2 METHOD AND MATERIALS
2.1 Study area
Zhejiang Province, one of the most socioeconomically developed provinces
in China, is chosen to be our study area for the following reasons.
Located at the southeastern coast of China, Zhejiang is located at the
southern fringe of the Yangtze River Delta
(Figure
1). Lying between 27°12´-31°30´N and
119°42´-122°06´E, it has a subtropical monsoon climate with abundant
rainfall (1,100 to 2,000 mm on average annually). With a land area of
10,550,600 ha, Zhejiang is a mountainous province characterized mostly
by mountains (~70%), agricultural land
(~20%), and water body (~10%), with
flat areas mainly in the northeast and mountainous areas in the
southwest (Figure 1). As of the end
of 2019, the total population was 58.5 million, of which 70% lived in
cities, where the forest coverage was 61.15% of the total land area
(citation from Zhejiang Statistical Yr book). Zhejiang province, with a
GDP of 954 billion USD and GDP per
capita of 16,474 USD in 2019, ranks the fourth and the fifth in China’s
31 provinces, respectively. The
geographical diversity and rapid economic development have led to
fundamental changes in land cover and land use. Recently, deforestation
associated with urbanization has become a serious problem, which renders
Zhejiang Province an ideal case study area for conducting research on
policy related deforestation.
Figure 1. Location and Elevation of Zhejiang Province, China
2.2 Data sources and
processing
We utilized primarily GlobeLand30 to detect and analyze land use changes
in Zhejiang Province from 2000 to 2020. GlobeLand30 is the first
open-source, fine-scale global land cover database based on remote
sensing models (J. Chen et al., 2015). This dataset is the only product
worldwide on land cover with a 30 meters resolution for the years of
2000, 2010 and 2020. The GlobeLand30 dataset is derived from over 20,000
Landsat and HJ-1 (China Environment and Disaster Reduction Satellite)
imageries using machine-learning models in combination with pixel-level
and object-based processing procedures. In particular, the dataset of
2020 also incorporates the 16-meter resolution GF-1 (China High
Resolution Satellite) multispectral images. The principle of image
selection in the dataset is to select multispectral images of the
vegetation growing season within ± 2 years of the baseline year in which
the data were generated and updated, provided that the images are
cloud-free or with least cloud. For areas that are difficult to acquire
data, the timing of image acquisition can be adjusted to ensure the
integrity of the overall coverage. The classification scheme includes 10
land cover types, which are agricultural land, forest land, grassland,
shrub land, wetland, water body, tundra, artificial surface, bare land,
and perennial snow and ice, with no mosaic pixels (J. Chen et al.,
2015).
Based on a third-party evaluation,
the overall accuracy of classification based on GlobeLand30 (for 2010)
is 83.50% and the Kappa coefficient
is 0.78. This result is from validation effort based on over 150,000
points in 80 tiles of 853 in total.
On the other hand, the overall
accuracy of classification based on
GlobeLand30 (for 2020) is 85.72% and the Kappa coefficient is 0.82
according to our validation results based on over 230,000 points from
the whole datasets using landscape index sampling model (Jun, Ban, &
Li, 2014; Liang et al., 2015).
To better analyze the results of
land use change in Zhejiang Province, we also used data from Zhejiang
Land Statistics Yearbook. Specifically, we analyzed official statistics
on land use change in Zhejiang Province since 2000. To more
comprehensively capture forest loss in Zhejiang Province, we also
selected the European Space Agency
(ESA) - Climate Change Initiative
(CCI) as another data source. With a medium-resolution (300m)
resolution, the dataset has a global coverage from 2000 to 2018, which
classified pixels (using a machine-learning algorithm) into over 22 land
cover categories (for instance, mosaic natural vegetation of tree,
shrub, herbaceous vegetation) (Bontemps et al., 2013). The accuracy of
the map is reported to be 71.5% (Defourny et al., 2017). In our study,
we reclassified the products according to the IPCC classification
criteria and extracted the forest class for further analysis; here for
alignment with our land classification typology, all ESA-CCI land cover
types with trees and mosaic trees and shrubs were reclassified as
forestland.
To further assess the annual change regarding forest loss, we used the
latest version (Version 1.7 Update) of Hansen’s forest cover dataset,
which is available online on the Google Earth Engine (GEE) website and
the Global Forest Watch website. The
most updated dataset contains the layers of 2000 tree canopy cover and
2001-2019 forest loss, providing the information regarding the year of
forest loss. The product has a 30m spatial resolution and is synthesized
by processing 654,178 Landsat 7 ETM+ images in high quality (Hansen et
al., 2013). The dataset defines trees as “all vegetation above 5m in
height” and forest loss as “the mortality or removal of all tree
covers in a 30m by 30m pixel” (Hansen et al., 2013). Hansen’s previous
update of forest gain was in 2012 and thus may be biased from forest
growth in reality. The overall accuracy, assessed by
the Food and Agriculture
Organization (FAO) statistics using both LiDAR surveys and other
satellite data, has been shown to be over 99% (Hansen et al., 2013). In
order to minimize any data error and improve classification accuracy, we
have combined remote sensing datasets with field surveys. The validation
results turned to be fairly good: we have achieved an overall accuracy
of 80% based on Hanen’s data in Zhejiang Province (Xiong et al., 2020).
In addition, we believe that the next generation of Hansen products
(e.g., Version 2.0) may provide more information on actual forest growth
and loss (Zeng et al., 2018). In this assessment, by referring to the
Global Forest Watch website, we set 30% as the threshold of defining
the canopy cover for all the following analyses of forest loss.
The administrative division data of Zhejiang provincial is derived from
Global Administrative Areas (GADM).
These datasets have their own strengths in showing the spatial and
temporal patterns of land change in Zhejiang Province (Table 1), and the
combined use of the results of these data analyses is beneficial in
exploring the spatial and temporal characteristics of land use
transformation.
Table 1. Different land data sources