LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change:Analysis of an Active Landslide

Active landslides have three major effects on landscapes: (1) land cover change, (2) topographical change, and (3) above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR) are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR) and 76% (using only orthophotos) to 90% (using data synergy) for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses

A hierarchical clustering method for land cover change detection and identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis

A Novel Approach for improving Post Classification Accuracy of Satellite Images by Using Majority Analysis

In past one year, due to climatic changes and some anthropogenic activities, the forests of Uttarakhand are burning. To identify the damage caused by the forest fires, an area of Nainital district has been taken for the study. Multi temporal Landsat 7 images were taken from April - 2020 and April – 2021. This paper shows a novel approach to increase the accuracy of the classified image. The Support Vector Machine classification is first done and then to improve the accuracy of the classified image, a post-classification technique called Majority Analysis is applied. This method helps to classify the unclassified pixel and it also smoothens out the boundary of the classified pixels, leading to higher accuracy rate. The classification accuracy has improved significantly for April 2020 and April 2021 images from 89.35% to 98.71% and from 88.52% to 99.76% respectively. The change detection study showed a drastic increase in the barren land due to the forest fires and on the contrary, the forest, scarce forest and the shrub land areas have decreased

DEEP LEARNING BASED AERIAL IMAGERY CLASSIFICATION FOR TREE SPECIES IDENTIFICATION

Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively

An unsupervised classification-based time series change detection approach for mapping forest disturbance

Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 – 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results.Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) grant entitled Advanced Methods, Education and Training in Hyperspectral Science and Technology (AMETHYST). Financial code: KS-NSERC2 Staenz 40307-4185-800

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Landcover Change Patterns in the Volta Gorge Area, Ghana: Interpretations from Satellite Imagery

In much of the developing world, shifting cultivation is the predominant agricultural system usually practised by farmers, which is associated with increasing the unit of land under cultivation in order to increase the output. This small-scale shifting agricultural practices based primarily on burning has been the factor responsible for the conversion of forest fringe vegetation into cropland and pasture and it initiates a continuum of changes in the land cover and consequently fragmentation of the landscape. Since the construction of the Akosombo Dam in 1965, the potential for farming and fishing has increased tremendously within the Volta gorge area and this has resulted in the influx of many settler farmers and fisher folk into the area. This brought so much pressure on the natural resource base of the area and has transformed the landscape accordingly. Within this paper we aim at the estimation of the trend in changes of the landscape using multi-temporal satellite image processing and spatial change analysis. We also seek to determine the land use / cover change in the Volta gorge area of the Volta basin of Ghana for the period 1975 to 2007. Land cover change and agricultural expansion was observed through digital processing and classification based on five multi-temporal medium resolution satellite imagery (Landsat: 1975, 1990, 2000, 2003, 2007) into five classes. From this, accurately classified pixel information was used to determine each landcover class size and the number of changed pixels into other classes through change detection. The study shows that significant changes in the landscape involved a general pattern of conversion of both Closed forest and Open forest and woodland into cropland, fallow lands, pasture, and Bare areas consisting of settlements, roads and exposed soil surfaces. For the period under consideration 1975-2007, the Agricultural class increased from 52,605 hectares in 1975 to become the land cover type with the highest proportion of cover at 221,567 hectares in 2007. The study concludes among others that the expansion of agriculture has increased in all directions with the amount of land devoted to cropland and pasture increasing for all slope categories but more especially for less steeper slopes. Keywords: Landuse/landcover, Multi-temporal, Digital processing, Change detectio

Efficient Action Detection in Untrimmed Videos via Multi-Task Learning

This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms state-of-the-art methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc