气候变化领域集成服务门户(CCPortal): Monitoring cropland abandonment with Landsat time series

CCPortal

DOI	10.1016/j.rse.2020.111873
	Monitoring cropland abandonment with Landsat time series
	Yin H.; Brandão A.; Jr; Buchner J.; Helmers D.; Iuliano B.G.; Kimambo N.E.; Lewińska K.E.; Razenkova E.; Rizayeva A.; Rogova N.; Spawn S.A.; Xie Y.; Radeloff V.C.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	Cropland abandonment is a widespread land-use change, but it is difficult to monitor with remote sensing because it is often spatially dispersed, easily confused with spectrally similar land-use classes such as grasslands and fallow fields, and because post-agricultural succession can take different forms in different biomes. Due to these difficulties, prior assessments of cropland abandonment have largely been limited in resolution, extent, or both. However, cropland abandonment has wide-reaching consequences for the environment, food production, and rural livelihoods, which is why new approaches to monitor long-term cropland abandonment in different biomes accurately are needed. Our goals were to 1) develop a new approach to map the extent and the timing of abandoned cropland using the entire Landsat time series, and 2) test this approach in 14 study regions across the globe that capture a wide range of environmental conditions as well as the three major causes of abandonment, i.e., social, economic, and environmental factors. Our approach was based on annual maps of active cropland and non-cropland areas using Landsat summary metrics for each year from 1987 to 2017. We streamlined per-pixel classifications by generating multi-year training data that can be used for annual classification. Based on the annual classifications, we analyzed land-use trajectories of each pixel in order to distinguish abandoned cropland, stable cropland, non-cropland, and fallow fields. In most study regions, our new approach separated abandoned cropland accurately from stable cropland and other classes. The classification accuracy for abandonment was highest in regions with industrialized agriculture (area-adjusted F1 score for Mato Grosso in Brazil: 0.8; Volgograd in Russia: 0.6), and drylands (e.g., Shaanxi in China, Nebraska in the U.S.: 0.5) where fields were large or spectrally distinct from non-cropland. Abandonment of subsistence agriculture with small field sizes (e.g., Nepal: 0.1) or highly variable climate (e.g., Sardinia in Italy: 0.2) was not accurately mapped. Cropland abandonment occurred in all study regions but was especially prominent in developing countries and formerly socialist states. In summary, we present here an approach for monitoring cropland abandonment with Landsat imagery, which can be applied across diverse biomes and may thereby improve the understanding of the drivers and consequences of this important land-use change process. © 2020 Elsevier Inc.
英文关键词	Agriculture; Annual land-cover maps; Google Earth Engine; Land abandonment; Land-use change; Signature extension; Training data generation
语种	英语
scopus关键词	Agricultural robots; Agriculture; Classification (of information); Developing countries; Image enhancement; Pixels; Remote sensing; Time series; Abandoned croplands; Annual classification; Classification accuracy; Environmental conditions; Environmental factors; Landsat time series; Pixel classification; Subsistence agriculture; Land use; abandoned land; accuracy assessment; agricultural land; assessment method; food production; grassland; image classification; land use change; Landsat; livelihood; pixel; satellite imagery; socioeconomic conditions; spatial distribution; succession; time series; Brazil; China; Italy; Mato Grosso; Nebraska; Nepal; Russian Federation; Sardinia; Shaanxi; United States; Volgograd [Russian Federation]
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179280
作者单位	SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, United States; Center for Sustainability and the Global Environment (SAGE), Nelson Institute of Environmental Studies, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, United States; Department of Integrative Biology, University of Wisconsin-Madison, 250 N Mills St, Madison, WI 53706, United States; Department of Geography, University of Wisconsin-Madison, 550 N Park St, Madison, WI 53706, United States
推荐引用方式 GB/T 7714	Yin H.,Brandão A.,Jr,et al. Monitoring cropland abandonment with Landsat time series[J],2020,246.
APA	Yin H..,Brandão A..,Jr.,Buchner J..,Helmers D..,...&Radeloff V.C..(2020).Monitoring cropland abandonment with Landsat time series.Remote Sensing of Environment,246.
MLA	Yin H.,et al."Monitoring cropland abandonment with Landsat time series".Remote Sensing of Environment 246(2020).