Development of Desertification Indicators for Desertification Monitoring from Landsat Images Using Python Programming

Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000–2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

MOVING OBJECT DETECTION FROM VIDEO CAPTURED BY MOVING SURVEILLANCE CAMERA

This paper presents an effective method for the detection of multiple moving objects from a video sequence captured by a moving surveillance camera. Moving object detection from a moving camera is difficult since camera motion and object motion are mixed. In the proposed method, we created a panoramic picture from a moving camera. After that, with each frame captured from this camera, we used the template matching method to found its place in the panoramic picture. Finally, using the image differencing method, we found out moving objects. Experimental results have shown that the proposed method had good performance with more than 80% of true detection rate on average.

Spatial Expansion of Urban Activities and Agricultural Lands Encroachment in Makurdi Metropolis

This study assessed spatial expansion of urban activities and agricultural lands in Makurdi metropolis, Benue State-Nigeria between the period of 1999 and 2012. The study employed ILWIS Academic 3.2a image processing software to conduct Land Use Land Cover Change (LULCC) analysis on Remotely Sensed (RS) data and Geographical Information System (GIS). A supervised classification was also adopted to identify land use type, and image differencing to identify the change. The study found out that there is a continuous decline in the total amount of farmland in Makurdi from 1999-2012. Specifically, it was found that in 1999, farmlands covered 43% of the study area while in 2012 it was reduced to 22%, indicating that spatial expansion of urban activities has been on the increase and may result in absolute loss in cropland with other sustainability risks and threats of livelihoods if not appropriately managed. The study therefore recommended that spatial expansion of urban activities should be well managed and controlled as well as to take into cognizance areas needed to be reserved for farming while carrying out urban development of an area.

BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING

Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena to promote better decision making. The bi-temporal hyperspectral imagery has a high potential for the detection of surface changes. However, the extraction of changes from bi-temporal hyperspectral imagery due to special content of data, and environment conditions (atmospheric condition), change into challenging task. To this end, this research proposed a change detection framework based on deep learning using bi-temporal hyperspectral imagery. The proposed framework is applied in two main steps: (1) predict phase that the change areas highlighted from no-change areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting change pixels based on 3D convolution neural network (CNN). The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. To evaluate the performance of the proposed method, two bi-temporal hyperspectral Hyperion with a variety of land cover classes were used. The results show that the proposed method has high accuracy and low false alarms rate: overall accuracy is more than 95%, and the kappa coefficient is greater than 0.9 and the miss-detection is lower than 10% and the false rate is lower than 4%.

TOWARDS DETECTION OF THERMAL ANOMALIES IN LARGE URBAN AREAS USING SIMULATION

Anomaly detection in imagery has widely been studied and enhanced towards the requirements of today’s available sensor data, whereas many of them require a background estimation in order to identify an anomaly or target. In this paper, we examine an analysis of simulation as background estimator for anomaly detection in thermal images of urban sceneries. We generate a surface temperature image and a sensor-like infrared image by combined image and elevation data and a thermal model suited for large scenes and fast simulation. With the simulated thermal image, we define anomalies as deviation between measurement and simulation. Pixel-wise image differencing of the measured and simulated temperatures and infrared images respectively are performed and evaluated concerning the full images as well as class-wise, including a material classification of the observed area. Our approach shows complementary results compared to RXD application on the measured infrared images. Metal roofs which appear warm in the thermal image and are not visually distinguishable from the residual image are detected.

A Geo-spatial Classification Time Series Change Detection Using Remote Sensing Images of Seshachalam Region

Time series is a scientific process of determining an ordered sequence of values of a variable within equally spaced time intervals. Mostly this is applied when looking at technical data and its influences on the neighboring surroundings. This type of scientific analysis that can be applied twofold. Firstly, it can be used to obtain a knowledge of the triggering forces and structure that produced the observed data. Additionally, it can be used to fit a model and to predict, monitor the area of interest. This scientific form of analysis can be applied in various sectors so long as the data can be measured over time. The following are some of the applications: Economic and sales forecasting, Crop Yield prediction, Forest cover changes, urbanization, among many other uses. In this analysis, we will focus on time series change detection using image differencing of a classified image and representing the outcome area in a bar graph. The area of study is Seshachalam Hills, Tirupathi an ecological zone in Andhra Pradesh, India.