Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery

Time-Series Satellite Imagery Demonstrates the Progressive Failure of a City Master Plan to Control Urbanization in Abuja, Nigeria

Urbanization is a global phenomenon, but its negative effects are most pronounced in developing countries. While much urbanization in the global South is unplanned, there have been occasional attempts at strategic, large-scale urban planning. One example is Abuja, Nigeria, a new city with origins in a 1970s Master Plan. Here, we use multi-temporal remote sensing to investigate four decades of urbanization in Abuja, showing the extent to which urban development has matched original intentions. Seven Landsat images from 1975 to 2014 were selected to correspond with Master Plan milestones and turning points in Nigeria’s socio-political development. Land cover classification and change detection results show built-up land increasing rapidly, from 1,167 ha in 1975 to 18,623 ha in 2014, mostly converted from grassland, often via a pioneer bare soil class. Comparing image analysis against the Master Plan shows that, in the early years, Abuja’s development matched broad planning intentions fairly closely. Later, though, unplanned development proliferated, and the city’s resemblance to the Master Plan diminished progressively. Level of adherence to the Master Plan varied widely according to the system of government. Notably, after long-term military rule was replaced by a democratic government around the turn of the millennium, unplanned development increased sharply

Land Use and Land Cover Changes, and Environment and Risk Evaluation of Dujiangyan City (SW China) Using Remote Sensing and GIS Techniques

Understanding of the Land Use and Land Cover (LULC) change, its transitions and Landscape risk (LR) evaluation in earthquake-affected areas is important for planning and urban sustainability. In the present study, we have considered Dujiangyan City and its Environs (DCEN), a seismic-prone area close to the 2008 Wenchuan earthquake (8.0 Mw) during 2007–2018. Five different multi-temporal data sets for the years 2007, 2008, 2010, 2015, and 2018 were considered for LULC mapping, followed by the maximum likelihood supervised classification technique. The individual LULC maps were further used in four time periods, i.e., 2007–2018, 2008–2018, 2010–2018, and 2015–2018, to evaluate the Land Use and Land Cover Transitions (LULCT) using combined remote sensing and GIS (Geographical Information System). Furthermore, multi-criteria evaluation (MCE) techniques were applied for LR mapping. The results of the LULC change data indicate that built-up, agricultural area, and forest cover are the prime categories that had been changed by the natural and anthropogenic activities. LULCT, along with multi-parameters, are suggested to avoid development in fault-existing areas that are seismically vulnerable for future landscape planning in a sustainable manner

Quantification of annual settlement growth in rural mining areas using machine learning

Studies on annual settlement growth have mainly focused on larger cities or incorporated data rarely available in, or applicable to, sparsely populated areas in sub-Saharan Africa, such as aerial photography or night-time light data. The aim of the present study is to quantify settlement growth in rural communities in Burkina Faso affected by industrial mining, which often experience substantial in-migration. A multi-annual training dataset was created using historic Google Earth imagery. Support vector machine classifiers were fitted on Landsat scenes to produce annual land use classification maps. Post-classification steps included visual quality assessments, majority voting of scenes of the same year and temporal consistency correction. Overall accuracy in the four studied scenes ranged between 58.5% and 95.1%. Arid conditions and limited availability of Google Earth imagery negatively affected classification accuracy. Humid study sites, where training data could be generated in proximity to the areas of interest, showed the highest classification accuracies. Overall, by relying solely on freely and globally available imagery, the proposed methodology is a promising approach for tracking fast-paced population dynamics in rural areas where population data is scarce. With the growing availability of longitudinal high-resolution imagery, including data from the Sentinel satellites, the potential applications of the methodology presented will further increase in the futur

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

Quantifying the Impacts of Land Use, Management and Climate Change on Water Resources in Missouri River Basin

A location-specific evaluation of hydrological landscape responses concerning past and projected climate and land use land cover (LULC) changes can provide a powerful intellectual basis for developing efficient and profitable agroecosystems, and overcoming uncertain and detrimental consequences of LULC and climate shifts. This dissertation assessed the impacts of land use, management, and climate change on water resources in the Missouri River Basin (MRB) through four specific studies that included: (i) to study the responses of leached nutrient concentrations and soil health to winter rye cover crop (CC) under no-till corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation, (ii) to simulate hydrological responses of integrated crop-livestock (ICL) system under projected climate changes in an agricultural watershed, (iii) to evaluate the hydrological landscape responses in relation to past (1986-2018) LULC and climate shifts across South Dakota (SD), and (iv) to evaluate the hydrological landscape responses in relation to past (1986-2018) LULC and climate shifts across MRB. Cover cropping has been promoted for the ecological agricultural intensification, however, the vulnerability of CC establishment and expected soil health and water quality benefits under short and cold growing periods for CC are of concerns among producers in the northern Great Plains (NGP) region. Thus, a field experiment from 2017 to 2020 was conducted to assess the impacts of winter rye (Secale cereale L.) CC on soil health and water quality parameters under a no-till corn-soybean rotation at Southeast Research Farm (SERF), Beresford, SD. Interestingly, the study site faced one dry (2020) and two abnormally wet (2018 and 2019) years which received 31% lower (2020), and 31% (2018) and 23% (2019) higher precipitation, respectively, than the annual average (1953-2019). Data showed that biomass of the rye CC was 251 kg ha-1 in 2018, 1213 kg ha-1 in 2019, and 147 kg ha-1 in 2020, coinciding with contrasting growing degree days i.e., 1458, 2042, 794, respectively, as a consequence of variable weather conditions. Cover cropping did not impact water quality for the majority of the study period. However, a significant reduction in leached nitrate (~19-20%) and total nitrogen (TN) (~8.5-16%) concentrations were found only in 2019, pertaining to sequestered 18.8 kg N ha-1. Rye CC showed 13 and 11% significantly higher microbially active carbon and water-extractable organic nitrogen, respectively, than the control (No CC) treatment. The non-significant impacts on soil health indicators due to winter rye showed that study duration (3 years) may not be sufficient to see the beneficial impacts of cover crop on soils. However, significant reductions in leached nitrate and TN concentrations for one (2019) out of three study years suggest that well-established rye CC (biomass = 1213 kg ha-1; which was 4.8 and 8.3 times higher than that in 2018 and 2020) has the potential of reducing nutrient leaching and enhancing soil health for the study region. The ICL systems, when well managed properly, have beneficial impacts on soils and water yield, however, very limited studies are available due to the complexity of these integrated systems. Thus, a simulation study was conducted to assess the hydrological impacts of long-term implementation of ICL systems at watershed scale with the projected climate scenarios on water yield using the Soil and Water Assessment Tool (SWAT) model over two time periods [i.e. Near Future (2021-2050) and Far Future (2070-2099)]. This study was conducted in three phases over Skunk Creek Watershed (SCW), SD, USA. In phase I, the impact of long-term ICL system implementation (1976- 2005; 30 years) on soil hydrology was evaluated. Phase II and phase III evaluated the impacts of projected climate changes under existing land cover and ICL system, respectively. Outcomes of phase I showed a significant decrease in water yield and surface runoff. Phase II showed the susceptibility of SCW to extreme events such as floods and waterlogging during spring, and droughts during summers under the projected climate changes. Phase III showed the reduction in water yield and surface runoff due to the ICL system and minimizing the induced detrimental impacts only due to climate change. Evapotranspiration (ET) plays a significant role in crop growth and development, therefore, an accurate estimation of ET is very important for water use and availability. The past hydrological landscape responses were studied using well-validated (r2 = 0.91, PBIAS= -4%, and %RMSE = 11.8%) actual evapotranspiration (ETa) time-series (1986- 2018) estimations. The developed ETa products were further used to understand the crop water-use (CWU) characteristics and existing historic mono-directional (increasing or decreasing) trends across the SD and MRB regions. Spatial variability of the Operational Simplified Surface Energy Balance (SSEBop) model- and Landsat-based ETa estimations showed strong correspondence with land cover and climate across the basin. The drier foothills in northwestern MRB, dominated by grassland/shrubland, showed lower ETa (\u3c 400 mm/year), whereas, cropland dominated regions in lower semi-humid MRB and forested headwater exhibited higher ETa (\u3e 500 mm/year). For the SD region, Mann Kendall trend analysis revealed an absence of a significant trend in annual CWU at a regional scale due to the combined impact of varying weather conditions, and the presence of both increasing (12%) and decreasing (9%) CWU trends over a substantial portion at the pixel-scale. Whereas, for the MRB, summer season CWU trend analysis revealed a significant increasing trend at the regional-scale with 30% MRB cropland pixels under a significant increasing trend at pixel-scale. The existing increasing trends can be explained by the shift in agricultural practices, increased irrigated cropland area, higher productions, moisture regime shifts, and decreased risk of farming in the dry areas. Moreover, the decreasing trend pixels could be the result of the dynamic conversion of wetlands to croplands, decreased and improved irrigation and water management practices in the region. Overall, both studies highlight the potential of Landsat imagery and remote sensing-based ETa modeling approaches in generating historical time-series ETa maps over a wide range of elevation, vegetation, and climate

Effects of large fires on boreal forests of China : historical reconstruction and future prediction through landscape modeling

Includes vita.Boreal forests of China store about 350 Tg tree biomass carbon, which is approximately 24–31 [percent] of the total forest carbon storage in China, and thus, play an important role in maintain national carbon balance. Long-term fire exclusion and climate warming have foster larger and more severe fires. On 1987 May 6, a catastrophic fire, known as the Black Dragon Fire, occurred in this region, and burned 1.3 million ha. This fire is among the top five of such megafires ever recorded in the world, resulting in high degree of tree mortality and reset forest succession stage for most burned stands. Forests have grown back since, with much more homogeneous age classes and composition, which post new ecological risks and challenges. It is predicted that the warming will continue in the next century, and thus uncertainties exist in future fire regimes and vegetation response under novel climate. Chapter II estimate the burn severity and carbon emissions from the Black Dragon fire. I combined field and remote sensing data to map four burn severity classes and calculated combustion efficiency in terms of the biomass immediately consumed in the fire. Results of this chapter showed that 1.30 million hectares burned and 52 [percent] of that area burned with high severity. The emitted carbon dioxide equivalents (CO2e), accounted for approximately 10 [percent] of total fossil fuel emissions from China in 1987, along with CO (2 [percent] - 3 [percent] of annual anthropogenic CO emissions from China) and non-methane hydrocarbons (NMHC) contributing to the atmospheric pollutants. This study provides an important basis for carbon emission estimation and understanding the impacts of megafires. Chapter III developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. I derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. I simulated the megafire and the post-megafire forest recovery from 1985-2015 using the LANDIS model. I calibrated the model and validated the simulation results using inventory data. I demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances. Chapter IV investigated the effects of future fire regimes on boreal forests of China under a warming climate. I simulated species composition and distribution changes to the year 2100 using a coupled forest dynamic model (LANDIS PRO) and ecosystem process model (LINKAGES). I focused on two possible fire regimes (frequent small fires and infrequent large fires). Results of this chapter showed that climate warming and fires strongly affected tree species composition and distribution in the boreal forests of China. Climate warming promoted transitions from boreal species to pioneer and temperate species. Fire effects acted in the same direction as climate change effects on species occurrences, thereby catalyzing climate-induced transitions. Frequent small fires exerted stronger effects on the species composition shifts than infrequent large fires. The combined effects of climate warming and fire on the shifts in species composition will accumulate through time and space and can induce a complete transition of forest type, and alter forest dynamics and functions.Includes bibliographical reference

The SWADE model for landslide dating in time series of optical satellite imagery

Landslides are destructive natural hazards that cause substantial loss of life and impact on natural and built environments. Landslide frequencies are important inputs for hazard assessments. However, dating landslides in remote areas is often challenging. We propose a novel landslide dating technique based on Segmented WAvelet-DEnoising and stepwise linear fitting (SWADE), using the Landsat archive (1985–2017). SWADE employs the principle that vegetation is often removed by landsliding in vegetated areas, causing a temporal decrease in normalized difference vegetation index (NDVI). The applicability of SWADE and two previously published methods for landslide dating, harmonic modelling and LandTrendr, are evaluated using 66 known landslides in the Buckinghorse River area, northeastern British Columbia, Canada. SWADE identifies sudden changes of NDVI values in the time series and this may result in one or more probable landslide occurrence dates. The most-probable date range identified by SWADE detects 52% of the landslides within a maximum error of 1 year, and 62% of the landslides within a maximum error of 2 years. Comparatively, these numbers increase to 68% and 80% when including the two most-probable landslide date ranges, respectively. Harmonic modelling detects 79% of the landslides with a maximum error of 1 year, and 82% of the landslides with a maximum error of 2 years, but requires expert judgement and a well-developed seasonal vegetation cycle in contrast to SWADE. LandTrendr, originally developed for mapping deforestation, only detects 42% of landslides within a maximum error of 2 years. SWADE provides a promising fully automatic method for landslide dating, which can contribute to constructing landslide frequency-magnitude distributions in remote areas