Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping

2016 ◽  
Vol 9 (7) ◽  
pp. 3340-3350 ◽  
Author(s):  
Zina Mitraka ◽  
Fabio Del Frate ◽  
Francesco Carbone
Keyword(s):  
Landsat Imagery ◽  
Spectral Unmixing ◽  
Urban Surface ◽  
Surface Cover

Characterizing urban surface cover and structure with airborne lidar technology

2009 ◽  
Vol 35 (3) ◽  
pp. 297-309 ◽  
Author(s):  
Nicholas R Goodwin ◽  
Nicholas C Coops ◽  
Thoreau Rory Tooke ◽  
Andreas Christen ◽  
James A Voogt
Keyword(s):  
Airborne Lidar ◽  
Urban Surface ◽  
Surface Cover

scholarly journals SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 2609-2614
Author(s):  
R. Zhuo ◽  
L. Xu ◽  
J. Peng ◽  
Y. Chen
Keyword(s):  
Time Series ◽  
Landsat Imagery ◽  
Temporal Analysis ◽  
Mean Value ◽  
Spectral Unmixing ◽  
Least Square ◽  
Abundance Estimation ◽  
Landsat 8 ◽  
Mixed Pixel ◽  
Analysis Of Time Series

Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the “purified” pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of “purified” pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed “joint unmixing” approach provides more accurate endmember and abundance estimation results compared with “separate unmixing” approach.

scholarly journals Variation in photosynthetic and nonphotosynthetic vegetation along edaphic and compositional gradients in northwestern Amazonia

2014 ◽  
Vol 11 (13) ◽  
pp. 3505-3513 ◽  
Author(s):  
M. A. Higgins ◽  
G. P. Asner ◽  
E. Perez ◽  
N. Elespuru ◽  
A. Alonso
Keyword(s):  
Species Composition ◽  
Tropical Forests ◽  
Plant Species ◽  
Landsat Imagery ◽  
Canopy Structure ◽  
Spectral Unmixing ◽  
Canopy Openness ◽  
Plant Species Composition ◽  
Compositional Gradients ◽  
Analysis System

Abstract. Tropical forests vary substantially in aboveground properties such as canopy height, canopy structure, and plant species composition, corresponding to underlying variations in soils and geology. Forest properties are often difficult to detect and map in the field, however, due to the remoteness and inaccessibility of these forests. Spectral mixture analysis of Landsat imagery allows mapping of photosynthetic and nonphotosynthetic vegetation quantities (PV and NPV), corresponding to biophysical properties such as canopy openness, forest productivity, and disturbance. Spectral unmixing has been used for applications ranging from deforestation monitoring to identifying burn scars from past fires, but little is known about variations in PV and NPV in intact rainforests. Here we use spectral unmixing of Landsat imagery to map PV and NPV in northern Amazonia, and to test their relationship to soils and plant species composition. To do this we sampled 117 sites crossing a geological boundary in northwestern Amazonia for soil cation concentrations and plant species composition. We then used the Carnegie Landsat Analysis System to map PV and NPV for these sites from multiple dates of Landsat imagery. We found that soil cation concentrations and plant species composition consistently explain a majority of the variation in remotely sensed PV and NPV values. After combining PV and NPV into a single variable (PV–NPV), we determined that the influence of soil properties on canopy properties was inseparable from the influence of plant species composition. In all cases, patterns in PV and NPV corresponded to underlying geological patterns. Our findings suggest that geology and soils regulate canopy PV and NPV values in intact tropical forests, possibly through changes in plant species composition.

Urban surface cover determined with airborne lidar at 2m resolution – Implications for surface energy balance modelling

Urban Climate ◽  
2015 ◽  
Vol 13 ◽  
pp. 52-72 ◽  
Author(s):  
Annika Nordbo ◽  
Petteri Karsisto ◽  
Leena Matikainen ◽  
Curtis R. Wood ◽  
Leena Järvi
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Airborne Lidar ◽  
Urban Surface ◽  
Surface Cover

scholarly journals Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing

2021 ◽  
Vol 13 (10) ◽  
pp. 1961
Author(s):  
Florent Lombard ◽  
Julien Andrieu
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Regional Scale ◽  
Rhizophora Mangle ◽  
Spectral Unmixing ◽  
Avicennia Germinans ◽  
Landsat 8 ◽  
Conservation Policies ◽  
Linear Spectral Unmixing ◽  
Mangrove Zonation

The mangrove areas in Senegal have fluctuated considerably over the last few decades, and it is therefore important to monitor the evolution of forest cover in order to orient and optimise forestry policies. This study presents a method for mapping plant formations to monitor and study changes in zonation within the mangroves of Senegal. Using Landsat ETM+ and Landsat 8 OLI images merged to a 15-m resolution with a pansharpening method, a processing chain that combines an OBIA approach and linear spectral unmixing was developed to detect changes in mangrove zonation through a diachronic analysis. The accuracy of the discriminations was evaluated with kappa indices, which were 0.8 for the Saloum delta and 0.83 for the Casamance estuary. Over the last 20 years, the mangroves of Senegal have increased in surface area. However, the dynamics of zonation differ between the two main mangrove hydrosystems of Senegal. In Casamance, a colonisation process is underway. In the Saloum, Rhizophora mangle is undergoing a process of densification in mangroves and appears to reproduce well in both regions. Furthermore, this study confirms, on a regional scale, observations in the literature noting the lack of Avicennia germinans reproduction on a local scale. In the long term, these regeneration gaps may prevent the mangrove from colonising the upper tidal zones in the Saloum. Therefore, it would be appropriate to redirect conservation policies towards reforestation efforts in the Saloum rather than in Casamance and to focus these actions on the perpetuation of Avicennia germinans rather than Rhizophora mangle, which has no difficulty in reproducing. From this perspective, it is necessary to gain a more in-depth understanding of the specific factors that promote the success of Avicennia germinans seeding.

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