Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series.

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.

Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62 °C and 2.85 °C, respectively, while they were acquired 3.57 °C and 2.69 oC from the combination of MLP and NN (NN-MLP) for the 8-day composite LST test set. The results revealed that the proposed hybrid models could be used for one step ahead spatiotemporal prediction of LST data.

Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans.

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research.