scholarly journals Object-based Monitoring of Land Cover Changes in Mangrove Ecosystems of Senegal

2007 ◽  
Author(s):  
Giulia Conchedda ◽  
Laurent Durieux ◽  
Philippe Mayaux
Keyword(s):  
Land Cover ◽  
Land Cover Changes ◽  
Mangrove Ecosystems ◽  
Object Based

scholarly journals The Use of Small Format Air Photos for Mapping Land Cover Changes in Gumuk Pasir Parangtritis Core-Zone, 2015-2019

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Maulidini Fatimah Azahra ◽  
J Jumadi ◽  
Agus Anggoro Sigit
Keyword(s):  
Land Cover ◽  
Sand Dune ◽  
Land Use Changes ◽  
Mapping Method ◽  
Aerial Photographs ◽  
Land Cover Changes ◽  
Core Zone ◽  
The Core ◽  
Object Based ◽  
Tourism Economy

Gumuk Pasir Parangtritis is one of the potentials of the coastal area of ​​Parangtritis village in Yogyakarta, with several important roles for the coastal ecosystem and its surroundings, such as ecology, disaster, tourism, economy, and aquifer reserves. However, behind this important role, the existence of sandbanks is increasingly threatened from year to year because the area of ​​sand cover continues to decline, especially in the core zone. Therefore, regular and effective mapping and monitoring efforts are needed. This study aims to a) conduct land cover mapping using the Geographic Object Based Image Analysis (GEOBIA) method in the 2015-2019 timeframe; b) analyze changes in land cover in the core zone of sandbanks during 2015-2019; and c) evaluate the results of restoration of sand dune core zone in terms of land cover changes that have occurred until 2019. Small format aerial photographs (FUFK) are the data used in this study while the mapping method used is rule-based classification. The land cover of the sand dune core zone in 2015 included buildings, vegetation, sand, roads and ponds, while in 2019 it was in the form of buildings, vegetation, sand, and roads. Based on the classification results in the two years, it can be seen that there are changes in land cover (including area) through the cross-section of the two classification results. Some of the factors include the number of land use changes, the amount of vegetation, and sand mining. Furthermore, this change can be used as a basis for evaluating the success of the restoration efforts of the Gumuk Pasir Parangtritis core zone to date. The results of the evaluation show that the restoration carried out so far has not had much impact so it can be said that it has not been successful, because the area of ​​sand cover has actually decreased a lot (from 528,680 m2 to 344,347 m2), while the land cover in the form of vegetation and buildings has increased in size (from 869,341 m2 to 1,037,879 m2 for vegetation cover and an area of ​​4,674 m2 to 22,953 m2 for buildings).

scholarly journals Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 644
Author(s):  
Shih-Yuan Lin ◽  
Cheng-Wei Lin ◽  
Stephan van Gasselt
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Detection Efficiency ◽  
Image Data ◽  
Coordinate Frame ◽  
Land Cover Changes ◽  
Robust Detection ◽  
Object Based ◽  
Intensity Images ◽  
Image Pairs

We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.

scholarly journals Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

2019 ◽  
Vol 6 (4) ◽  
pp. 775
Author(s):  
Eveline Pereira ◽  
Eduarda Silveira ◽  
Inácio Thomaz Bueno ◽  
Fausto Weimar Acerbi Júnior
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Seasonal Changes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Changes ◽  
Spectral Features ◽  
Object Based ◽  
Global Accuracy ◽  
Or Fires

The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm.

scholarly journals Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil

2017 ◽  
Vol 41 (5) ◽  
pp. 554-564 ◽  
Author(s):  
Eduarda Martiniano de Oliveira Silveira ◽  
Fausto Weimar Acerbi Júnior ◽  
José Márcio de Mello ◽  
Inácio Thomaz Bueno
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Input Data ◽  
Dam Failure ◽  
Support Vector ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Analysis Tool ◽  
Object Based

ABSTRACT Object-based change detection is a powerful analysis tool for remote sensing data, but few studies consider the potential of temporal semivariogram indices for mapping land-cover changes using object-based approaches. In this study, we explored and evaluated the performance of semivariogram indices calculated from remote sensing imagery, using the Normalized Differential Vegetation Index (NDVI) to detect changes in spatial features related to land cover caused by a disastrous 2015 dam failure in Brazil’s Mariana district. We calculated the NDVI from Landsat 8 images acquired before and after the disaster, then created objects by multiresolution segmentation analysis based on post-disaster images. Experimental semivariograms were computed within the image objects and semivariogram indices were calculated and selected by principal component analysis. We used the selected indices as input data to a support vector machine algorithm for classifying change and no-change classes. The selected semivariogram indices showed their effectiveness as input data for object-based change detection analysis, producing highly accurate maps of areas affected by post-dam-failure flooding in the region. This approach can be used in many other contexts for rapid and accurate assessment of such land-cover changes.

scholarly journals Land cover changes assessment using object‐based image analysis in the Binah River watershed (Togo and Benin)

2015 ◽  
Vol 2 (10) ◽  
pp. 403-416 ◽  
Author(s):  
Hèou Maléki Badjana ◽  
Jörg Helmschrot ◽  
Peter Selsam ◽  
Kpérkouma Wala ◽  
Wolfgang‐Albert Flügel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Changes ◽  
River Watershed ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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