Maximo Larry Lopez Caceres

516 total citations
28 papers, 381 citations indexed

About

Maximo Larry Lopez Caceres is a scholar working on Environmental Engineering, Ecology and Atmospheric Science. According to data from OpenAlex, Maximo Larry Lopez Caceres has authored 28 papers receiving a total of 381 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Environmental Engineering, 12 papers in Ecology and 11 papers in Atmospheric Science. Recurrent topics in Maximo Larry Lopez Caceres's work include Remote Sensing and LiDAR Applications (12 papers), Remote Sensing in Agriculture (8 papers) and Plant Water Relations and Carbon Dynamics (6 papers). Maximo Larry Lopez Caceres is often cited by papers focused on Remote Sensing and LiDAR Applications (12 papers), Remote Sensing in Agriculture (8 papers) and Plant Water Relations and Carbon Dynamics (6 papers). Maximo Larry Lopez Caceres collaborates with scholars based in Japan, Italy and Germany. Maximo Larry Lopez Caceres's co-authors include Yago Díez, Sarah Kentsch, Mariano Cabezas, Motohisa Fukuda, Daniel Serrano, Toshiro Yamanaka, Shu Hase, Chitoshi Mizota, Akira Oikawa and Benjamin Burkhard and has published in prestigious journals such as Sensors, Remote Sensing and Hydrological Processes.

In The Last Decade

Maximo Larry Lopez Caceres

26 papers receiving 377 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maximo Larry Lopez Caceres Japan 11 187 163 118 70 64 28 381
Lili Lin China 11 281 1.5× 243 1.5× 149 1.3× 52 0.7× 45 0.7× 40 467
Zhenbang Hao China 12 284 1.5× 291 1.8× 140 1.2× 79 1.1× 44 0.7× 45 520
Marcos Benedito Schimalski Brazil 10 289 1.5× 353 2.2× 126 1.1× 66 0.9× 44 0.7× 36 555
Jonathon J. Donager United States 6 304 1.6× 221 1.4× 135 1.1× 28 0.4× 49 0.8× 7 465
Gabriela Takahashi Miyoshi Brazil 10 322 1.7× 341 2.1× 85 0.7× 83 1.2× 30 0.5× 20 550
Jason McVay United States 8 340 1.8× 336 2.1× 244 2.1× 48 0.7× 91 1.4× 8 687
Laura Elena Cué La Rosa Brazil 12 331 1.8× 421 2.6× 133 1.1× 114 1.6× 73 1.1× 29 650
Christian Knoth Germany 7 165 0.9× 206 1.3× 81 0.7× 46 0.7× 26 0.4× 12 345
Haotian You China 16 267 1.4× 359 2.2× 321 2.7× 38 0.5× 86 1.3× 50 613
Jasmine Muir Australia 11 297 1.6× 276 1.7× 123 1.0× 73 1.0× 35 0.5× 15 497

Countries citing papers authored by Maximo Larry Lopez Caceres

Since Specialization
Citations

This map shows the geographic impact of Maximo Larry Lopez Caceres'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 Maximo Larry Lopez Caceres with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximo Larry Lopez Caceres more than expected).

Fields of papers citing papers by Maximo Larry Lopez Caceres

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maximo Larry Lopez Caceres. 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 Maximo Larry Lopez Caceres. The network helps show where Maximo Larry Lopez Caceres may publish in the future.

Co-authorship network of co-authors of Maximo Larry Lopez Caceres

This figure shows the co-authorship network connecting the top 25 collaborators of Maximo Larry Lopez Caceres. A scholar is included among the top collaborators of Maximo Larry Lopez Caceres 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 Maximo Larry Lopez Caceres. Maximo Larry Lopez Caceres 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.
Caceres, Maximo Larry Lopez, et al.. (2024). Using UAV RGB Images for Assessing Tree Species Diversity in Elevation Gradient of Zao Mountains. Remote Sensing. 16(20). 3831–3831. 1 indexed citations
2.
Castaño, Carles, et al.. (2024). Contrasting fungal functional groups influence nutrient cycling across four Japanese cool-temperate forest soils. Applied Soil Ecology. 198. 105360–105360. 1 indexed citations
3.
Díez, Yago, et al.. (2024). Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning. Remote Sensing. 16(19). 3654–3654. 1 indexed citations
4.
Caceres, Maximo Larry Lopez, et al.. (2024). Influence of Slope Aspect and Vegetation on the Soil Moisture Response to Snowmelt in the German Alps. Hydrology. 11(7). 101–101. 3 indexed citations
5.
Díez, Yago, et al.. (2022). Treetop Detection in Mountainous Forests Using UAV Terrain Awareness Function. Computation. 10(6). 90–90. 4 indexed citations
6.
Ferracini, Chiara, et al.. (2022). Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images. Computation. 10(4). 63–63. 9 indexed citations
7.
8.
Caceres, Maximo Larry Lopez, et al.. (2021). Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. Remote Sensing. 13(2). 260–260. 43 indexed citations
9.
Kentsch, Sarah, et al.. (2021). Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors. 21(2). 471–471. 16 indexed citations
10.
Díez, Yago, et al.. (2021). Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review. Remote Sensing. 13(14). 2837–2837. 109 indexed citations
11.
12.
Caceres, Maximo Larry Lopez, et al.. (2020). Soil temperature and soil moisture dynamics in winter and spring under heavy snowfall conditions in North‐Eastern Japan. Hydrological Processes. 34(15). 3235–3251. 10 indexed citations
13.
Kentsch, Sarah, et al.. (2020). Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study. Remote Sensing. 12(8). 1287–1287. 57 indexed citations
14.
Caceres, Maximo Larry Lopez, et al.. (2019). Using Water Stable Isotopes to Trace Water Sources of Three Typical Japanese Tree Species under Heavy Rainfall Conditions. Open Journal of Forestry. 10(1). 7–21. 3 indexed citations
15.
Caceres, Maximo Larry Lopez, et al.. (2019). N Isotope Fractionation in Tree Tissues During N Reabsorption and Remobilization in Fagus crenata Blume. Forests. 10(4). 330–330. 11 indexed citations
16.
Caceres, Maximo Larry Lopez, et al.. (2019). Nitrogen resorption and fractionation during leaf senescence in typical tree species in Japan. Journal of Forestry Research. 31(6). 2053–2062. 12 indexed citations
17.
Caceres, Maximo Larry Lopez, Fumiaki Takakai, Go Iwahana, et al.. (2015). Snowmelt and the hydrological interaction of forest-grassland ecosystems in Central Yakutia, eastern Siberia. Hydrological Processes. 29(14). 3074–3083. 10 indexed citations
18.
Caceres, Maximo Larry Lopez, et al.. (2012). Method for estimation of stem carbon fixation of Japanese black pine by combining stem analysis and soft X-ray densitometry. Journal of Forest Research. 19(1). 226–232. 8 indexed citations
19.
Mizota, Chitoshi, et al.. (2011). Differential response of twoPinusspp. to avian nitrogen input as revealed by nitrogen isotope analysis for tree rings. Isotopes in Environmental and Health Studies. 47(1). 62–70. 12 indexed citations
20.
Caceres, Maximo Larry Lopez, et al.. (2011). Effects of pre‐treatment on the nitrogen isotope composition of Japanese black pine ( Pinus thunbergii ) tree‐rings as affected by high N input. Rapid Communications in Mass Spectrometry. 25(21). 3298–3302. 11 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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