Mapping of active and empty aquaponds using spectral indices in coastal region of Guntur District, Andhra Pradesh, India

Aim: To evaluate spectral indices as tools for separation of active aquaponds filled with water and engaged in shrimp/fish production from empty aquaponds using Landsat -8 data in coastal region of Guntur district, Andhra Pradesh. Methodology: The active and empty aquaponds were demarcated with Landsat satellite (Landsat-8) Operational Land Imager’s (OLI) multispectral images using maximum likelihood classifier (MLC) algorithm and spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Water Ratio Index (WRI) and Automated Water Extraction Index (AWEInsh) by means of thresholds. Results: The supervised classification using maximum likelyhood classifier recorded the highest active aquapond area whereas; NDWI, combination of indices and WRI resulted in lower but almost similar extents. Evaluation of confusion matrix using validation points revealed that NDWI, WRI and combination of indices resulted in all most perfect agreement with a kappa value of more than 0.9. Maximum likelihood classifier, NDVI and MNDWI could separate active ponds and empty ponds from other land uses with strong agreement, while AWEInsh could separate different land uses only with moderate agreement. Interpretation: The study indicates that spectral indices like NDWI, WRI and combination of indices are able to delineate aquaponds that were cultured for shrimp/fish and kept empty at a given time with noticeably high accuracy using satellite data for better managing of resources in coastal ecosystem.

Đánh giá biến động rừng ngập mặn tỉnh Tiền Giang trên cơ sở ảnh vệ tinh giai đoạn 1988-2018

Trong nghiên cứu này, tư liệu ảnh viễn thám Landsat 5-TM, 8-OLI và phương pháp phân loại có kiểm định Maximum Likelihood Classifier – MCL được sử dụng để phân loại và đánh giá biến động diện tích rừng ngập mặn (RNM) tỉnh Tiền Giang giai đoạn 1988-2018. Kết quả giải đoán ảnh viễn thám năm 1988, 1998, 2013, 2018 và kết quả chồng xếp các bản đồ rừng ngập mặn qua các giai đoạn cho thấy diện tích RNM ở Tiền Giang giảm liên tục từ năm 1988 đến năm 2013, sau đó tăng từ năm 2013 đến năm 2018. Nếu xét trong khoảng thời gian 30 năm từ 1988 đến 2018, tổng diện tích RNM ở Tiền Giang đã giảm 12,4% so với ban đầu, với 1.761,8 ha năm 1988 giảm xuống còn 1.543,5 ha năm 2018, giảm đi 218,4 ha. Tốc độ phục hồi của RNM được xác định là 36 ha/năm, thấp hơn so với tốc độ biến mất của chúng trong giai đoạn 1988-2018 (43 ha/năm). RNM được phục hồi chủ yếu từ mặt nước biển ven bờ (chiếm 66,6%); trồng mới RNM trong các ao nuôi tôm bị bỏ hoang, hoặc trồng RNM kết hợp với nuôi trồng thủy sản (NTTS) (chiếm 27,6%).

EELGRASS MAPPING IN ATLANTIC CANADA USING WORLDVIEW-2 IMAGERY

Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services, and while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac Bay, New Brunswick (Canada), an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the non-parametric Random Forests (RF) classifier. While Random Forests was expected to produce higher classification accuracies, it was shown not to be much better than MLC. The overall validation accuracy was 97.6% with RF and 99.8% with MLC.

AUTOMATIC DETECTION OF GREY INFRASTRUCTURE BASED ON VHR IMAGE

Grey infrastructure is an integral part of the urban environment. Continuous modernization of architecture, construction, routes or services in that region leads to more and more new grey infrastructure appearing. The reason for this are constant migrations of people, dissemination of a healthy lifestyle or improvement of its level. Its growth is particularly noticeable in agglomerations where keeping the balance between sealed and vegetated area is very much concerned. Therefore, it is necessary to constantly monitor changes over time and thus update the databases containing information on land cover such as the Topographical Database. For this purpose VHR images were processed and analysed in terms of detection efficiency of topographical objects defined as grey infrastructure. This study presents the results of an analysis of the possibility of updating the land cover classes in the Topographical Database based on WorldView-2 satellite images.The methods used to detect grey infrastructure come from a machine learning approach such as Random Forests and parametric Maximum Likelihood classifier, resulting at a 90% level of accuracy.The other aim of the work was to analyse changes in the grey infrastructure on the basis of the Topographic Database at scale 1:10000 using a VHR satellite image. The analysis of its changes was carried out on the dynamically developing city of Warsaw.

CLASSIFICATION OF HIGH-RESOLUTION SATELLITE IMAGES USING FUZZY LOGICS INTO DECISION TREE

In this paper, DTFL an image classifier based on Decision Tree and Fuzzy Logics is proposed. At the beginning of classification using DTFL, each pixel is located at the highest level of a decision tree where it belongs to the combination of all classes. DTFL transfers a pixel to a lower level of the decision tree where the pixel belongs to a combination of fewer classes. Decision-making about transfers is based on fuzzy logic with seven different membership functions including triangular-shaped, trapezoidal-shaped, π-shaped, bell-shaped, Gaussian, differential S-shaped and multiplicative S-shaped. Eventually, pixels will reach the lowest level of the decision tree where it belongs to only one class. For accuracy assessment, DTFL was used to classify a GeoEye-1 image. The overall accuracy of 96.14% and a kappa coefficient of 96.06% were reached by DTFL. In comparison, the overall accuracy of 89.91% and a kappa coefficient of 89.77% were reached by a Maximum Likelihood Classifier, MLC. In the case of applying a threshold in MLC to reach the same accuracy as DTFL, 8.73% of pixels take the non-classified label while using DTFL all the pixels get a proper label. The results indicate that the proposed classifier extracts more information from images.

Pixel based Classification of Poultry Farm using Satellite Images

Remote sensing has emerged as a compelling tool to survey and monitor natural resources and other features of an area due to the inherent advantages of synoptic view, repetitive nature and capability to study inaccessible areas. Satellite data/aerial photos are interpreted using keys such as colour/tone, texture, pattern, association, size, shape, etc., and computer-based techniques. Presently geospatial technology is used in various sectors like agriculture, forestry, geology, marine, urban and rural planning and so on, with applications in agriculture seeing a rise in India. This paper elaborates on the method employed for identification of poultry farms in India, using images from satellites such as CARTOSAT and RESOURCESAT (LISS4) and also Google Earth Images. Each poultry farm varies in the size and number of poultry sheds which further depend on the number of chickens bred, location of vegetation and water resources nearby, temperature and humidity of location, etc. Thus, based on these factors, training sites in Hessarghatta, Harohalli, Dommasandra near Bengaluru City, Karnataka were identified. The paper elucidates application of vegetation and water masks using the classification of NDVI. Two pixel-based classification techniques - Maximum Likelihood Classifier and K-Nearest Neighbour Classifier using SNAP Application were applied. Statistics were observed for the accuracy of classified output, and it was shown that Maximum Likelihood Classifier provided more accurate results. The method presented in this paper can be fine-tuned and applied for poultry farms anywhere by studying Poultry Farms in different terrains and using various associations to identify them.

THE CONTRIBUTION OF SENTINEL-2 SATELLITE IMAGES FOR GEOLOGICAL MAPPING IN THE SOUTH OF TAFILALET BASIN (EASTERN ANTI-ATLAS, MOROCCO)

<p><strong>Abstract.</strong> Geological mapping in desert, mountainous or densely vegetated areas are sometimes faced with many constraints. Recently several remote sensing methods are used on ASTER or LANDSAT imagery for making that task easier. The aim of this paper is to evaluate the applicability of some of these methods on Sentinel-2A images. The study, therefore, focuses on a lithological classification using these multispectral images in the south of the Tafilalet basin. To achieve this goal, two L1C level images were used. Decorelation stretch combined with the optimal index factor (OIF) and Minimum Noise Fraction (MNF) were the main improvements used for RGB combination images. The classifiers Spectral Angle Mapper (SAM) and the Maximum Likelihood classifier (MLC) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest drawn geological maps and RGB images of false colour combinations were used to select regions of interest (ROI) as the endmembers to use for these classifiers. Obtained results showed a clear discrimination of the different lithological units of the study area. Classifications evaluation showed that the Maximum likelihood classifier is more accurate with an overall accuracy of 76% and a Kappa coefficient is 0.74. Finally, this study has shown the importance of the use of sentinel-2 multispectral images in geological mapping and has shown that the high spectral resolution of the VNIR and SWIR bands creates a synergy with the high spatial resolution for optimal lithological mapping.</p>