Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest
By Niti B. Mishra, Kelley A. Crews. 2014
114 citations recorded by the source corpus.
Subfields: Ecology, Forestry, Global and Planetary Change, Remote Sensing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Jing Yao
- Swalpa Kumar Roy
- Danfeng Hong
- Jocelyn Chanussot
- Lei Guo
- Nobuyuki Kobayashi
- Gong Cheng
- Hiroshi Tani
In Global and Planetary Change
In Remote Sensing
Citations in the corpus
- Discrimination of crop types with TerraSAR-X-derived information (2015, methodology): “Some optical satellites such as the Landsat have also been used for crop 8 type classification (Hartfield et al. 2013; Mishra and Crews 2014).”
- Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “have been undertaken driven by its real-world applications, such as urban planning [5], [6], natural hazards detection [7]–[9], environment monitoring [10]–[12], vegetation mapping [13], [14], and geospatial object detection [15]–[22].”
- Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “and land cover [29]–[32], environment monitoring and natural hazards detection [33], [34], vegetation mapping [35], [36] and urban planning.”
- A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…Kim et al., 2011; Leon and Woodroffe, 2011; Li et al., 2014; Li and Shao, 2013; Lisita et al., 2013; Macfaden et al., 2012; Mallinis et al., 2008; Mishra and Crews, 2014; Moskal et al., 2011; Myint et al., 2011; Phinn et al., 2012; Tzotsos et al., 2011; Walker and Blaschke, 2008; Walker and…”