Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data

Abstract

Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984-2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area-Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions.

A Model for Estimating the Vegetation Cover in the High-Altitude Wetlands of the Andes (HAWA)

“…In the context of this research, NDVI has been used to detect vegetation cover in wetlands and determine the extent of wetland areas [14]. Different authors have highlighted that NDVI is one of the most important indexes for evaluating the state of vegetation, because it is correlated with photosynthesis and primary production [20][21][22].…”

“…Today, there are many methods for monitoring the state of conservation of the wetlands through remote sensing [5,23]. Remote sensing allows evaluation of land use changes and variations in vegetation cover [5,20,23,24] and changes in the wetland ecosystems because of hydrological variations [23,25]. Multitemporal analysis of satellite images is usually used to identify and estimate the changes in the surface of the cover with vegetation [5,23].…”

“…Multitemporal analysis of satellite images is usually used to identify and estimate the changes in the surface of the cover with vegetation [5,23]. Vegetation and land use change in wetland areas may also be due to abrupt changes produced by floods, fires, or human interventions [20,26]. Studies, such as those of Li et al [20], have evaluated the dynamics and loss of marshes and meadows vegetation in wetland due to hydrological variations using NDVI data derived from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) sensors, and generated accuracies up to 91%.…”

Editorial for the Special Issue “Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?”

“…Studies using long-term remote sensing observations highlight their importance in monitoring coastal zones [11,12]. De Sanjosé Blasco et al [11] monitor the long-term (1875-2017) retreat of coastal sandy systems along the Cantabrian Coast (Spain) using geomatics techniques.…”

“…De Sanjosé Blasco et al [11] monitor the long-term (1875-2017) retreat of coastal sandy systems along the Cantabrian Coast (Spain) using geomatics techniques. In turn, Li et al [12] examine land cover and greenness dynamics in Hangzhou Bay based on 30 years (1985-2016) of Landsat satellite data.…”