Towards a polyalgorithm for land use change detection

https://doi.org/10.1016/j.isprsjprs.2018.07.002Get rights and content

Abstract

One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Most of the many TSA based LULCC algorithms proposed in the remote sensing community perform well on datasets for which they were designed, but their performance on randomly chosen datasets from across the globe has not been studied. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based ‘polyalgorithm’ for LULCC is three algorithms (BFAST, EWMACD, and LandTrendR), precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. For a given pixel, Hausdorff distance is used to compare the distance between the change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment.

Introduction

Land use change is described as changes in how humans use the surface of the Earth (e.g., for agriculture, plantations, pastures, managed woods, conservation, settlements, or leaving it alone as natural ecosystem). Changes in land use lead to changes in albedo, thereby directly affecting the temperatures of the surrounding area. Significant and lasting changes in land use and land cover (LULC) have more profound effects. The past century has seen an exponential growth in human activities such as deforestation and urbanization causing significant changes in land cover in several parts of the world (Hansen et al., 2013). Simultaneously, significant changes in the global climate have also been observed, driven in part by LULC change (LULCC) (e.g., Fall et al., 2010). LULCC also has impacts on a wide variety of other ecosystem services. Monitoring LULCC across the globe, therefore, has become the need du jour. Land use change detection comprises any methodology used for determining the occurrence and nature of change in LULC.

Earth observation satellites (EOS) such as Landsat capture images of the Earth’s surface at regular intervals using multiple spectral frequencies. These images hold valuable information that, if harnessed well, can be immensely helpful in understanding, monitoring, and managing our natural resources, as well as studying LULCC. One way of analyzing these satellite images for LULCC studies is time series analysis (or, temporal trajectory analysis). For time series analysis, several images of the scene under consideration, taken over a period of time, are stacked together chronologically and subsequently analyzed. Commonly, the time series for each pixel is treated individually; the full image stack is thus a collection of many time series. The choice of spectral band(s) varies from application to application. The objective is to discover a ‘trend’ in how different relevant variables (indicators) evolve over time. In change detection analysis, when the trajectory of one or more of the variables departs from the normal, a change is detected. Time series analysis for LULCC studies has been receiving increasing attention in the last decade, specifically, after the Landsat data became freely accessible in 2008 (Woodcock et al., 2008). Several time series analysis algorithms have been proposed by different groups in the remote sensing community.

Despite a plethora of time series analysis algorithms available in remote sensing, design and selection of algorithms for LULCC detection in remote sensing appears to be almost always context specific. Most of the methods proposed to date seem to perform well on the type of data that they are designed for. Their performance on randomly picked datasets from across the globe has not been studied. The onus of choosing an appropriate algorithm that will perform well on their particular dataset falls on the user. Unfortunately, no single algorithm designed so far seems to work for all datasets (Cohen et al., 2017). For example, the Western Antarctica as well as the Greenland Ice Sheets are beginning to collapse due to global warming, the melting leading to continually receding snow covers at the respective locations. For these regions, using LULC algorithms based on periodicity assumptions is expected to lead to incorrect predictions and/or false alarms, although the nature and extent of this has not been studied yet. Even if there were no global warming, mild shifts in the ‘phase’ and ‘amplitude’ of seasons are known to take place (Petitjean et al., 2011). Time warping techniques (Petitjean et al., 2011) to deal with these issues may be helpful in some contexts, but their accuracy and scalability has not yet been satisfactorily investigated. Approaches based on periodicity and a moving window are possible, with additional computational costs.

A polyalgorithm is an effective strategy to unite the strengths and circumvent the weaknesses of multiple algorithms that are also individually designed to solve the same problem. The concept of polyalgorithm was introduced by Rice and Rosen (1966). A polyalgorithm uses a combination of several basic methods in a framework. Each of these basic methods is applicable to the same problem, with only their performance and/or success being different for different datasets (inputs). The construction of this framework involves experimenting with an increasingly heterogeneous set of situations to evolve a robust algorithm that is capable of choosing a correct subset of algorithms suitable for a given input, and has performance metrics to integrate their outputs. The details of algorithm selection and processing stay hidden from the user. Polyalgorithms have been designed in the past for solving various problems, for example, nonlinear systems of equations (Rice and Rosen, 1966, Rice, 1969, Rice, 2014, Rice, 1967), matrix computations on parallel architectures (Li, 1996, Häfner et al., 1999), and certain chemical models (Gomeni and Gomeni, 1979).

This work lays the foundation for a polyalgorithm for LULCC detection. Three currently existing, fundamentally different from each other, change detection algorithms are utilized. A similar work in this direction is Zhan et al. (2002), wherein a framework is developed to evaluate five different algorithms on the input dataset, compare them based on certain scores, and then return the best results. Similar approaches are also gaining ground recently in the field of classification algorithms (Dietterich et al., 2001, Kittler et al., 1998, Wozniak et al., 2014). Most recently, in Healey et al. (2017), multiple change detection algorithms are utilized to build a decision trees based ensemble algorithm for LULCC.

The rest of this paper is organized as follows: Section 2 presents background on state-of-the-art change detection algorithms available in remote sensing, puts them in the context of the general time series literature, and explains the choice of algorithms used in this work. Section 3 defines the notation. Sections 4 Exponentially weighted moving average change detection (EWMACD), 5 LandTrendR, 6 Breaks for additive and seasonal trend (BFAST) describe three different trend and change detection algorithms — EWMACD, BFAST, and LandTrendR; experimental results demonstrating the successes, failures, and sensitivity to parameters for each algorithm are presented. Prospects for a viable polyalgorithm are discussed in Sections 7 Prospects for a viable polyalgorithm, 8 Conclusions and future work concludes with an assessment and future work.

Section snippets

Background

Most of the LULC algorithms proposed in the remote sensing literature can be divided into two categories: bitemporal analysis and temporal trajectory analysis. Bitemporal analysis was more popular before 2008 (when the availability of satellite data to the public was very limited) and forms the classical way of analyzing images — these algorithms analyze changes occurring between two images (dates). The more preferred bitemporal algorithms rely on image differencing (Banner and Lynham, 1981,

Preliminaries

Notation and definitions. For an m×n matrix A, an n-vector x,I{1,,m},J{1,,n}, let AIJ denote the submatrix of A formed from the rows indexed by I and the columns indexed by J, and xJ denote the subvector of x indexed by J. AI· (A·J) are the rows (columns) of A indexed by I (J), respectively. An image is an R×C matrix D, where each Drc (pixel) is an S×B matrix, whose (s,b) element (Drc)sb is the signal value at time index s and frequency band index b. S is the number of missing data values

Exponentially weighted moving average change detection (EWMACD)

EWMACD, proposed in Brooks et al. (2014), is a kernel regression approach modeling the time series as a linear combination of trigonometric polynomials. The model is trained over data collected in the initial two (or more) years. When the observations in the subsequent years deviate from the values forecast by the model for a ‘substantial’ length of time (persistence), a change is declared (recovery or disturbance). The training period as well as the persistence are parameters of the algorithm.

LandTrendR

LandTrendR was proposed in (Kennedy et al., 2010). Starting with a linear regression fit to the entire time series, LandTrendR partitions the time series step by step, adding breakpoints (called vertices here) at each step. A set of potential vertices is thus generated in a straight top-down approach. Once these vertices have been generated, they are refined in multiple passes: (i) first the least influential vertices (corresponding to most obtuse angles) are discarded; (ii) then, of the now

Breaks for additive and seasonal trend (BFAST)

BFAST (Verbesselt et al., 2010a) decomposes the given time series iteratively into three components: trend, seasonal, and noise. BFAST computes and evaluates least squares fits in windows of increasing size. Qualitatively, (i) first the possibility of there being any structural change in the given time series is determined by computing the partial sums of residuals of least squares fits in windows (OLS-MOSUM). The limiting process of these partial sums is the increments of a Brownian bridge

Prospects for a viable polyalgorithm

Once the algorithms have been implemented, the next step is to develop a procedure to choose the most appropriate outcome for the input. One possibility is to draw a consensus between algorithm outcomes: if more than half the algorithms being used predict breakpoint sets that are in proximity of each other, one of those algorithms gets chosen as the most appropriate algorithm. To this end, a method to measure the distance between the breakpoint sets is needed. Hausdorff distance from topology

Conclusions and future work

At present, the polyalgorithm offers some improvement over EWMACD or LandTrendR alone, but not over BFAST. Since BFAST is the most rigourous/exhaustive component algorithm, and the polyalgorithm is not as good as BFAST yet, there is surely room for improvement of the polyalgorithm. The experiments carried out in this work are just the beginning of further work towards a viable polyalgorithm. Several directions of research/development must be pursued in this regard.

First, the number of

Author contributions

RS wrote the codes and conducted the experiments; RS, LTW, and RHW wrote the paper; EBB, VAT, YZ, and REK assisted in data collection and validation.

Acknowledgements

This work was supported in part by USDA Forest Service Grant 13-JV-11330145-046, NSF Grant CNS-1565314, U.S. Geological Survey Contract G12PC00073, NASA Grants NNX17AI07G and NNX17AI09G, and the Virginia Agricultural Experiment Station and the McIntire-Stennis Program of NIFA, USDA (Project Number 1007054, “Detecting and Forecasting the Consequences of Subtle and Gross Disturbance on Forest Carbon Cycling”). The authors thank the anonymous reviewers for their insightful feedback on the original

References (88)

  • M. Hussain et al.

    Change detection from remotely sensed images: from pixel-based to object-based approaches

    ISPRS J. Photogram. Remote Sens.

    (2013)
  • S. Jin et al.

    A comprehensive change detection method for updating the National Land Cover Database to circa 2011

    Remote Sens. Environ.

    (2013)
  • R.E. Kennedy et al.

    Trajectory-based change detection for automated characterization of forest disturbance dynamics

    Remote Sens. Environ.

    (2007)
  • R.E. Kennedy et al.

    Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – temporal segmentation algorithms

    Remote Sens. Environ.

    (2010)
  • A. Koski et al.

    Syntactic recognition of ECG signals by attributed finite automata

    Pattern Recogn.

    (1995)
  • U. Ramer

    An iterative procedure for the polygonal approximation of planar curves

    Comput. Graph. Image Process.

    (1972)
  • L. Rudin et al.

    Nonlinear total variation based noise removal algorithms

    Phys. D: Nonlinear Phenom.

    (1992)
  • C.J. Tucker

    Red and photographic infrared linear combinations for monitoring vegetation

    Remote Sens. Environ.

    (1979)
  • J. Verbesselt et al.

    Detecting trend and seasonal changes in satellite image time series

    Remote Sens. Environ.

    (2010)
  • J. Verbesselt et al.

    Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

    Remote Sens. Environ.

    (2010)
  • E. Vintrou et al.

    Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products

    Int. J. Appl. Earth Observ. Geoinform.

    (2012)
  • M. Wozniak et al.

    A survey of multiple classifier systems as hybrid systems

    Inform. Fusion

    (2014)
  • P. Xiao et al.

    Change detection of built-up land: a framework of combining pixel-based detection and object-based recognition

    ISPRS J. Photogram. Remote Sens.

    (2016)
  • J. Xing et al.

    A scale-invariant change detection method for land use/cover change research

    ISPRS J. Photogram. Remote Sens.

    (2018)
  • X. Zhan et al.

    Detection of land cover changes using MODIS 250 m data

    Remote Sens. Environ.

    (2002)
  • Z. Zhu et al.

    Object-based cloud and cloud shadow detection in Landsat imagery

    Remote Sens. Environ.

    (2012)
  • Z. Zhu et al.

    Continuous change detection and classification of land cover using all available landsat data

    Remote Sens. Environ.

    (2014)
  • Agrawal, R., Faloutsos, C., Swami, A., 1993. Efficient similarity search in sequence databases. In: Lomet, D.B. (Eds.),...
  • Banner, A., Lynham, T., 1981. Multitemporal analysis of Landsat data for forest cutover mapping — a trial of two...
  • Box, G.E.P., Jenkins, G.M., 1970. Time Series Analysis: Forecasting and Control. San Francisco: Holden Day. (Revised...
  • E.B. Brooks et al.

    On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data

    IEEE Trans. Geosci. Remote Sens.

    (2014)
  • E.B. Brooks et al.

    Edyn: dynamic signaling of changes to forests using exponentially weighted moving average charts

    Forests

    (2017)
  • S. Cai et al.

    Detecting change dates from dense satellite time series using a sub-annual change detection algorithm

    Remote Sens.

    (2015)
  • J.B. Campbell et al.

    Introduction to Remote Sensing

    (2011)
  • Chan, K.P., Fu, A.W-C., 1999. Efficient time series matching by wavelets. In: Proc. of the 15th IEEE Int. Conference on...
  • G. Chen et al.

    Object-based change detection

    Int. J. Remote Sens.

    (2012)
  • C-S.J. Chu et al.

    Mosum tests for parameter constancy

    Biometrika

    (1995)
  • W.B. Cohen et al.
  • W.B. Cohen et al.

    An efficient and accurate method for mapping forest clear cuts in the Pacific Northwest using Landsat imagery

    Photogram. Eng. Remote Sens.

    (1998)
  • W.B. Cohen et al.

    How similar are forest disturbance maps derived from different landsat time series algorithms

    Forests

    (2017)
  • P.R. Coppin et al.

    Processing of multitemporal Landsat TM imagery to optimise extraction of forest cover change features

    IEEE Trans. Geosci. Remote Sens.

    (1994)
  • P. Coppin et al.

    Digital change detection methods in ecosystems monitoring: a review

    Int. J. Remote Sens.

    (2004)
  • T.G. Dietterich et al.

    Ensemble methods in machine learning

  • D.H. Douglas et al.

    Algorithms for the reduction of the number of points required to represent a digitized line or its carricature

    Canad. Cartograph.

    (1973)
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