Spatial sampling design optimization of monitoring network for terrestrial ecosystem in China

Abstract

The rapid socioeconomic development leads to the deterioration of ecological environment. Ecosystem assessment has been conducted worldwide, e.g. the Millennium Ecosystem Assessment to assess consequences of ecosystem change for human well-being. To enhance ecosystem assessment in China, this study proposes the design of a monitoring network for the terrestrial ecosystem consisting of core stations and localized points. With focus on ecosystem services of NPP, water conservation, soil retention and sandstorm prevention, core stations of the monitoring network for observing all four services are first selected by assessing and improving spatial representativeness in ecoregions of forest, grassland and desert ecosystems. Then a spatial sampling method is applied to choose localized points for observing each specific service. Eventually expert's knowledge is used to make final decisions of added stations and points by utilizing existing networks and considering factors such as topography, spatial coverage. Combining both aforementioned approaches and experts knowledge, 60 core stations and 176 localized points are finally determined for the monitoring network. For the forest ecosystem, 39 core stations are decided with 31 selected from existing networks and eight newly added core stations improve spatial representativeness by 51.58 %, 68.11 % and 75.55 % in Temperate grasslands, Temperate desert and Alpine vegetation in Tibet Plateau respectively. For the grassland and desert ecosystem, 21 core stations are chosen with 18 from existing networks and three newly added core stations improve the representativeness by 21.60 % and 44.88 % in Tibet alpine grassland and Grassland in southern mountain areas respectively. Priorities in the implementation phase should be given to instruments installation for monitoring all four services in core stations from existing networks and setting up new stations in regions where representativeness are significantly improved.

Keywords: Data-driven approach; Forest; Grassland and desert; Spatial representativeness; Spatial sampling method.