Introduction
Ecosystem services are the benefits that people obtain from various
ecosystems that can be described and measured (Tamayo et al. 2018;
Costanza et al. 1997). Mendelsohn and Olmstead (2009) described the
value of ecosystem services (ESV) as “the sum of what all members of
society would be willing to pay” for “the economic benefit provided by
environmental products or service” (Mendelsohn & Olmstead 2009).
Hence, the estimation of ESV can make a vital contribution to
biodiversity protection and sustainable development (Billé et al. 2012).
Assessments of ESV at the national, regional, basin and even single
ecosystem levels can show how these services support our lives and how
people develop natural resources rationally (Wei et al. 2018). The
valuation methods now available are highly developed and can be mainly
divided into behavioral (revealed preference) methods and attitudinal
(stated preference) methods (Mendelsohn and Olmstead 2009). Behavioral
methods attempt to calculate the environmental value of goods indirectly
through market analysis (Braden et al. 2010; Phaneuf et al. 2008;
Harrington & Portney 1987). Attitudinal methods use subjectively
designed surveys to create a table of ecological value equivalents. Two
common valuation systems include the system created by Costanza in 1997
(Costanza et al. 1997) and the millennium ecosystem assessment framework
(Alcamo 2003).
However, the understanding of ESV is not comprehensive because multiple
types of service interrelate in complex and dynamic ways (Spake et al.
2017). The current research perspectives on ESV consider it to be the
result of a process: “human-driven factors of ecosystem change
ecosystem process and functions ecosystem services”. “Human-driven
factors of ecosystem change” can be interpreted as basic socioeconomic
conditions, including population, GDP, industry structure, and energy
consumption. “Ecosystem process and functions” can be represented by
land and land cover change at the geospatial level, which is
traditionally the most important part of the information used to
estimate ESV (Barbier et al. 2011). However, how ESV interacts with
socioeconomic factors remains ambiguous (Meacham et al. 2016), which
leads to difficulties in the application of ESV in ecological
management. In other words, even if a low ESV area is identified, we
still do not know how to promote it efficiently through regional
planning or industry regulation. Studies started to include the
socioeconomic drivers of ESV into consideration for the implementation
of responsive policies. Yang et al. (2019) found that ESV is tightly
correlated with socioeconomic status. Wu et al. (2019) found nonlinear
relations between GDP and ESV and between population density and ESV,
but no more complete causality was explained.
As one kind of machine learning algorithm, deep learning is a multilayer
perceptron neural network (Reichstein et al. 2019). It offers
significant breakthroughs in solving classification and nonlinear
regression problems (Sze et al. 2017). Deep learning can extract the
valid features of data input through complex computational models and
represent them at a higher level of abstraction, eventually achieving
complex self-learning functions through multiple transformations and
combinations (LeCun et al. 2015). Traditional evaluation and analysis
methods are often not sufficiently effective in describing the
continuous and quantitative rules in a complicated ecosystem (Moore et
al. 2017). Deep learning may be an effective tool for dealing with this
problem.
In this work, deep learning was used to explore the relationships
between “human drivers of ecosystem change” and “ESV” on a dataset
from Nanjing, China. The city of Nanjing is one of the megacities in the
Yangtze River basin; it has experienced rapid economic development since
the 1970s that is still occurring today (Li et al. 2016) (Figure S1). At
the end of the 20th century, the urbanization of Nanjing entered an
accelerated phase, which led to a rapid increase in population,
unreasonable industrial structure, unbalanced land use, high energy
consumption, and environmental degradation (Yuan et al. 2018; Shi et al.
2019). Over the last two decades, the population has increased from 3 M
to 8.5 M, and its GDP has increased from 338.12 billion CNY in 2008 to
1171.51 billion CNY in 2018. As an ecologically sensitive area, the
changes
in its ecological system and services have been continuously monitored
and studied. Taking ESV as a parameter of the ecosystem, a better
understanding of their internal driving mechanism will be conducive to
optimizing local policies and regional planning (Shiferaw et al. 2019).
Methods