Continuous change detection and classification of land cover using all available Landsat data
基于所有可获得的landsat数据土地覆盖的连续变化检测与分类
abstract
A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. It is capable of detecting many kinds of land cover change continuously as new images are collected and providing land cover maps for any given time. A two-step cloud, cloud shadow, and snow masking algorithm is used for eliminating “noisy” observations.A time series model that has components of seasonality, trend, and break estimates surface reflectance and brightness temperature. The time series model is updated dynamically with newly acquired observations. Due to the differences in spectral response for various kinds of land cover change, the CCDC algorithmuses a threshold derived fromall seven Landsat bands. When the difference between observed and predicted images exceeds a threshold three consecutive times, a pixel is iden-tified as land surface change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model estimation are used as input to the Random Forest Classifier (RFC).We applied the CCDC algorithm to one Landsat scene in New England (WRS Path 12 and Row 31). All available (a total of 519) Landsat images acquired between 1982 and 2011 were used. A random stratified sample design was used for assessing the change detection accuracy, with 250 pixels selected within areas of persistent land cover and 250 pixels selected within areas of change identified by the CCDC algorithm.The accuracy assessment shows that CCDC results were accurate for detecting land surface change, with producer#39;s accuracy of 98% and user#39;s accuracies of 86% in the spatial domain and temporal accuracy of 80%. Land cover reference data were used as the basis for assessing the accuracy of the land cover classification. Theland cover map with 16 categories resulting from the CCDC algorithm had an overall accuracy of 90%.
我们提出了一个用所有可获得的landsat数据来检测土地覆盖的连续变化与分类的新的算法。随着在给定时间内新的影像被收集和提供为土地覆盖图，我们可以探测许多种类的土地覆盖的连续变化，用去除云、云影、雪掩膜算法来消除噪声观测点。一个长时间序列模型，它由季节性、趋势、地表反射率的变化、亮度温度所组成。当观测影像与预测影像之间的差异连续超过一个阈值三次时，这一个像元会被识别为地表变化。土地覆盖的分类将在土地变化检测之后进行。来自估计模型的时间序列模型的系数和均方根误差作为随机森林的输入参数。我们把CCDC算法应用到英格兰的landsat一景影像中（路径12行号31）。利用所有可获得的时间在1982到2011landsat影像（总共519张）。利用随机生成的样本点来评估变化检测的精度，利用250个选中在连续土地覆盖范围内的像元点和250个选中在利用CCDC算法变化识别的范围内像元点。精度评价表明，CCDC的结果对地表变化的探测精度较高，生产者精度为98％，用户精度为86％，空间和时间精度为80％。利用土地覆被参考数据作为评价土地覆被分类精度的依据。CCDC算法生成的16类土地覆盖类型总体精度为90%。
1. Introduction
Mapping and monitoring land cover have been widely recognized as an important scientific goal(Anderson, 1976; Foody, 2002; Friedl et al.,2002; Hansen, Defries, Townshend, amp; Sohlberg, 2000; Homer, Huang,Yang, Wylie, amp; Coan, 2004; Loveland et al., 2000; Wulder et al., 2008).Land cover influences the energy balance, carbon budget, and hydrolog-ical cycle as many different physical characteristics change as a function of land cover, such as albedo, emissivity, roughness, photosynthetic capacity, and transpiration. Land cover change can be natural or anthro-pogenic, but with human activity inch surface has been modified significantly in recent years by various kinds of land cover change. Knowledge of land cover and land cover change is necessary for modeling the climate and biogeochemistry of the Earth system and for many kinds of management purposes. Satellite images have long been used to assess the Earth surface because of repeated synoptic collection of consistent measurements (Lambin amp; Strahler, 1994).
土地覆盖制图和检测土地覆盖变化已被广泛认为是一项重要的科研目标。土地覆盖影响能量平衡、碳循环和水文循环，许多的物理特征随土地覆盖的变化而变化，如反射率、发射率、粗糙度、光合作用能力和蒸腾量。土地覆被变化可以是自然的或人为的，但是随着人类活动的增加，对地表产生了显著的改变，所以近年来产生各种类型的土地覆被变化。土地覆盖和土地覆盖变化的知识对于模拟地球系统的气候和生物地球化学以及对于多种管理方式来说是必要的。由于卫星影像重复综合地收集连续的观测数据，卫星影像已经长时间被用来评估地球表面信息。
1.1.Monitoring land cover change with remote sensing
Images from the Landsat series of satellites are one of the most im-portant sources of data for studying different kinds of land cover change,such as deforestation, agriculture expansion and intensification, urban growth, and wetland loss (Coppin amp; Bauer, 1996; Galford et al., 2008;Jensen, Rutchey, Koch, amp; Narumalani, 1995; Seto et al., 2002; Woodcock,Macomber, Pax-Lenney, amp; Cohen, 2001), due to their long record of con-tinuous measurement, spatial resolution, and near nadir observations(Pflugmacher, Cohen, amp; Kennedy, 2012; Woodcock amp; Strahler, 1987;
Wulder et al., 2008). One of the drawbacks of Landsat data is the rela-tively low temporal frequency. For each Landsat sensor, overpasses ofthe same location occur every 16 days, and data at this temporal frequency are only commonwithin the United Stateswhere the sensors are turned on for every overpass. For other parts of the world, the fre-quency of data collection is generally less, depending on many factors such as cloud cover predictions and For other parts of the world, the fre-quency of data collection is generally less, depending on many factors such as cloud cover predictions and availability of international ground stations of international ground stations (Arvidson, Goward, Gasch, amp;Williams, 2006). Even for images that are collected, clouds reduce the amount of usable data (Zhang,Rossow, Lacis, Oinas, amp; Mishchenko, 2004). Therefore, most change detection algorithms using Landsat have used two dates of Landsat im-ages (Collins amp;Woodcock, 1996; Coppin, Jonckheere, Nackaerts, Muys, amp; Lambin, 2004; Healey, Cohen, Yang, amp; Krankina, 2005; Masek et al.,2008; Singh, 1989). Though these kinds of algorithms are relatively sim-ple to implement, they are not always applicable. It may take a few years to find an ideal pair of Landsat images that are free of clouds,cloud shadows, and snow(hereafter referred to as “clear”) and acquired at the same time of year.
遥感影像检测土地覆盖变化
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