Bi-temporal Change Detection, Change Trajectories and Time Series Analysis for Forest Monitoring

Thonfeld, Frank; Hecheltjen, Antje; Menz, Gunter

doi:10.1127/pfg/2015/0259

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Bi‐temporal comparison is the most widely used approach to change‐detection (Hussain et al, ; Lu et al, ; Tewkesbury et al, ) but it is also the most simplistic. Temporal trajectory analysis would give more insight into whether continuous change is occurring and in which direction (Coppin et al, ; Czerwinski, King, & Mitchell, ; Thonfeld, Hecheltjen, & Menz, ). This approach was not used in this case because the timespan of the dataset was too short to expect to see continuous change, phenological conditions are not comparable between images, and the study area would be reduced to the area of the imagery captured at the highest tide.…”

Section: Discussionmentioning

confidence: 99%

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Lightfoot

Scott

Fitzsimmons

2020

Aquatic Conservation

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Intertidal habitat maps are needed at both fine and coarse scales to monitor change and inform conservation and management, but current methods of field survey and expert interpretation of aerial imagery can be time‐consuming and subjective. Object‐based image analysis (OBIA) of remote sensing data is increasingly employed for producing habitat or land cover maps. Users create automated workflows to segment imagery, creating ecologically meaningful objects, which are then classified based on their spectral or geometric properties, relationships to other objects and contextual data. This study evaluates the change‐detection capability of OBIA in the intertidal environment by developing and comparing two OBIA methods for quantifying change in extent and distribution of habitats from freely available multi‐temporal aerial imagery and LiDAR data. Despite considerable variability in the data, pre‐ and post‐classification change detection methods had sufficient accuracy (mean overall accuracy from 70.5 to 82.6%) to monitor deviation from a background level of natural environmental fluctuation. This insight into spatial and temporal patterns of natural cyclical change and their detectability by OBIA could inform use of remote sensing for regular, rapid coastal assessment, providing an alert system to direct survey resources to areas of ecologically relevant change.

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Section: Discussionmentioning

confidence: 99%

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Lightfoot

Scott

Fitzsimmons

2020

Aquatic Conservation

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“…Same was observed in the wet season for year 2015. In general, the quality and density of vegetation [4,18,19] cover for the study area on an average, was higher in 2015 and 2017 for the wet and dry seasons, respectively.…”

Section: Normalized Difference Vegetationmentioning

confidence: 98%

Thermodynamic Entropy: A Natural Metric for Landcover Classification

Myina,

Myina

2023

AJRAF

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Most landcover (LC) studies employ physical characteristics involving spectral reflectance. This study employs the concept of energy and entropy for LC classification. Satellite imageries from Landsat-8, the Operational Land Imager (OLI) and the Thermal Infra-Red Sensor (TIRS) captured at 100 m for three years (2013, 2015 and 2017) and resampled to a spatial resolution of 30 m for multispectral measurements were acquired. The normalized difference vegetation index (NDVI) of the study area was computed for both the wet (September/October) and dry (March) seasons of years 2013, 2015 and 2017, respectively; and the vegetation cover maps and landcover classification of the area based on the NDVI values were generated. Land surface temperature (LST) and net radiation of the surface area were computed and used as inputs for computing the surface entropy flux (SEF) of the study area. Overall, the dry season of 2017 had the highest vegetation cover while the wet season vegetation cover was highest in 2015. The high dry season vegetation cover in 2017 is attributable to higher level of living biomass than previous years. NDVI-based LC classification of the area showed an overlap in distinguishing between built-up areas and vegetation cover as well as between bare (land) surface and free water bodies. This is attributable to skewed measure of centrality for the sample distribution possibly resulting from the quality (resolution) of data used or sampling techniques. Free water bodies had the highest SEF values that fall within expected behavior for bodies of such state (liquids). The vegetation covers had the second highest SEF values for the entire site and the highest on land surface, which could be attributed to higher latent heat fluxes of such covers resulting from the evapotranspiration processes. The bare surfaces and built-up area of the study area were observed to have the lowest SEF values. The LC of the study area was reclassified based on SEF values via ground truthing i.e., by taking statistical distribution of well-known surface classes across the years under study. This was successful in distinguishing vegetation cover, surfaces of free water bodies and bare surfaces. A regression analysis revealed that LST influences SEF by up to 69 – 75% while vegetation cover has no appreciable influence on SEF. A two-sample t-test showed a significant (P = 0.05) difference between the SEF for bare surfaces and that for vegetation cover. All these presents SEF as a reliable natural metric for LC classification.

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“…Despite these benefits, it still raises specific challenges regarding: the irregular temporal phenological signature of different land cover types; the insufficient sampling used to train the supervised classification; the missing temporal data [42]; the network architectures or specific datasets shaping that need to be developed for exploiting the temporal information jointly with the spatial and spectral information of the data [47]. Thus, in a more classical way, other sets of approaches and methods can be used varying from manual change interpretation [48] to bi-temporal linear data transformation [49] or multi-temporal spectral mixture analysis [50] and deep learning [51].…”

Section: Introductionmentioning

confidence: 99%

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

Nguyen

Dang

et al. 2022

Remote Sensing

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Socio-economic development has promoted the modification of land cover patterns in the coastal area of Ha Long, Cam Pha cities since the 1990s. The urban growth, together with intensive coal mining activities, has improved the life quality of residents. However, it has also caused many environmental problems in this region. Change detection techniques based on post-classification comparison were applied for monitoring the spatial and temporal evolution of land covers. The confusion matrix for 2001 and 2019 showed high overall accuracy (97.99%, 94.95%) and Kappa coefficient (0.97, 0.92), respectively. Statistics from classified images have revealed that man-made features increased by about 15.32%, while natural features, mangrove jungles, and water bodies decreased 10.64%, 1.96%, 2.72%, respectively, and urban evolution presents various dynamics, soft in the first period (1991–2001), but stronger in the second period (2001–2019) with different characteristics. The study also expresses the constraint of topographic and geologic resources, which have prevented the urban development in this coastal area. Such obtained results are very important for understanding interactions and relations between natural and human phenomena and they may help authorities by providing indicators and maps able to highlight necessary actions for sustainable development.

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Bi-temporal Change Detection, Change Trajectories and Time Series Analysis for Forest Monitoring

Cited by 7 publications

References 18 publications

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Thermodynamic Entropy: A Natural Metric for Landcover Classification

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

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