Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

Abstract

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

“…This understanding allows LULC characteristics and their possible effects on the environment, natural resources, and landscape creation to be forecast [49]. The CA-Markov model, which takes advantage of the Markov model's nearness and the benefits of CA, has been demonstrated to be a successful tool for simulating changes in land use in previous research [50][51][52]. We can determine the prospective spatial distribution of transitions using this model [22,53].…”

Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example

“…As mentioned, this study involves a variety of data, including satellite remote sensing and terrain data, socioeconomic data, geographic information data, planning restriction data, and field investigation data, as shown in Table 1. To choose remote sensing data, two aspects must be taken into account; that is, one is cloud-free [9,54,55], and the other is the continuity of the sensor in operation [56,57]. We (2) Terrain data The terrain data are the digital elevation model (DEM), and in total, five tiles of ASTER GDEM V3 data (30 m resolution) were acquired from NASA (National Aeronautics and Space Administration, Washington, DC, USA).…”