Daniel Mandallaz

1.4k total citations
42 papers, 1.0k citations indexed

About

Daniel Mandallaz is a scholar working on Nature and Landscape Conservation, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Daniel Mandallaz has authored 42 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Nature and Landscape Conservation, 25 papers in Environmental Engineering and 13 papers in Global and Planetary Change. Recurrent topics in Daniel Mandallaz's work include Forest ecology and management (26 papers), Remote Sensing and LiDAR Applications (21 papers) and Soil Geostatistics and Mapping (12 papers). Daniel Mandallaz is often cited by papers focused on Forest ecology and management (26 papers), Remote Sensing and LiDAR Applications (21 papers) and Soil Geostatistics and Mapping (12 papers). Daniel Mandallaz collaborates with scholars based in Switzerland, Canada and United States. Daniel Mandallaz's co-authors include Jochen Mau, Jean-Philippe Schütz, Michael Götz, Willi Schmid, Andreas Hill, Adrian Lanz, Christian Ginzler, Peter Niemz, Steen Magnussen and Peter Bachmann and has published in prestigious journals such as Biometrics, Journal of Pharmaceutical Sciences and Forest Ecology and Management.

In The Last Decade

Daniel Mandallaz

41 papers receiving 950 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Mandallaz Switzerland 18 597 490 382 183 154 42 1.0k
Edwin J. Green United States 13 332 0.6× 189 0.4× 251 0.7× 57 0.3× 64 0.4× 45 600
T. R. Dell United States 9 556 0.9× 277 0.6× 324 0.8× 110 0.6× 118 0.8× 16 1.4k
Oscar Garćıa Canada 18 753 1.3× 288 0.6× 549 1.4× 55 0.3× 52 0.3× 43 924
James P. Barrett United States 9 315 0.5× 173 0.4× 184 0.5× 40 0.2× 23 0.1× 16 528
Benee F. Swindel United States 16 419 0.7× 30 0.1× 310 0.8× 151 0.8× 116 0.8× 49 751
Bo Ranneby Sweden 9 84 0.1× 60 0.1× 134 0.4× 128 0.7× 44 0.3× 23 490
Magnus Ekström Sweden 17 101 0.2× 136 0.3× 182 0.5× 77 0.4× 111 0.7× 55 731
H. Todd Mowrer United States 9 133 0.2× 165 0.3× 167 0.4× 76 0.4× 19 0.1× 17 353
Qing Xu China 15 234 0.4× 410 0.8× 131 0.3× 197 1.1× 75 0.5× 41 633
H. Michael Rauscher United States 15 225 0.4× 98 0.2× 416 1.1× 88 0.5× 60 0.4× 59 714

Countries citing papers authored by Daniel Mandallaz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Mandallaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Mandallaz. 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 Daniel Mandallaz. The network helps show where Daniel Mandallaz may publish in the future.

Co-authorship network of co-authors of Daniel Mandallaz

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Mandallaz. A scholar is included among the top collaborators of Daniel Mandallaz 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 Daniel Mandallaz. Daniel Mandallaz 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.
Hill, Andreas, et al.. (2018). A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels. Remote Sensing. 10(7). 1052–1052. 20 indexed citations
2.
Magnussen, Steen, Daniel Mandallaz, Adrian Lanz, et al.. (2016). Scale effects in survey estimates of proportions and quantiles of per unit area attributes. Forest Ecology and Management. 364. 122–129. 11 indexed citations
3.
Mandallaz, Daniel, et al.. (2016). Design-based properties of some smal-area estimators in forest inventory with two-phase sampling. Canadian Journal of Forest Research. 43(5). 441–449. 17 indexed citations
4.
Mandallaz, Daniel, et al.. (2015). Design-based regression estimation of net change for forest inventories. Canadian Journal of Forest Research. 45(12). 1775–1784. 9 indexed citations
5.
Mandallaz, Daniel, et al.. (2015). Comparison of classical, kernel-based, and nearest neighbors regression estimators using the design-based Monte Carlo approach for two-phase forest inventories. Canadian Journal of Forest Research. 45(11). 1480–1488. 7 indexed citations
6.
Mandallaz, Daniel, et al.. (2014). Integrating remote sensing and past inventory data under the new annual design of the Swiss National Forest Inventory using three-phase design-based regression estimation. Canadian Journal of Forest Research. 44(10). 1177–1186. 30 indexed citations
7.
Hill, Andreas, et al.. (2014). Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models. Forests. 5(9). 2253–2275. 22 indexed citations
8.
Mandallaz, Daniel. (2013). Regression estimators in forest inventories with two-phase sampling and partially exhaustive information with applications to small-area estimation. Repository for Publications and Research Data (ETH Zurich). 3 indexed citations
9.
10.
Jiang, Yueyang, Qianlai Zhuang, & Daniel Mandallaz. (2012). Modeling Large Fire Frequency and Burned Area in Canadian Terrestrial Ecosystems with Poisson Models. Environmental Modeling & Assessment. 17(5). 483–493. 18 indexed citations
11.
Steinmann, Katharina, Daniel Mandallaz, Christian Ginzler, & Adrian Lanz. (2012). Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data. Scandinavian Journal of Forest Research. 28(4). 373–385. 27 indexed citations
12.
Mandallaz, Daniel. (2007). Sampling Techniques for Forest Inventories. 141 indexed citations
13.
Schütz, Jean-Philippe, Michael Götz, Willi Schmid, & Daniel Mandallaz. (2006). Vulnerability of spruce (Picea abies) and beech (Fagus sylvatica) forest stands to storms and consequences for silviculture. European Journal of Forest Research. 125(3). 291–302. 190 indexed citations
14.
Mandallaz, Daniel & Adrian Lanz. (2001). Forest inventory: further results for optimal sampling schemes based on the anticipated variance. Canadian Journal of Forest Research. 31(10). 1845–1853. 10 indexed citations
15.
Mandallaz, Daniel, et al.. (1999). Forest inventory with optimal two-phase two-stage sampling schemes based on the anticipated variance. Canadian Journal of Forest Research. 29(11). 1691–1708. 4 indexed citations
16.
Mandallaz, Daniel, et al.. (1999). Forest inventory with optimal two-phase two-stage sampling schemes based on the anticipated variance. Canadian Journal of Forest Research. 29(11). 1691–1708. 30 indexed citations
17.
Mandallaz, Daniel & Jochen Mau. (1996). COMPARISON OF DIFFERENT METHODS OF DECISION MAKING IN BIOEQUIVALENCE ASSESSMENTS. 42. 213–222. 5 indexed citations
18.
Mandallaz, Daniel, et al.. (1986). Die Vitalität von Weißtannen und ihre Abhängigkeit von bestandesstrukturellen, ertragskundlichen, ernährungskundlichen und waldbaulichen Variablen. Forstwissenschaftliches Centralblatt. 105(1). 406–420. 4 indexed citations
19.
Grieve, Andrew P., et al.. (1983). Bayesian Approach to Bioequivalence Assessment: An Example. Journal of Pharmaceutical Sciences. 72(10). 1178–1181. 37 indexed citations
20.
Mandallaz, Daniel & Jochen Mau. (1981). Comparison of Different Methods for Decision-Making in Bioequivalence Assessment. Biometrics. 37(2). 213–213. 110 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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