over
tropical forests indicated a reduced rate of recovery (critical slowing down) when
tall canopy trees of intact forests under decreasing rainfall approached a tipping
point, inducing high mortality. The rainfall threshold was at a similar level as the
one indicated for stable state transitions from the spatial analysis. Similar to the
temporal warning signals, the patch size distribution of spatially patterned ecosystems, such as arid ecosystems, showed a meltdown when approaching extinction
(Dakos et al. 2011). High-resolution RS data across large spatial scales and patch
size analysis can therefore be used to assess the extinction risk of these vulnerable
ecosystems under decreasing rainfall conditions.
17.3.4 Protected Area Monitoring
RS plays an essential role in monitoring natural ecosystems, especially in protected
areas. Human pressure over these areas has changed dramatically over the last
decades (Geldmann et al. 2014), justifying a need for monitoring. Perhaps the two
most influential papers that first demonstrated the usefulness of RS to monitor biodiversity in protected areas were Liu et al. (2001), which showed ecological degradation in protected areas designed to protect giant pandas (Ailuropoda melanoleuca),
and Asner et al. (2005), which mapped deforestation in the Amazon with Landsat
data and showed large rates of deforestation within legally designated protected
areas. In 2007, the journal Remote Sensing of Environment published a special issue
on monitoring protected areas in which a series of papers provided a framework for
establishing monitoring programs, presented techniques and methods to make operational the use of remotely sensed data in protected area monitoring, and showcased
a few examples linking remotely sensed data to models used to inform ecological
assessments (Gross et al. 2008). RS can aid monitoring of many aspects of biodiversity (Cavender-Bares et al., Chap. 2; Gamon et al., Chap. 16) and ecosystem
functioning within protected areas, including forest extent, land use/land cover
change, local species pool and turnover, invasions (Bolch et al., Chap. 12), and carbon dynamics. The technical aspects of how RS can address some of these issues
are presented in other chapters; here we review a few selected examples.
One of the major uses of RS in monitoring protected areas involves assessing
land cover change and dynamics, for example, due to anthropogenic or natural disturbance. Liu et al. (2001) used Landsat data to estimate changes in forest cover and
giant panda habitat before and after a reserve was created and showed how the
Wolong Nature Reserve was becoming progressively more fragmented and how this
resulted in crucial loss of habitat for the giant panda. Koltunov et al. (2009) showed
how selective logging in the Amazon region led to different forest dynamics and to
land cover change. Asner et al.’s (2005) seminal work was followed by global maps
F. Schrodt et al.
For example, the relationship between stable ecosystem states and rainfall can be
inferred from the global probability density of forests extracted from remotely
sensed forest cover and its relation to rainfall (Verbesselt et al. 2016). Temporal
autocorrelation of NDVI time series and vegetation optical depth from radar over
tropical forests indicated a reduced rate of recovery (critical slowing down) when
tall canopy trees of intact forests under decreasing rainfall approached a tipping
point, inducing high mortality. The rainfall threshold was at a similar level as the
one indicated for stable state transitions from the spatial analysis. Similar to the
temporal warning signals, the patch size distribution of spatially patterned ecosystems, such as arid ecosystems, showed a meltdown when approaching extinction
(Dakos et al. 2011). High-resolution RS data across large spatial scales and patch
size analysis can therefore be used to assess the extinction risk of these vulnerable
ecosystems under decreasing rainfall conditions.
17.3.4 Protected Area Monitoring
RS plays an essential role in monitoring natural ecosystems, especially in protected
areas. Human pressure over these areas has changed dramatically over the last
decades (Geldmann et al. 2014), justifying a need for monitoring. Perhaps the two
most influential papers that first demonstrated the usefulness of RS to monitor biodiversity in protected areas were Liu et al. (2001), which showed ecological degradation in protected areas designed to protect giant pandas (Ailuropoda melanoleuca),
and Asner et al. (2005), which mapped deforestation in the Amazon with Landsat
data and showed large rates of deforestation within legally designated protected
areas. In 2007, the journal Remote Sensing of Environment published a special issue
on monitoring protected areas in which a series of papers provided a framework for
establishing monitoring programs, presented techniques and methods to make operational the use of remotely sensed data in protected area monitoring, and showcased
a few examples linking remotely sensed data to models used to inform ecological
assessments (Gross et al. 2008). RS can aid monitoring of many aspects of biodiversity (Cavender-Bares et al., Chap. 2; Gamon et al., Chap. 16) and ecosystem
functioning within protected areas, including forest extent, land use/land cover
change, local species pool and turnover, invasions (Bolch et al., Chap. 12), and carbon dynamics. The technical aspects of how RS can address some of these issues
are presented in other chapters; here we review a few selected examples.
One of the major uses of RS in monitoring protected areas involves assessing
land cover change and dynamics, for example, due to anthropogenic or natural disturbance. Liu et al. (2001) used Landsat data to estimate changes in forest cover and
giant panda habitat before and after a reserve was created and showed how the
Wolong Nature Reserve was becoming progressively more fragmented and how this
resulted in crucial loss of habitat for the giant panda. Koltunov et al. (2009) showed
how selective logging in the Amazon region led to different forest dynamics and to
land cover change. Asner et al.’s (2005) seminal work was followed by global maps
F. Schrodt et al.