scholarly journals Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

2021 ◽  
Vol 13 (3) ◽  
pp. 367
Author(s):  
Edson E. Sano ◽  
Paola Rizzoli ◽  
Christian N. Koyama ◽  
Manabu Watanabe ◽  
Marcos Adami ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Forest Cover ◽  
Satellite Image ◽  
Optical Data ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Data Sets ◽  
Image Sampling ◽  
Forest Maps ◽  
Sar Data

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.

scholarly journals Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

2015 ◽  
Vol XL-7/W3 ◽  
pp. 777-782 ◽  
Author(s):  
A. Braun ◽  
V. Hochschild
Keyword(s):  
Land Cover ◽  
Optical Sensors ◽  
Land Degradation ◽  
Spatial Information ◽  
Environmental Changes ◽  
Optical Data ◽  
Landsat 8 ◽  
Refugee Camps ◽  
Internally Displaced ◽  
Regional Conflicts

Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses. <br><br> EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps. <br><br> This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.

scholarly journals BIOMASS AND CARBON STOCK ESTIMATION USING IN-SITU OBSERVATIONS AND GIS IN GILGIT BALTISTAN, PAKISTAN

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 49-55
Author(s):  
E. Fatima ◽  
S. S. Ali
Keyword(s):  
Carbon Stock ◽  
Anthropogenic Activities ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Accurate Method ◽  
Carbon Accounting ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Inventory Data ◽  
Gilgit Baltistan

Abstract. Carbon dioxide (CO2) emission and other greenhouse gases are rising day by day due to anthropogenic activities which lead to global warming and cause natural disasters. Thus REDD+ comes up with an initiative to reduce emissions from deforestation through Carbon accounting, in which the under developing countries Measure, Report, and Verify (MRV) the sum of Above Ground Biomass (AGB)/carbon stored in a particular forest. Nonetheless, the major challenge for REDD+ is to find an accurate method for biomass estimation. The purpose of this study was to model and map the AGB and carbon stock of Gilgit-Baltistan, Pakistan. For this purpose, we linked Landsat 8 and forest inventory data to assess the potential of Vegetation Indices (Vis) derived AGB estimation. Inventory data consisted of the tree measurements from 480 plots that data was collected in the year (June–Oct) 2016 in a 72,971 km2 (28,174 sq mi) study area, in Gilgit-Baltistan. Out of these plots, 287 was used in Calibration and 191 is used for Validation. This paper provides a regression equation between the reflection values from the Landsat-8 satellite image and sample areas where terrestrial aboveground biomass (AGB) was calculated by direct measurement method. As a result of the calculations made, a positive linear correlation between AGB and NDVI was relatively high compared to other vegetation indices i.e 0.59 in the year 2016 or for the year 2013.

scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

scholarly journals Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4012 ◽  
Author(s):  
Jianing Zhen ◽  
Jingjuan Liao ◽  
Guozhuang Shen
Keyword(s):  
Classification Accuracy ◽  
Mangrove Forest ◽  
Nature Reserve ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Accurate Information ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Sar Data

Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

Fusion of High-Resolution SAR and Optical Imageries Based on a Wavelet Transform and IHS Integrated Algorithm

2021 ◽  
Vol 52 ◽  
pp. 62-72
Author(s):  
Abdelrahman Yehia ◽  
Mohamed Safy ◽  
Ahmed S. Amein
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Optical Sensors ◽  
Remote Sensing Data ◽  
Optical Image ◽  
Data Sets ◽  
Sar Image ◽  
Volume Data ◽  
Fusion Algorithm ◽  
Sar Data

Multi-sensor remote sensing data can significantly improve the interpretation and usage of large volume data sources. A combination of satellite Synthetic Aperture Radar (SAR) data and optical sensors enables the use of complementary features of the same image. In this paper, SAR data is injected into optical image using a combining fusion method based on the integration of wavelet Transform and IHS (Intensity, Hue, and Saturation) transform. Not only to preserve the spectral information of the original (MS) image, but also to maintain the spatial content of the high-resolution SAR image. Two data sets are used to evaluate the proposed fusion algorithm: one of them is Pleiades, Turkey and the other one is Boulder, Colorado, USA. The different fused outputs are compared using different image quality indices. Visual and statistical assessment of the fused outputs displays that the proposed approach has an effective translation from SAR to the optical image. Hence, enhances the SAR image interpretability.

scholarly journals Forest Biomass Estimation using Multi-Polarization SAR Data Coupled with Optical Data

2020 ◽  
Vol 119 (8) ◽  
pp. 1316
Author(s):  
Praveen Kumar ◽  
Akhouri Pramod Krishna
Keyword(s):  
Forest Biomass ◽  
Optical Data ◽  
Biomass Estimation ◽  
Sar Data

scholarly journals Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 727 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Janik Deutscher ◽  
Carina Sobe ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Time Series ◽  
Patch Size ◽  
Forest Disturbance ◽  
Optical Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Disturbed Forest ◽  
Optical Time ◽  
The Individual ◽  
Sentinel 2

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

scholarly journals Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping

2019 ◽  
Author(s):  
Frederick N. Numbisi ◽  
Frieke M. B. Van Coillie ◽  
Robert De Wulf
Keyword(s):  
Optical Sensors ◽  
Forest Cover ◽  
Spectral Characteristics ◽  
Image Texture ◽  
Optical Data ◽  
Sar Image ◽  
Grey Level ◽  
Sar Images ◽  
Quantization Level ◽  
Class Prediction

Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.

scholarly journals AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA &amp; OPTICAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 137-140
Author(s):  
K. Ramalingam ◽  
A. B. Ramathilagam ◽  
P. Murugesan
Keyword(s):  
Tamil Nadu ◽  
Unsupervised Classification ◽  
Optical Data ◽  
Landsat 8 ◽  
Maize Crop ◽  
Classification Approach ◽  
Area Estimation ◽  
Crop Calendar ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> This study was carried out to estimate the area of cotton and maize crops in Permabalur district of Tamil Nadu using microwave and optical data. Permabalur was selected as the study area, as it is the largest cotton and maize producing district in Tamil Nadu. The multi-temporal Sentinel-1A SAR data was acquired from 09th July, 2016 to 17th January, 2017 as it coincides with the crop calendar of these crops. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data were compared. The cloud free Landsat 8 data acquired on 7th October 2016 was fused with the Vertical–Vertical (VV) and Vertical-Horizontal (VH) polarized data of 13th October and classified. Unsupervised classification approach was adopted to classify the cotton and maize pixels. The highest accuracy of 72.73% and 76.24% were achieved in VV polarization + Landsat 8 data and VH polarization + Landsat 8 data respectively. The cotton and maize areas were estimated to be 20,218&amp;thinsp;ha and 28,032&amp;thinsp;ha respectively. It is also evident that VH polarization fused with optical data is better in discriminating the cotton and maize crop than VV polarization fused with optical data.</p>

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