A. M. Kinoshita

1.4k total citations
44 papers, 928 citations indexed

About

A. M. Kinoshita is a scholar working on Global and Planetary Change, Water Science and Technology and Ecology. According to data from OpenAlex, A. M. Kinoshita has authored 44 papers receiving a total of 928 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Global and Planetary Change, 15 papers in Water Science and Technology and 12 papers in Ecology. Recurrent topics in A. M. Kinoshita's work include Fire effects on ecosystems (27 papers), Plant Water Relations and Carbon Dynamics (15 papers) and Hydrology and Watershed Management Studies (12 papers). A. M. Kinoshita is often cited by papers focused on Fire effects on ecosystems (27 papers), Plant Water Relations and Carbon Dynamics (15 papers) and Hydrology and Watershed Management Studies (12 papers). A. M. Kinoshita collaborates with scholars based in United States, Mexico and Israel. A. M. Kinoshita's co-authors include T. S. Hogue, Eric D. Stein, Anne Chin, Joan L. Florsheim, Joseph W. Wagenbrenner, Brian A. Ebel, Kevin D. Bladon, Natalie Mladenov, Jeffrey S. Brown and Janet Barco and has published in prestigious journals such as The Science of The Total Environment, Journal of Hydrology and Conservation Biology.

In The Last Decade

A. M. Kinoshita

42 papers receiving 912 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
A. M. Kinoshita United States 17 708 298 221 206 106 44 928
Shanzhong Qi China 17 405 0.6× 167 0.6× 221 1.0× 241 1.2× 171 1.6× 50 854
Dennis W. Hallema United States 15 547 0.8× 177 0.6× 324 1.5× 191 0.9× 116 1.1× 27 812
Zhao Jun China 12 448 0.6× 125 0.4× 203 0.9× 230 1.1× 134 1.3× 40 813
Akinola Adesuji Komolafe Nigeria 15 479 0.7× 80 0.3× 202 0.9× 134 0.7× 130 1.2× 32 741
Zeyin Hu China 17 475 0.7× 121 0.4× 135 0.6× 249 1.2× 253 2.4× 28 968
Friday Uchenna Ochege China 16 428 0.6× 116 0.4× 321 1.5× 106 0.5× 159 1.5× 44 871
Yunrui Ma China 12 543 0.8× 127 0.4× 128 0.6× 243 1.2× 181 1.7× 22 997
Lianqing Xue China 18 706 1.0× 103 0.3× 529 2.4× 264 1.3× 151 1.4× 78 1.2k
Jawad Al‐Bakri Jordan 14 302 0.4× 115 0.4× 142 0.6× 169 0.8× 95 0.9× 48 688
Alphonse Kayiranga China 16 338 0.5× 88 0.3× 231 1.0× 175 0.8× 56 0.5× 33 714

Countries citing papers authored by A. M. Kinoshita

Since Specialization
Citations

This map shows the geographic impact of A. M. Kinoshita's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. M. Kinoshita with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. M. Kinoshita more than expected).

Fields of papers citing papers by A. M. Kinoshita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. M. Kinoshita. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. M. Kinoshita. The network helps show where A. M. Kinoshita may publish in the future.

Co-authorship network of co-authors of A. M. Kinoshita

This figure shows the co-authorship network connecting the top 25 collaborators of A. M. Kinoshita. A scholar is included among the top collaborators of A. M. Kinoshita based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. M. Kinoshita. A. M. Kinoshita is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lee, Christine, Adam Chlus, Hans‐Peter Marshall, et al.. (2024). Computationally Efficient Retrieval of Snow Surface Properties From Spaceborne Imaging Spectroscopy Measurements Through Dimensionality Reduction Using K-Means Clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 8594–8605. 1 indexed citations
2.
Kinoshita, A. M., et al.. (2024). Water yield response to forest treatment patterns in a sierra nevada watershed. Journal of Hydrology Regional Studies. 53. 101762–101762. 3 indexed citations
3.
Kinoshita, A. M., et al.. (2022). Upland and Riparian Surface Soil Processes in an Urban Creek with Native and Non-Native Vegetation after Fire. Fire. 5(2). 32–32. 2 indexed citations
4.
Mladenov, Natalie, et al.. (2022). Chemical and Microbial Markers for Discriminating Sanitary Sewer Contamination in Coastal, Urban Streams. ACS ES&T Water. 2(10). 1747–1759. 6 indexed citations
5.
Ebel, Brian A., Joseph W. Wagenbrenner, A. M. Kinoshita, & Kevin D. Bladon. (2022). Hydrologic recovery after wildfire: A framework of approaches, metrics, criteria, trajectories, and timescales. Journal of Hydrology and Hydromechanics. 70(4). 388–400. 15 indexed citations
6.
Jennings, Megan K., Erin Conlisk, Alexandra D. Syphard, et al.. (2021). A landscape‐scale framework to identify refugia from multiple stressors. Conservation Biology. 36(1). e13834–e13834. 21 indexed citations
8.
Kinoshita, A. M., et al.. (2021). Post-fire Vegetation and Hydrologic Recovery in a Mediterranean Climate. 1 indexed citations
9.
Verbyla, Matthew E., et al.. (2021). An Assessment of Ambient Water Quality and Challenges with Access to Water and Sanitation Services for Individuals Experiencing Homelessness in Riverine Encampments. Environmental Engineering Science. 38(5). 389–401. 19 indexed citations
10.
Lee, Christine, Joshua B. Fisher, Gregory Halverson, et al.. (2020). ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sensing. 12(24). 4126–4126. 14 indexed citations
11.
Kinoshita, A. M., et al.. (2020). Remote sensing of vegetation conditions after post-fire mulch treatments. Journal of Environmental Management. 260. 109993–109993. 13 indexed citations
12.
Lancaster, Jeremy T., et al.. (2020). An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California. Hydrological Processes. 35(1). 24 indexed citations
13.
Kinoshita, A. M., et al.. (2020). Vegetation and Fluvial Geomorphology Dynamics after an Urban Fire. Geosciences. 10(8). 317–317. 5 indexed citations
14.
Hallema, Dennis W., A. M. Kinoshita, Mauricio Galleguillos, et al.. (2019). Fire, forest and city water supplies. 70(251). 58–66. 2 indexed citations
15.
Hallema, Dennis W., A. M. Kinoshita, Deborah A. Martin, et al.. (2019). Fire, forests and city water supply. 70(1). 58–66. 2 indexed citations
16.
Chin, Anne, et al.. (2019). Interacting geomorphic and ecological response of step-pool streams after wildfire. Geological Society of America Bulletin. 131(9-10). 1480–1500. 13 indexed citations
17.
Florsheim, Joan L., et al.. (2017). Effect of storms during drought on post‐wildfire recovery of channel sediment dynamics and habitat in the southern California chaparral, USA. Earth Surface Processes and Landforms. 42(10). 1482–1492. 36 indexed citations
18.
Kinoshita, A. M., et al.. (2014). Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada. Hydrology and earth system sciences. 18(11). 4601–4615. 27 indexed citations
19.
Hogue, T. S., et al.. (2013). Pre- and post-fire pollutant loads in an urban fringe watershed in Southern California. Environmental Monitoring and Assessment. 185(12). 10131–10145. 70 indexed citations
20.
Stein, Eric D., et al.. (2012). Stormwater contaminant loading following southern California wildfires. Environmental Toxicology and Chemistry. 31(11). 2625–2638. 68 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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