Deriving a per-field land use and land cover map in an agricultural mosaic catchment

Abstract

Abstract. Detailed data on land use and land cover constitute important information for Earth system models, environmental monitoring and ecosystem services research. Global land cover products are evolving rapidly; however, there is still a lack of information particularly for heterogeneous agricultural landscapes. We censused land use and land cover field by field in the agricultural mosaic catchment Haean in South Korea. We recorded the land cover types with additional information on agricultural practice. In this paper we introduce the data, their collection and the post-processing protocol. Furthermore, because it is important to quantitatively evaluate available land use and land cover products, we compared our data with the MODIS Land Cover Type product (MCD12Q1). During the studied period, a large portion of dry fields was converted to perennial crops. Compared to our data, the forested area was underrepresented and the agricultural area overrepresented in MCD12Q1. In addition, linear landscape elements such as waterbodies were missing in the MODIS product due to its coarse spatial resolution. The data presented here can be useful for earth science and ecosystem services research. The data are available at the public repository Pangaea

Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – Application to the Venice Lagoon, Italy

“…We used the Maximum Likelihood Classifier Tool in the Spatial Analyst Toolbox in ArcGIS (v10.2) to perform a per-field supervised classification. Similarly to Seo et al (2014), we digitised a set of training fields (parcels) over the ground truth locations (digitisation of field records). Given the spatial homogeneity of the backscatter patches surrounding the ground truth locations we used 1-m² square polygon fields to map all classes except the sponge class, for which circular and irregular fields were used.…”

Evaluating the Effectiveness of Spatially Reconfiguring Erosion Hot Spots to Reduce Stream Sediment Load in an Upland Agricultural Catchment of South Korea

“…Rice paddies (8%) and residential areas (3%) (e.g., roads and artificial structures) occupy the flat central area of the catchment. Semi-natural vegetation field (8%), shrublands (1%), and bare surface (5%), including fallow and barren field, cover the remaining areas [35].…”

“…Vegetation structures contain the permanent interception of rainfall (PI), pervious ground cover (GC), canopy cover (CC), average vegetation height (PH), average diameter of individual plant elements at the surface (D), and number of individual plant elements per unit area (NV). LULC parameters were derived based on the LULC map of the Haean catchment in the year 2010 from Seo et al [35] (see Figure 5). .…”

“…The DMMF model was calibrated and validated for stream discharge and suspended sediment to test its performance in the Haean catchment. The testing was performed utilizing data from the year 2010 when the LULC map, as well as the field-measured stream discharge and suspended sediment data, were well established [30,35]. We confined the testing period from the 67th day of the year (DOY), which is three days after first heavy rainfall of the year, to reduce the uncertainty of initial soil water content by equating it with the soil water content at field capacity.…”

Linking canopy reflectance to crop structure and photosynthesis to capture and interpret spatiotemporal dimensions of per-field photosynthetic productivity

“…However, their use has been limited by poor revisit times, coarse spatial resolution, and/or cloudy weather. They technically conceal delicate fluctuations of ecosystem productivity tightly associated with perfield ecological conditions on which plants survival and dispersal depend (Seo et al, 2014). Applications of spatially coarse satellite products generate considerable spatiotemporal uncertainties in evaluating strength of daily carbon fluxes among microsites of the same plant function type at principle growth stages.…”