Developing a new disturbance index for tracking gradual change of forest ecosystems in the hilly red soil region of southern China using dense Landsat time series

Highlights

• The disturbance sensitive vegetation index (DSVI) is a good indicator for subtle disturbance changes of forest.

• Tracking gradual change of forest ecosystems in the hilly red soil region.

• The evaluation aspects (velocity, disturbance frequency, and variance) selected can effectively analysis of the long-term processes of forest gradual change and evaluate the quality and stability of forest ecosystems .

Abstract

Gradual change is prevalent across the forest landscape and generates long-lasting effects for the landscape surface; thus, tracking long-term gradual change can effectively characterize forest change processes. The objective of this study was to establish a vegetation index for change monitoring so as to determine long-term gradual change processes of forest ecosystems in typical red soil areas. The study area was located in Hengyang City, Hunan Province, China. Landsat images, field survey, and auxiliary data were collected to devise a disturbance sensitive vegetation index (DSVI) as an indicator of forest change. Long-term (1985–2019) forest changes were detected using the LandTrendr algorithm on the Google Earth Engine (GEE), while three evaluation aspects of velocity, frequency, and variance were used to analyze the processes of forest gradual change in red soil regions. Results indicate that the DSVI is a suitable index for forest change detection due to its stronger sensitivity compared to other indexes. Further, it shows excellent change detection ability for different types of gradual changes, such as those caused by drought and significant soil erosion. Furthermore, 97.26% of the forests showed gradual change, and approximately 2/3 of monitored forests showed an increasing growth trend while 1/3 showed a decreasing trend. The dominant (28.33%) forest disturbance frequency indicated instability in red soil regions. Dispersion degree of forest variance was mainly low (48.46%) or medium (28.84%). This research establishes the DSVI as a promising method to track forest gradual change and contributes to a better understanding of gradual change processes of forest landscape over time.

Introduction

Gradual changes are “within-state” changes that occur within the forest landscape and do not involve changes in land cover types (Vogelmann et al., 2012; Vogelmann et al., 2016). These changes mainly relate to diseases and insect pests, vegetation growth and succession, soil erosion, wind, pollution, and climate. Gradual change is not related to the normal phenological cycle; however, if the phenological cycle changes over time, this is also considered a gradual change (Vogelmann et al., 2016). Gradual change generates long-term effects for the landscape surface which can effectively characterize the change trajectory of ground features. Understanding gradual change of the forest landscape is beneficial for the management of forest ecosystems, helping to improve their quality and stability; further, it also aids in the analysis of environmental degradation caused by forest water and soil loss.

Red soil is mainly distributed in the low mountains and hills to the south of the Yangtze River in China (Liang et al., 2010). Due to the interference of natural and human factors, this red soil region has become one of the two major areas of soil erosion and its severity is second only to that of the Loess Plateau (Zhao, 1995). Soil erosion affects forest ecosystems by making them sensitive to disturbance. As disturbance is the main driver of forest ecosystem changes (Cohen et al., 2016); the forest landscape within the red soil region has undergone significant change, and due to long-term changes of various degrees, the forest ecosystem in the hilly red soil region has become vulnerable (Li et al., 2011).

From the perspective of mapping and monitoring the landscape changes, scientists and forest managers have classified the forest landscape changes into three types: abrupt change, seasonal change, and gradual change (Verbesselt et al., 2010; Vogelmann et al., 2012; Vogelmann et al., 2016). Abrupt changes are associated with changes in land cover types, typically caused by deforestation, urbanization, fire, agricultural expansion, and flooding (Hansen and Loveland, 2012; Vogelmann et al., 2016). These changes are relatively simple to detect using current remote sensing technology. Seasonal changes are related to the phenological period of vegetation, which includes the response to the sun angle and climate characteristics (Vogelmann et al., 2016), generally showing periodical change. Gradual change occurs within vegetation communities, and commonly occurs throughout the forest landscape in the same manner as seasonal change; however, gradual change is typically ignored (Vogelmann et al., 2016). This is problematic because, similar to abrupt change events and seasonal change events, the cumulative effects of forest gradual change have substantial impacts on ecosystem processes (Lovett et al., 2006; Vogelmann et al., 2012). At present, most research focuses on abrupt changes rather than gradual change (Beck et al., 2007; Kennedy et al., 2007; Kennedy et al., 2010; Mcdonald et al., 2007; Meddens et al., 2013; Schwantes et al., 2016; Senf et al., 2015; Thessler et al., 2005; Vogelmann et al., 2009; Vogelmann et al., 2012; Vogelmann et al., 2016; Zhu et al., 2016); therefore, more attention should be paid to the gradual change of forests in red soil areas.

Choosing a suitable algorithm is a critical step for gradual change detection. In recent decades, many Landsat time-series change detection algorithms have been proposed and applied for forest gradual change detection (Lucorresponding et al., 2004). For example, Trajectory-Based Change Detection (TBCD) algorithm can be applied to capture gradual forest change trends as well as forest recovery rates (Kennedy et al., 2007), and a method of analyzing the linear regression of trends is used to describe the characteristics of gradual forest changes (Vogelmann et al., 2009; Vogelmann et al., 2012). Continuous Change Detection and Classification (CCDC) algorithm is used to detect wetland changes, drought, and pine beetle outbreaks (Vogelmann et al., 2016; Zhu et al., 2012), while Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm has been used to detect drought-, pests-induced forest change, and forest biomass gradual change (Kennedy et al., 2010; Meigs et al., 2011; Pflugmacher et al., 2014; Schwantes et al., 2016). All these change detection algorithms have certain advantages and shortcomings regarding their application in forest gradual change detection. As the study area was located in southern China, sufficient aerial imagery could not be obtained due to cloud cover. Therefore, the LandTrendr algorithm was selected for this study, which can detect forest changes in areas affected by cloud cover with annual imagery, and has been applied using Google Earth Engine (GEE) (Kennedy et al., 2018). The GEE platform can completely access Landsat files and features parallel processing which can enhance the calculation speed of LandTrendr (Gorelick et al., 2017).

There are several challenges for detecting and monitoring forest gradual change, including atmospheric impacts, change index construction, and temporal image gaps (Vogelmann et al., 2009; Vogelmann et al., 2016). It is especially difficult to select the appropriate indicator for detecting forest gradual changes. Therefore, it was necessary to develop an effective disturbance sensitive vegetation index (DSVI) for monitoring forest gradual change. Other vegetation indices have been applied to detect forest gradual change, such as the normalized difference vegetation index (NDVI) (Beck et al., 2007; Vogelmann et al., 2012), enhanced vegetation index (EVI) (Zhu et al., 2016), short wave infrared/near infrared index (SWIR/NIR) (Vogelmann et al., 2009; Vogelmann et al., 2012), and the normalized burn ratio (NBR) (Senf et al., 2015). However, NDVI values are easily saturated and have low sensitivity to subtle changes in high vegetation areas (Li et al., 2007), while EVI increases sensitivity to high biomass areas and does not easily reach saturation, but is not sensitive to subtle disturbances (Huete et al., 2002; Li et al., 2007; Wang et al., 2006). Furthermore, SWIR/NIR is better suited for forest damage monitoring with low leaf area index and is not sensitive to disturbance changes with high leaf area index (Wu et al., 1997), while NBR is only sensitive to changes in the forest before and after a fire (García and Caselles, 1991; Key and Benson, 2004). These indices may not be sensitive enough to detect subtle disturbance changes of forest gradual change. Therefore, our overall goal was to propose a new vegetation index (i.e., DSVI) that can sufficiently detect subtle disturbances in order to analyze the gradual change of the forest in red soil regions. Using the LandTrendr algorithm on the GEE platform and selected evaluation aspects, we present analysis of the long-term processes of forest gradual change by ArcMap 10.2, MATLAB and Microsoft Excel, and evaluate the quality and stability of forest ecosystems in the red soil region of southern China.