Augmented Sample-Based Real-Time Spatiotemporal Spectral Unmixing

Recently, the method of spatiotemporal spectral unmixing (STSU ) was developed to fully explore multi-scale temporal information (e.g., MODIS –Landsat image pairs) for spectral unmixing of coarse time series (e.g., MODIS data). To further enhance the application for timely monitoring, the real-time STSU( RSTSU) method was developed for real-time data. In RSTSU, we usually choose a spatially complete MODIS–Landsat image pair as auxiliary data. Due to cloud contamination, the temporal distance between the required effective auxiliary data and the real-time data to be unmixed can be large, causing great land cover changes and uncertainty in the extracted unchanged pixels (i.e., training samples). In this article, to extract more reliable training samples, we propose choosing the auxiliary MODIS–Landsat data temporally closest to the prediction time. To deal with the cloud contamination in the auxiliary data, we propose an augmented sample-based RSTSU( ARSTSU) method. ARSTSU selects and augments the training samples extracted from the valid (i.e., non-cloud) area to synthesize more training samples, and then trains an effective learning model to predict the proportions. ARSTSU was validated using two MODIS data sets in the experiments. ARSTSU expands the applicability of RSTSU by solving the problem of cloud contamination in temporal neighbors in actual situations.

ANALYSIS OF LAND COVER CHANGES IN MANGROVE VEGETATION USING LANDSAT IMAGE DATA IN COASTAL, SUNGAI APIT DISTRICT, SIAK REGENCY, RIAU PROVINCE

This research was conducted in March-April 2021 in the Coastal District of Sungai Apit, Siak Regency, Riau Province. The purpose of this study was to analyze changes in the land cover area of ​​mangrove vegetation and mangrove vegetation index in Sungai Apit District, Siak Regency, Riau Province. The method used in this study is a survey method with the interpretation of Landsat image data recorded in 2000, 2005, 2010, 2015, 2020. The results of the study obtained that mangrove forests with the highest area were in 2000 with an area of ​​mangrove vegetation reaching 7990,586 ha and there was a decline with the lowest number in 2015 with a vegetation area of ​​486,43 ha and in 2020 the mangrove vegetation area of ​​497,511 ha. Overall as much as 79% of the mangrove forest area has been damaged and changed its function within a period of 20 years. The NDVI value in Sungai Apit District is moderate with a value of 0,3-0,5, the category of meeting with a value of 0,5-0,6, and the very dense category of 0,6-0,8

Changes in the area of ​​Lake Urmia based on Landsat Image

Lake Urmia is an area with dimensions of 130 by 40 km and is one of the salt lakes in the world. This basin is a prominent example of a closed inland basin. Unfortunately, the water level of Lake Urmia has dropped significantly in recent years and has been widely increased in saline lands around. In this research, the study of changes in the area of ​​Lake Urmia based on Landsat image has been studied. The time series of changing the area of ​​Lake Urmia from 1984 to 2016 has a downward trend with a reduction rate of 727 km2. Of course, after 2014, it has increased by several tens of kilometers.

A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.

A Study on The Use of UAV Images to Improve the Separation Accuracy of Agricultural Land Areas

Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images was used to separate pistachio cultivars and separate weed from trees. In order to use the high spatial resolution of UAV images, image fusion was carried out through high-pass filter, wavelet, principal component transformation, BROVEY, IHS and Gram Schmidt methods, and ERGAS, RMSE and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE and ERGAS 0.91, 12.22 cm and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi and a mixing class of Kalleh Ghoochi and Fandoghi were applied and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79 and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and combined image showed that the validation accuracy obtained from harvest has raised by 17% because of combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.