scholarly journals Patch-Based Assessments of Shifting Cultivation Detected by Landsat Time Series Images in Myanmar

2018 ◽  
Vol 10 (9) ◽  
pp. 3350
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Shigejiro Yoshida
Keyword(s):  
Time Series ◽  
Shifting Cultivation ◽  
Accuracy Assessment ◽  
Google Earth ◽  
Visual Interpretation ◽  
High Resolution Satellite Images ◽  
And Function ◽  
The Tropics ◽  
Time Series Images

Shifting cultivation is a widely practiced agriculture system in the tropics. Regardless of the dominant land use, the dynamics of shifting cultivation over large areas are of limited knowledge. We conducted patch-based assessments and characterization of shifting cultivation extracted from already developed dataset, which detected shifting cultivation by a trajectory-based analysis using annual Landsat TM/ETM+/OLI time series images from 2000 to 2014 in Myanmar. An accuracy assessment was conducted in terms of the size and number of cleared areas compared with reference polygons of shifting cultivation, which were manually delineated by visual interpretation using Landsat and high-resolution satellite images from Google Earth™ in the selected areas. The producer’s and user’s accuracies in detecting the number of shifting cultivation patches were 78.1% and 88.4%, respectively. In whole study area, the probability of disturbances caused by shifting cultivation was significantly affected by distance to the nearest village, indicating the importance of accessibility from residences. The number of shifting cultivation patches showed a decreasing trend in this region and it will lead to less cleared forests such as located far from residences. These dynamics of shifting cultivation have possibility to affect the mosaic patterns of landscape and function maintained in the landscape in this region.

scholarly journals Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition

2020 ◽  
Vol 12 (15) ◽  
pp. 2411 ◽  
Author(s):  
Thanh Noi Phan ◽  
Verena Kuch ◽  
Lukas W. Lehnert
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Land Cover Classification ◽  
High Accuracy ◽  
Google Earth ◽  
Temporal Aggregation ◽  
Google Earth Engine ◽  
Time Series Images ◽  
Land Cover Maps

Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

Localization and function ofRhipicephalus (Boophilus) microplusvitellin-degrading cysteine endopeptidase

Parasitology ◽  
2010 ◽  
Vol 137 (12) ◽  
pp. 1819-1831 ◽  
Author(s):  
ADRIANA SEIXAS ◽  
ANDRÉIA B. ESTRELA ◽  
JULIANA C. CEOLATO ◽  
EMERSON G. PONTES ◽  
FLÁVIO LARA ◽  
...  
Keyword(s):  
Fat Body ◽  
Boophilus Microplus ◽  
Basal Region ◽  
Cysteine Endopeptidase ◽  
Endopeptidase Activity ◽  
And Function ◽  
The Tropics ◽  
Different Tissues ◽  
Important Parasite

SUMMARYThe tickRhipicephalus (Boophilus) microplusis an important parasite of cattle in many areas of the tropics. Characterization of molecules involved in mechanisms such as vitellogenesis and embryo development may contribute to a better understanding of this parasite's physiology. The vitellin-degrading cysteine endopeptidase (VTDCE) is the most active enzyme involved in vitellin hydrolysis inR. micropluseggs. Here we show an association between VTDCE and vitellin in an additional site, apart from the active site. Our data also demonstrate cysteine endopeptidase activity in different tissues such as ovary, gut, fat body, salivary gland and female haemolymph, where it is controlled by a physiological inhibitor. InR. microplusfemale gut, VTDCE is localized in areas of protein synthesis and trafficking with the underlying haemolymph. VTDCE is also localized in the ovary basal region, in vesicle membranes of ovary pedicel cells and in oocyte cytosol. These results suggest that VTDCE plays a role in vitellin digestion during tick development.

scholarly journals SPATIOTEMPORAL CHANGE ANALYSIS OF THE PROTECTED AREAS: A CASE STUDY – İĞNEADA FLOODPLAIN FORESTS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 735-740
Author(s):  
M. Toker ◽  
E. Çolak ◽  
F. Sunar
Keyword(s):  
Time Series ◽  
Protected Areas ◽  
National Parks ◽  
National Park ◽  
Spatial Scales ◽  
Google Earth ◽  
Floodplain Forests ◽  
Landsat Satellite ◽  
Time Series Images ◽  
The Impact

Abstract. Protected areas are important with land or water body ecosystems that have biodiversity, flora and fauna species. In Turkey, National Parks are one of the protected areas managed according to the National Parks Law No. 2873. Among them, the İğneada Floodplain Forests National Park, located in İğneada town in the province of Kırklareli, Turkey has been declared as a national park in 2007, and has an importance being a rare ecosystem, which consists of wetland, swamp, lakes and coastal sand dunes. Planning of Protected Areas can be done in a variety of ways, taking into account the balance of protection/use and should follow policies and guidelines. Today, for the sustainability and effective management of forest ecosystems, remote sensing technology provides an effective tool for assessing and monitoring ecosystem health at different temporal and spatial scales. In this study, potential temporal changes in the National Park were analyzed with Landsat satellite time series images using two different methods. First method, the Landtrendr algorithm (Landsat-based Detection of Trends in Disturbance and Recovery) developed for multitemporal satellite data, uses pixel values as input data and analysis them by using regression models to capture, label and map the changes. In this context, Landsat satellite time series images were taken quinquennial between 1987 and 2007 and biennially until 2017 for Landtrendr analysis (i.e. before and after its declaration as a National Park, respectively). As a second approach, the Google Earth Engine (GEE) cloud-based platform, which facilitates access to high-performance computing resources to process large long-term data sets, was used to analyze the impact of land cover changes. The results showed that the area was subjected to various pressures (i.e. due to illegal felling, pollution, etc.) until it was declared as a National park. Although there was general improvement and recovery after the region declared as a Park, it was seen that the sensitive dynamics of the region require continuous monitoring and protection using geo-information technologies.

scholarly journals Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

2019 ◽  
Vol 11 (10) ◽  
pp. 1235 ◽  
Author(s):  
Aaron M. Shew ◽  
Aniruddha Ghosh
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
National Level ◽  
Google Earth ◽  
Dry Season ◽  
Future Research ◽  
Cropping Patterns

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

scholarly journals Wheat Area Mapping in Afghanistan Based on Optical and SAR Time-Series Images in Google Earth Engine Cloud Environment

2020 ◽  
Vol 8 ◽  
Author(s):  
Varun Tiwari ◽  
Mir A. Matin ◽  
Faisal M. Qamer ◽  
Walter Lee Ellenburg ◽  
Birendra Bajracharya ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Cloud Environment ◽  
Google Earth Engine ◽  
Time Series Images

scholarly journals Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR

2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Kaiyu Zhang ◽  
Xikai Fu ◽  
Xiaolei Lv ◽  
Jili Yuan
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Detection Methods ◽  
Sar Images ◽  
Assessment Index ◽  
Moment Maps ◽  
Marketing Planning ◽  
Frequency Map ◽  
Time Series Images

Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs.

scholarly journals Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine

2020 ◽  
Vol 12 (21) ◽  
pp. 3663
Author(s):  
Meinan Zhang ◽  
Huabing Huang ◽  
Zhichao Li ◽  
Kwame Oppong Hackman ◽  
Chong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Google Earth ◽  
Visual Interpretation ◽  
Entire Study ◽  
Heterogeneous Landscapes ◽  
Land Cover Map ◽  
Google Earth Engine ◽  
The Tropics ◽  
Sentinel 2

Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.

scholarly journals Variação da Linha de Costa na Região da Ilha dos Guarás – PA Através de Análise de Série Temporal de Imagens de Satélites (Coastline Changes Using Sattelite Images time series in Guarás Island Region – Pará State, Brazil)

2012 ◽  
Vol 4 (5) ◽  
pp. 922
Author(s):  
Luis Americo Conti
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Satellite Images ◽  
Coastal Protection ◽  
Island Region ◽  
Temporal Gis ◽  
High Resolution Satellite Images ◽  
Geospatial Tools ◽  
Identification And Characterization

Este trabalho apresenta uma analise da variação da linha de costa na região da Ilha dos Guarás, Município de Mariteua-PA ao longo das ultimas 3 décadas. Foi analisada uma seqüência de imagens de satélite de alta resolução da área e estabelecido um projeto em Sistema de Informações geográficas (SIG) visando a identificação e caracterização da linha de costa e estabelecendo padrões de mudanças. Os caso estudado mostrou que ferramentas geoespaciais como o “digital shoreline analyst” (DAS) podem ferramentas ser de grande importância na manipulação, análise e modelagem de dados costeiros, principalmente aplicados a estudos de conservação e monitoramento. A Ilha dos Guarás, em especial, apresentou um comportamento bastante peculiar com uma tendência de acreção nas porções laterais (Leste e Oeste) em oposição a uma tendência menos clara de erosão na porção central. Tais processos parecem ter sido mais determinantes na conformação da estrutura costeira da Ilha até o inicio da década de 2000 quando se tornou consideravelmente menos intenso.   Palavras Chave: Linha de costa, sensoriamento remoto, dinâmica costeira, modelagem  Coastline Changes Using Sattelite Images time series in Guarás Island Region – Pará State, Brazil  ABSTRACTThis work analyzes the coastline changes in the Ilha dos Guarás, Para State, Brazil. A sequence high resolution satellite images from 1985 to 2011 of the areas were analyzed in order to develop a systematic Geographic Information Systems (GIS) approach for the identification and characterization of the shoreline characteristics and how they change over the time. The main goal of our work is the proposal of a unified database to incorporate both spectral and spatial data in a temporal GIS framework. The examples analyzed showed that geospatial tools such as the Digital Shoreline Analyst (DSA) used could became a powerful tool for handling and analyzed data focused on the environmental monitoring and the coastal protection and conservation. The Guarás Island showed a peculiar dynamic with two areas of intense accretion in the flanks of the island in opposition of the central portion of the island submitted to a unstable erosional process. These processes were more severe until the beginning of the 21th century when it became considerable less intense.   Keywords: Coastline, Remote Sensing, Coastal dynamics, shoreline modelling

Assessments of preprocessing methods for Landsat time series images of mountainous forests in the tropics

2018 ◽  
Vol 23 (3) ◽  
pp. 139-148 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Shigejiro Yoshida
Keyword(s):  
Time Series ◽  
The Tropics ◽  
Time Series Images
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