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.

The following paper about the comparison of LULC classification methods has been recently accepted for publication by Remote Sensing.

Basheer, S., X. Wang, A. Farooque, R.A. Nawaz, K. Liu, T. Adekanmbi, and S. Liu. Comparison of Land Use Land Cover Classifiers Using Different
Satellite Imagery and Machine Learning Techniques. Remote Sensing, accepted on October 3, 2022.

More details will come soon once the paper is published.

Title: A mixed-level factorial inference approach for ensemble long-term hydrological projections over the Jing River Basin

Journal: Journal of Hydrometeorology

DOI: https://doi.org/10.1175/JHM-D-21-0158.1

Abstract: Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model, emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multi-scale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River Basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2 °C and 5.2 °C, which are much higher than the increases under RCP4.5. The maximum increase of the CARM-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 10³ m³/s in November, respectively. In addition, in a multimodel GCM-RCM-HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.