Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

Abstract

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

“…The development of a data-integration methodology by adding various other parameters and applying weighting by considering different relationships between parameters is required. In terms of accuracy assessment, a grid-based agreement level method could be adopted to assess the consistency of the result based on similar existing models [65][66][67][68]. This study can be localized using high-resolution data to examine wildfires caused by natural or human actions.…”

Multi-Source Remote Sensing Data Product Analysis: Investigating Anthropogenic and Naturogenic Impacts on Mangroves in Southeast Asia

“…Agreement analysis is used to compare the similarity of various data with the same product level [119]. In this study, agreement analysis was used to compare four mangrove distribution datasets by making four levels of agreement.…”

“…level [119]. In this study, agreement analysis was used to compare four mangrove distribution datasets by making four levels of agreement.…”

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

“…These data cannot be directly compared with other products, which have specific information on cropping intensity per year. Agreement analyses are widely used to compare the similarity to various data, and one of the requirements is that the same product level must have comparable data [71]. Because we compare the cropping intensity of three different products in this agreement analysis, we classify the characteristics and group each product into three levels of agreement.…”