The following paper about future precipitation extremes in China under CMIP6 has been recently accepted for publication by iScience.
Guo, J., Y. Shen, X. Wang, X. Liang, Z. Liu, and L. Liu. Evaluation and Projection of Precipitation Extremes under 1.5°C and 2.0°C GWLs over China using Bias-corrected CMIP6 models. iScience, accepted on February 7, 2023.
More details will come soon once the paper is published.
The following paper about the ecological perturbation of Mangla Watershed in Pakistan has been recently accepted for publication by Water.
Rahim, A., X. Wang, N. Javed, F. Aziz, A. Jahangir, and T. Khurshid. The Ecological Perturbation of Mangla Watershed in Pakistan Due to Hydrological Alteration. Water, accepted on February 3, 2023.
More details will come soon once the paper is published.
Title: Tracking Deforestation, Drought, and Fire Occurrence in Kutai National Park, Indonesia
Journal: Remote Sensing
DOI: https://doi.org/10.3390/rs14225630
Abstract: The dry lowland and mangrove forests of Kutai National Park (KNP) in Indonesia provide invaluable ecosystem services to local human populations (>200,000 in number), serve as immense carbon sinks to recapture anthropogenic emissions, and safeguard habitats for thousands of wildlife species including the critically endangered Northeast Bornean orangutan (Pongo pygmaeus morio). With recent reports of ongoing illegal logging and large–scale wildfires within this National Park, we sought to leverage the extensive catalogue and processing power of Google Earth Engine to track the rates and influences of forest loss within KNP over various time periods since 1997. We present estimates of forest loss from the Hansen Global Forest Change v1.9 dataset (2000–2021) which detected a loss of 15% (272 km2) of forest cover within KNP since 2000, half of which (137 km2) coincided with the El Niño–induced wildfires of 2015–2016. Using the MCD64A1 C6.1 MODIS dataset, we found significant spatial overlap between burned area and forest loss detections during the 2015–2016 period but identified considerable omissions in the burned area dataset over smallholder farms within KNP. We discuss the implications of deforestation in areas of primary orangutan habitat and how patterns of forest loss have influenced drought and fire dynamics within KNP. Finally, we compare time–series estimates of precipitation, the ENSO index, burned area, and forest loss to demonstrate that fire risk within KNP depends largely—but not exclusively—on drought severity, and that rates of non–fire (gradual) and fire–related (extreme) forest loss threaten the remaining forests of this National Park.
The following paper about the deforestation, drought, and fire occurrence in Kutai National
Park, Indonesia has been recently accepted for publication by Remote Sensing.
Guild, R., X. Wang, and A. Russon. Tracking Deforestation, Drought, and Fire Occurrence in Kutai National Park, Indonesia. Remote Sensing, accepted on November 5, 2022.
More details will come soon once the paper is published.
Title: Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques
Journal: Remote Sensing
DOI: https://doi.org/10.3390/rs14194978
Abstract: Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.