An ecosystem classification system based on remote sensor information in China

Zhiyun, 欧阳志云 Ouyang; Lu, 张路 Zhang; Bingfang, 吴炳方 Wu; Xiaosong, 李晓松 Li; Weihua, 徐卫华 Xu; Yi, 肖燚 Xiao; Zheng, Hua

doi:10.5846/stxb201407281527

Cited by 10 publications

(8 citation statements)

References 2 publications

(2 reference statements)

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“…Ecosystem types were consistent with the six LULC level I classes in this study, because we had considered the spectrum characteristics, vegetation coverage, and ecosystem plant community component characteristics in our LULC classification system [44]. Thus, ecosystem pattern changes The LULC transfer matrix delivers dynamic information on mutual transfer processes between different LULC types from the beginning to the end of a given period; thus it not only includes the static area data of different LULC types at a point in time, but also contains information about the area transferred out and in during a period [45].…”

Section: Detection Of Ecosystem Pattern Changesmentioning

confidence: 89%

Did Ecological Restoration Hit Its Mark? Monitoring and Assessing Ecological Changes in the Grain for Green Program Region Using Multi-source Satellite Images

et al. 2019

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Ecological restoration programs are expected to control environmental deterioration and enhance ecosystem functions under a scenario of increasing human disturbance. The largest ecological restoration program ever implemented in China, the first round of the countrywide Grain for Green Program (GGP), finished in 2010. However, it is not known whether the ecological changes that resulted from the GGP met the restoration goal across the whole implementation region. In this study, we monitored and assessed the ecological changes in the whole GGP region in China over the lifetime of the first round of implementation (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), by establishing a comprehensive assessment indicator system composed of ecosystem pattern, ecosystem quality (EQ), and key ecosystem services (ESs). Remote sensing interpretation, ecological model simulations based on multi-source images, and trend analysis were used to generate land use and land cover (LULC) datasets and estimate ES and ESs indicators. Results showed that while forest increased by 0.77%, artificial land increased more intensely by 22.38%, and cropland and grassland decreased by 1.81% and 0.68%, respectively. The interconversion of cropland and forest played a primary role in ecosystem pattern change. The increase in ecosystem quality measures, including fractional vegetation cover (0.1459% yr −1 ), leaf area index (0.0121 yr −1 ), and net primary productivity (2.6958 gC m −2 yr −1 ), and the mitigation of ecosystem services deterioration in soil water loss (−0.0841 t ha yr −1 ) and soil wind loss (−1.0071 t ha yr −1 ) in the GGP region, indicated the positive ecological change in the GGP region to some extent, while southern GGP subregions improved more than the those in the north on the whole. The GGP implementation other than climate change impacted ecological change, with contributions of 14.23%, 9.94%, 8.23%, 30.45%, and 18.05% in the ecological outputs mentioned above, respectively. However, the water regulation did not improve (−2283 t km −2 yr −1 ), revealing trade-offs between ecosystem services and inappropriate afforestation in ecological restoration programs. Future GGP implementation should change the practice of large-scale afforestation, and focus more on the restoration of existing forest and cultivation of young plantings, formulating rational and specific plans and designs for afforestation areas through the establishment of near-natural vegetation communities, instead of single-species plantations, guided by regional climate and geographical characteristics.

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Section: Detection Of Ecosystem Pattern Changesmentioning

confidence: 89%

Did Ecological Restoration Hit Its Mark? Monitoring and Assessing Ecological Changes in the Grain for Green Program Region Using Multi-source Satellite Images

et al. 2019

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“…The dataset is based on remote sensing feature classification data. The management modes of land features, community structure, and ecological process differences were analyzed using MODIS satellite data Q13A1 [32,33]. Temperature and precipitation data were provided by the China Meteorological Data Sharing Network (https://data.cma.cn/data/detail/dataCode/A.0012.0001.html, accessed on 5 January 2017) and topographic data, from the United States' GEOM satellite.…”

Section: Study Areamentioning

confidence: 99%

Spatial Trade-Offs and Synergies between Ecosystem Services in Guangdong Province, China

Xu,

Yang,

Yang

et al. 2023

Land

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The trade-offs between ecosystem services directly affect the quality of the ecological environment and the survival and development of human society, which is of great concern to academia, governments, and non-governmental organizations. Guangdong Province is a strong economic performer in China; hence, we selected it to explore the trade-off and synergy differences between different ecosystem services, and to investigate the mechanisms of their influence in economically developed regions with a large population density. Our results showed three main points: (1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. In addition, northern Guangdong has high levels of water retention, with a value of 5804.73 × 104 m3/km2 and high values for carbon sequestration and soil retention. Western Guangdong is a functional area for food production, and the Pearl River Delta is an economically developed region with low levels of ecosystem services. (2) Overall, in Guangdong Province, three pairs of ecosystem services, namely water retention–soil retention, carbon sequestration–water retention, and carbon sequestration–soil retention, showed a strong positive correlation and good synergistic relationships. The other three pairs of relationships show strong trade-off effects. (3) The relationships between similar ecosystem services show completely different characteristics in different regions. Carbon sequestration and water retention, carbon sequestration and biodiversity conservation, water retention and biodiversity conservation, and soil retention and biodiversity conservation were mainly manifested in high–high synergies, particularly in northern Guangdong; carbon sequestration and soil retention and water retention and soil retention, primarily manifested synergies; carbon sequestration and food production, water retention and food production, and soil retention and food production mainly manifested as trade-off relationships.

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“…Land 2024, 13, 845 2 of 20 Accurately mapping and continuously monitoring the spatial and temporal distribution patterns of dryland ecosystems are crucial for protecting these fragile regions and enhancing their resistance and resilience to various disturbances [6]. Early ecosystem mapping mainly relied on field surveys and manual interpretation [7,8], which were inefficient, costly, and unable to meet the requirements of regular and timely monitoring and mapping [9].…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of remote sensing technology, mapping the spatial patterns of ecosystems using land use/land cover as the basic unit has been widely adopted in large-scale, ecosystem-related studies [10][11][12]. For instance, Ouyang et al [13] proposed a remote-sensing-based ecosystem classification framework based on land use classification criteria. Liu et al [14] also attempted to classify and map China's ecosystems by integrating land use with vegetation cover, population density, drought index, elevation, slope, and soil type.…”

Section: Introductionmentioning

confidence: 99%

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Li,

Guo,

Sun

et al. 2024

Land

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Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions.

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An ecosystem classification system based on remote sensor information in China

Abstract: ecosystem classification system based on remote sensor information in China.Acta

Cited by 10 publications

References 2 publications

Did Ecological Restoration Hit Its Mark? Monitoring and Assessing Ecological Changes in the Grain for Green Program Region Using Multi-source Satellite Images

Did Ecological Restoration Hit Its Mark? Monitoring and Assessing Ecological Changes in the Grain for Green Program Region Using Multi-source Satellite Images

Spatial Trade-Offs and Synergies between Ecosystem Services in Guangdong Province, China

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

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