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.

scholarly journals Surface composition of debris-covered glaciers across the Himalaya using linear spectral unmixing of Landsat 8 OLI imagery

2021 ◽  
Vol 15 (9) ◽  
pp. 4557-4588
Author(s):  
Adina E. Racoviteanu ◽  
Lindsey Nicholson ◽  
Neil F. Glasser
Keyword(s):  
Large Scale ◽  
Surface Composition ◽  
Spectral Unmixing ◽  
Eastern Himalaya ◽  
Landsat 8 ◽  
Mountain Range ◽  
Dynamic Topography ◽  
Debris Cover ◽  
Linear Spectral Unmixing ◽  
The Himalaya

Abstract. The Himalaya mountain range is characterized by highly glacierized, complex, dynamic topography. The ablation area of Himalayan glaciers often features a highly heterogeneous debris mantle comprising ponds, steep and shallow slopes of various aspects, variable debris thickness, and exposed ice cliffs associated with differing ice ablation rates. Understanding the composition of the supraglacial debris cover is essential for a proper understanding of glacier hydrology and glacier-related hazards. Until recently, efforts to map debris-covered glaciers from remote sensing focused primarily on glacier extent rather than surface characteristics and relied on traditional whole-pixel image classification techniques. Spectral unmixing routines, rarely used for debris-covered glaciers, allow decomposition of a pixel into constituting materials, providing a more realistic representation of glacier surfaces. Here we use linear spectral unmixing of Landsat 8 Operational Land Imager (OLI) images (30 m) to obtain fractional abundance maps of the various supraglacial surfaces (debris material, clean ice, supraglacial ponds and vegetation) across the Himalaya around the year 2015. We focus on the debris-covered glacier extents as defined in the database of global distribution of supraglacial debris cover. The spectrally unmixed surfaces are subsequently classified to obtain maps of composition of debris-covered glaciers across sample regions. We test the unmixing approach in the Khumbu region of the central Himalaya, and we evaluate its performance for supraglacial ponds by comparison with independently mapped ponds from high-resolution Pléiades (2 m) and PlanetScope imagery (3 m) for sample glaciers in two other regions with differing topo-climatic conditions. Spectral unmixing applied over the entire Himalaya mountain range (a supraglacial debris cover area of 2254 km2) indicates that at the end of the ablation season, debris-covered glacier zones comprised 60.9 % light debris, 23.8 % dark debris, 5.6 % clean ice, 4.5 % supraglacial vegetation, 2.1 % supraglacial ponds, and small amounts of cloud cover (2 %), with 1.2 % unclassified areas. The spectral unmixing performed satisfactorily for the supraglacial pond and vegetation classes (an F score of ∼0.9 for both classes) and reasonably for the debris classes (F score of 0.7). Supraglacial ponds were more prevalent in the monsoon-influenced central-eastern Himalaya (up to 4 % of the debris-covered area) compared to the monsoon-dry transition zone (only 0.3 %) and in regions with lower glacier elevations. Climatic controls (higher average temperatures and more abundant precipitation), coupled with higher glacier thinning rates and lower average glacier velocities, further favour pond incidence and the development of supraglacial vegetation. With continued advances in satellite data and further method refinements, the approach presented here provides avenues towards achieving large-scale, repeated mapping of supraglacial features.

scholarly journals Índices de vegetación y unidades de paisaje de la Reserva de Fauna Silvestre Ciénaga de La Palmita e Isla de Pájaros, estado Zulia, Venezuela

Revista Alfa ◽  
2020 ◽  
Vol 4 (11) ◽  
pp. 157-169
Author(s):  
Antonio Vera ◽  
Gustavo Morillo ◽  
Darisol Pacheco
Keyword(s):  
Rhizophora Mangle ◽  
Avicennia Germinans ◽  
Laguncularia Racemosa ◽  
Landsat 8 ◽  
Conocarpus Erectus

Se determinaron los índices de vegetación y las unidades de paisaje de la Reserva de Fauna Silvestre Ciénaga de La Palmita e Isla de Pájaros, empleando imágenes LANDSAT 8 (2013-2016) y el NDVI usando el programa ENVI. Se identificaron siete unidades de paisaje. El espejo de agua mostró un NDVI bajo (0,00-0,03), asociado al muy escaso vigor vegetal y el borde ciénaga-porción terrestre presentó 0,04-0,08 vinculado a los primeros indicios de productividad. Los suelos fangosos-desnudos revelaron 0,09-0,16 relacionado a escasas capas de fitoplancton. Las comunidades xerófilas intervenidas presentaron 0,17-0,24 contrastando con 0,25-0,28 de las formaciones xerófilas, espinosas y semideciduas bajas. Las comunidades de Avicennia germinans, Conocarpus erectus y Laguncularia racemosa mostraron 0,29-0,39 debido al bajo porte e individuos muertos; y Rhizophora mangle reveló 0,4-0,55, vinculado con la altura arborea, dosel semicerrado, pocos claros y verdor foliar intenso-brillante. Las unidades variaron desde comunidades xerófilas intervenidas hasta comunidades de R. mangle relativamente bien conservadas.

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 Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 187 ◽  
Author(s):  
Viktor Myroniuk ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Andrii Bilous ◽  
Shuguang Liu
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Forest Cover ◽  
Regional Scale ◽  
Forest Products ◽  
Google Earth ◽  
Tree Cover ◽  
Landsat 8 ◽  
Fragmented Landscapes ◽  
Forest Mapping

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

scholarly journals Land Use and Land Cover Change Analysis Using GIS and Remote Sensing in The Case of Kersa District, Jimma Zone, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Nigus Tekleselassie Tsegaye
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Forest Cover ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Natural Forest ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based

Abstract Background: Land use and land cover change is driven by human actions and also drives changes that limit availability of products and services for human and livestock, and it can undermine environmental health as well. Therefore, this study was aimed at understanding land use and land cover change in Kersa district over the last 30 years. Time-series satellite images that included Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, which covered the time frame between 1990-2020, were used to determine the change in land use and land cover using object based classification.Results: The object based classification result revealed that in 1990 TM Landsat imagery, natural forest (16.07%), agroforestry (9.21%), village (12.03%), urban (1.93%), and agriculture (60.76%) were identified. The change result showed a rapid reduction in natural forest cover of 25.04%, 9.15%, and 23.11% occurred between (1990-2000), (2000-2010), and (2010-2020) study periods, respectively. Similarly agroforestry decreased by 0.88% and 63.9% (2000-2010) and (2010-2020), respectively. The finding indicates the increment of agricultural land, village, and urban, while the natural forest and agroforestry cover shows a declining trend.Conclusions: The finding implies that there was a rapid land use and land cover change in the study area. This resulted in loss of natural resource and biodiversity. Overall, proper and integrated approach in implementing policies and strategies related to land use and land cover management should be required in kersa district.

scholarly journals SPATIO-TEMPORAL ASSESSMENT OF MARGALLA HILLS FOREST BY USING LANDSAT IMAGERY FOR YEAR 2000 AND 2018

2018 ◽  
Vol XLII-3 ◽  
pp. 69-72
Author(s):  
R. Batool ◽  
K. Javaid
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Temporal Changes ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Open Land ◽  
Spatio Temporal ◽  
Negative Impacts ◽  
Geographic Information Sciences ◽  
Year 2000

Environmental imbalance due to human activities has shown serious threat to ecosystem and produced negative impacts. The main goal of this study is to identify, monitor and classify temporal changes of forest cover, build up and open spaces in Margalla Hills National Park, Islamabad. Geographic Information Sciences (GISc) and Remote Sensing (RS) techniques has been used for the assessment of analysis. LANDSAT-7 Enhanced Thematic Mapper (ETM+) and LANDSAT-8 Operational Land Imager (OLI) were utilized for obtaining data of year 2000 and 2018. Temporal changes were evaluated after applying supervised classification and discrimination was analyzed by Per-Pixel based change detection. Results depicts forest cover decrease from 87 % to 74 % whereas build up has increased from 5 % to 7 % over the span. Consequences also justify the presence of open land in study area that has been increased from 2 % to 7 % respectively.

scholarly journals Annual Forest Monitoring as part of Indonesia's National Carbon Accounting System

2015 ◽  
Vol XL-7/W3 ◽  
pp. 441-448
Author(s):  
K. Kustiyo ◽  
O. Roswintiarti ◽  
A. Tjahjaningsih ◽  
R. Dewanti ◽  
S. Furby ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Carbon Accounting ◽  
Forest Monitoring ◽  
Automated System ◽  
Landsat 8 ◽  
Accounting System ◽  
Forest Change

Land use and forest change, in particular deforestation, have contributed the largest proportion of Indonesia’s estimated greenhouse gas emissions. Indonesia’s remaining forests store globally significant carbon stocks, as well as biodiversity values. In 2010, the Government of Indonesia entered into a REDD+ partnership. A spatially detailed monitoring and reporting system for forest change which is national and operating in Indonesia is required for participation in such programs, as well as for national policy reasons including Monitoring, Reporting, and Verification (MRV), carbon accounting, and land-use and policy information. <br><br> Indonesia’s National Carbon Accounting System (INCAS) has been designed to meet national and international policy requirements. The INCAS remote sensing program is producing spatially-detailed annual wall-to-wall monitoring of forest cover changes from time-series Landsat imagery for the whole of Indonesia from 2000 to the present day. Work on the program commenced in 2009, under the Indonesia-Australia Forest Carbon Partnership. A principal objective was to build an operational system in Indonesia through transfer of knowledge and experience, from Australia’s National Carbon Accounting System, and adaptation of this experience to Indonesia’s requirements and conditions. A semi-automated system of image pre-processing (ortho-rectification, calibration, cloud masking and mosaicing) and forest extent and change mapping (supervised classification of a ‘base’ year, semi-automated single-year classifications and classification within a multi-temporal probabilistic framework) was developed for Landsat 5 TM and Landsat 7 ETM+. Particular attention is paid to the accuracy of each step in the processing. With the advent of Landsat 8 data and parallel development of processing capability, capacity and international collaborations within the LAPAN Data Centre this processing is being increasingly automated. Research is continuing into improved processing methodology and integration of information from other data sources. <br><br> This paper presents technical elements of the INCAS remote sensing program and some results of the 2000 – 2012 mapping.

scholarly journals Changes of Norway Spruce Health in the Białowieża Forest (CE Europe) in 2013–2019 during a Bark Beetle Infestation, Studied with Landsat Imagery

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 34
Author(s):  
Maja Bryk ◽  
Beata Kołodziej ◽  
Radosław Pliszka
Keyword(s):  
Norway Spruce ◽  
Bark Beetle ◽  
Bark Beetles ◽  
Large Scale ◽  
Sustainable Forest Management ◽  
Landsat Imagery ◽  
Regional Scale ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Białowieża Forest

Among the largest disturbances affecting the health of spruce forests is the large-scale appearance of bark beetles. Knowledge on the spatial distribution of infected-spruce areas is vital for effective and sustainable forest management. Medium-spatial-resolution (20–30 m) satellite images are well-suited for spruce forest disturbance monitoring at a landscape and regional scale following bark beetle outbreaks. The aim of this study was to evaluate the health of a Norway spruce stand after a bark beetle outbreak based on Landsat 8 images and thematic and vector data, supplemented with selected climate variables. This research was conducted for a spruce stand in the Białowieża Forest District in 2013, 2015, 2017, and 2019. We hypothesised that the changes in spruce health would significantly influence the NDVI distributions during the studied years. Our research revealed that the weather conditions in the period of May–September were beneficial for beetle development and detrimental for the spruce stand, particularly in 2015, 2018, and 2019. SWIR-NIR-G and NDVI images showed a gradual deterioration in spruce health. The quantitative NDVI distributions varied; the minimum, mean, and median decreased; and the distribution shape of the index values changed over the studied years. An analysis of the spatial NDVI distributions revealed that the threshold NDVI value separating spruce stand areas in good and poor health was ca. 0.6. This study confirmed the applicability of NDVI for monitoring alterations in spruce stands, and indicated that spatial NDVI distributions can provide valuable support in forest monitoring at a landscape scale, since medium-resolution, ready-to-use NDVI images are easily available from the Landsat archives, facilitating the routine assessment of stand health.

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