Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments

Land cover classification maps of Mongolia from 2001 to 2020

The broad importance of land use and land cover information has been defined by and confirmed for many applications. Therefore, many land cover products have been developed at various scales (i.e., spatial resolution) and extensions (i.e., local, national, region, and global). Several studies have reported inconsistencies among global land cover (GLC) products causing that the accuracy of these products differ between regions. Recently, this issue has received a new level of attention, because many studies have pointed out that the inaccuracy of land cover products at regional scale can make a huge impact on the results of other applications relying on the GLC products (in the following downstream applications). Therefore, developing a method which can be easily and quickly applied to many different regions, but produce highly accurate land cover information is of utmost importance. To meet the first two criteria, several studies successfully used existing GLC products to automatically generate samples. However, none of these studies have been focused on the quality of the samples, which directly and largely affect the classification results. In this context, and taking Mongolia as a case study, we proposed a simple, fast, and accurate method to produce annual land cover maps with 250 m spatial resolution for entire Mongolia over a period of 20 years, from 2001 to 2020. The maps are based on MODIS data (products MOD13Q1 and MCD12Q1, version 6) and produced using modern machine learning techniques (the Random Forest) on the Google Earth Engine. Training samples have been selected by developing a semi-random approach which ensures that samples are spatially well-distributed, the number of samples for each class is in a similar order irrespective of the dominance of the land-cover classes and the samples are sufficiently apart from each other to reduce spatial autocorrelation. It is worth noting that we have selected Mongolia because of the low accuracy of GLC in this vast and remote country. Our results show that the accuracy of the new land cover maps improved compared with the corresponding MODIS products and if visually compared to Landsat images acquired at the same time. Overall accuracy from the validation data was approximately 90% for all new maps compared to 75% for the existing MODIS product. The result suggests that land cover maps, particularly for vegetation downstream application studies, can be largely improved based on the MODIS land cover products both regarding their spatial resolution and accuracy. Regarding Mongolia, these land cover maps are valuable e.g., for land degradation research, such as grassland monitoring, changes in forest cover, and monitoring desertification. Especially, information on grassland ecosystems is of utmost importance for Mongolia since more than half of the country economically depends on grassland resources. Therefore, Mongolia will benefit from the new dataset, not only economically, but also scientifically by helping researchers to discover more about the natural and social conditions of Mongolia. TIF file description: The land cover maps have 6 classes (land cover types), Water, Forest, Shrub land, Grassland, Others, and Bare land. Please note that water was masked out using the “JRC Global Surface Water Mapping Layers” (Pekel et al. 2016). LUC = Land use cover Spatial coverage: Mongolia (41.55709 - 52.17325 °N; 87.72279 - 120.04456°E

A simple, fast, and accurate method for land cover mapping in Mongolia

Low accuracy of global land cover (LC) products at local and regional scales is concerning, because of its huge impact on downstream applications. Therefore, developing a method for high accuracy LC maps at local and regional scale is of utmost importance. Taking Mongolia as a case study, we proposed a simple, fast, and accurate method to produce annual LC maps with 250 m spatial resolution from 2001 to 2020 using MODIS data. Our products have higher spatial resolution and higher accuracy (Overall Accuracy ∼90%) compared to MCD12Q1. These new maps are critical e.g., for land degradation research, and desertification/forest cover monitoring. Especially, information on grassland ecosystems is of utmost importance for Mongolia since more than half of the country economically depends on grassland resources. Therefore, Mongolia will benefit from the new dataset. Furthermore, due to the simplicity of the method, it can be easily applied and transferred to other regions

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

Grassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large-scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi-scale and multi-temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area-wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel-derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients