National-scale cropland mapping based on spectral-temporal features and outdated land cover information

Abstract

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring.

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

“…Cropland abandonment can also be associated with both negative (degradation of vegetation) and positive (vegetation recovery) vegetation trends [41,42]. Thus, despite the general suitability of "global" methods for land cover mapping [43,44], they have less accurate performances than locally calibrated models for land cover mapping [45][46][47][48] and capturing accurately all diverging trajectories of land-cover change. Similarly, crop rotation practices may differ from country to country, reflecting different land-use policies, regional food security programs and market conditions.…”

Definition of candidate Essential Variables for the monitoring of mineral resource exploitation

“…For regions where agricultural production represents the main economic activity, an increase in land degradation can affect the communities' ability to sustain themselves. The indicator is calculated from the mined surface and the area covered by crops and represents the percentage (%) of crop surface lost following the installation of the mine (Waldner et al 2017). The indicator could be expressed as surface of crop lost % ð Þ ¼ mining activity area total surface of the crop  100%…”

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

“…Maxwell et al, [17] demonstrated an effective corn classification from Landsat images through an automated process in south-central Nebraska of USA, and Yin et al, [18] used dense Landsat time series data to map agricultural and land abandonment with a high level of accuracy in the Caucasus, covering parts of Russia and Georgia. Many of these studies either employed supervised or unsupervised classification to ascertain the needed land-cover information [19,20], either at the pixel or object-based levels. Despite their accurate performances, they have some limitations [21,22].…”