A machine-learning approach to forecasting remotely sensed vegetation health

Nay, John J.; Burchfield, Emily; Gilligan, Jonathan M.

doi:10.1080/01431161.2017.1410296

Cited by 38 publications

(19 citation statements)

References 33 publications

(32 reference statements)

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“…Machine learning, as a powerful modeling tool, has successfully improved the estimation and classification accuracy of environmental variables (air pollution, vegetation health condition, soil moisture, land surface temperature, etc.) and land cover types from remotely sensed images [26][27][28][29][30][31][32]. In addition, machine learning algorithms are excellent in solving nonlinear problems of variables with very high dimensions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

et al. 2018

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Hyperspectral sensors provide detailed information for dust retention content (DRC) estimation. However, rich hyperspectral data are not fully utilized by traditional image analysis techniques. We integrated several recently developed machine learning algorithms to estimate DRC on plant leaves using the spectra measured by the ASD FieldSpec 3. The experiments were carried out on three common green plants of southern China. The important hyperspectral variables were first identified by applying the random forest (RF) algorithm. Three estimation models were then developed using the support vector machine (SVM), classification and regression tree (CART), and RF algorithms. The results showed that the increase in dust retention contents on plant leaves enhanced their reflectance in the visible wavelength but weakened their reflectance in the infrared wavelength. Wavelengths in the ranges of 450–500 nm, 550–600 nm, 750–1000 nm, and 1100–1300 nm were identified as important variables using the RF algorithm and were used to estimate the DRC. The comparison of the three machine learning techniques for DRC estimation confirmed that the SVM and RF models performed well because their estimations were similar to the measured DRC. Specifically, the average R2 for SVM and RF model are 0.85 and 0.88. The technical approach of this study proved to be a successful illustration of using hyperspectral measurements to estimate the DRC on plant leaves. The findings of this study can be applied to monitor the DRC on leaves of other plants and can also be integrated with other types of spectral data to measure the DRC at a regional scale.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

et al. 2018

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“…This index allows a better monitoring of vegetation through the reduction of canopy substrate and the influence of the atmosphere (Huete et al, 1997). According to Nay et al (2017) recorded in EVI in tropical Sri Lanka in California, values between 0.5 and 0.8 indicating healthy vegetation, and low EVI values are suggestive of stressed vegetation or atmospheric noise.…”

Section: Analysis Of Vegetation Indicesmentioning

confidence: 99%

Change in Biophysical Parameters from the Creation of a Natural Monument in Mato Grosso

Souza

Danelichen²,

Biudes

et al. 2019

Rev. Bras. Geog. Fis.

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Among the categories of conservation units, the Natural Monument (MoNa) presents a high challenge because it is established areas. MoNa Morro de Santo Antônio is the only one in the state of Mato Grosso and degree of preservation is unknown. Thus, the objective of this work was to evaluate the use and occupation of the soil after the creation of MoNa Morro de Santo Antônio in the state of Mato Grosso and its influence on biophysical parameters from remote sensing techniques. For this, images of the surface reflectance obtained by Landsat 5 and 8 were used. The vegetation classification in the MoNa Morro de Santo Antônio was supervised. The estimated biophysical parameters were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Normalized Difference Moisture Index (NDMI). The results presented in this work show that after the creation of MoNa Morro de Santo Antônio the native Cerrado class increased by 3.38%, the Undergrowth vegetation increased by 5.59% and the Forest dense class increased from 2.21% between 2006 and 2015. The Undergrowth vegetation class had a greater predominance. All indexes were lower in 2006 and higher in 2015. The results of this study are preliminary, but indicate that the creation of MoNa Morro de Santo Antônio in Mato Grosso influenced the preservation of its vegetation.

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“…Recently, the use of machine learning techniques, including the support vector machine (SVM), classification and regression tree (CART), random forest (RF), k-nearest neighbors (k-NN) and linear discriminant analysis (LDA) techniques for classifying forest characteristics have been gaining popularity. These techniques have been widely used in remote sensing for species classification [46,47], vegetation health assessment [48][49][50][51], biomass mapping [52][53][54], wetland mapping [55][56][57] and landslide risk evaluation [58,59]. He et al [40] also used RF in a hierarchical approach in order to classify tree species.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

2020

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Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

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A machine-learning approach to forecasting remotely sensed vegetation health

Cited by 38 publications

References 33 publications

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

Change in Biophysical Parameters from the Creation of a Natural Monument in Mato Grosso

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

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