Modeling tree canopy height using machine learning over mixed vegetation landscapes
27
TL;DR: In this paper, the authors explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance by combining Light Detection and Ranging (LiDAR) and Landsat datasets, and used spatially-weighted geographical random forests (GRF) and traditional random forest (TRF) methods to predict tree canopy in a mixed dry forest woodland in complex mountainous terrain.
read more
About: This article is published in International Journal of Applied Earth Observation and Geoinformation. The article was published on 01 Sep 2021. and is currently open access. The article focuses on the topics: Tree canopy & Canopy.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests
TL;DR: In this paper , an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data is presented.
Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network
TL;DR: Wang et al. as mentioned in this paper designed a Chl-a inversion model based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1.
Deep learning approaches and interventions for futuristic engineering in agriculture
Subir Kumar Chakraborty,Narendra Chandel,Dilip Jat,Mukesh K. Tiwari,Yogesh Anand Rajwade,A. Subeesh +5 more
TL;DR: This study demonstrates the discriminative and predictive power of state-of-the-art deep learning approaches that have been successfully applied to the various facets of engineering in agriculture; ranging from estimation of soil moisture, water stress determination, disease detection, weed identification, agro-produce quality evaluation and more.
31
Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models
Rajiv Gupta,Laxmi Kant Sharma +1 more
TL;DR: In this paper , a continuous coverage of multi-spectral optical and synthetic aperture radar (SAR) along with sparsely global ecosystem dynamics investigation (GEDI) spaceborne LiDAR data in the machine learning (ML) models for mapping Hcanopy in the mixed tropical forests of Shoolpaneshwar wildlife sanctuary (SWLS), Gujarat, India.
25
Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data
TL;DR: In this paper , the authors used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which was trained with airborne lidar data.
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
•Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Red and photographic infrared linear combinations for monitoring vegetation
TL;DR: In this article, the relationship between various linear combinations of red and photographic infrared radiances and vegetation parameters is investigated, showing that red-IR combinations to be more significant than green-red combinations.
10.5K
A soil-adjusted vegetation index (SAVI)
TL;DR: In this article, a transformation technique was presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths, which nearly eliminated soil-induced variations in vegetation indices.
6.8K
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
TL;DR: Locally weighted regression as discussed by the authors is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series.
5.7K