A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa

CITATION: Zeng, H. et al. 2020. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 30: 397–409. doi:10.1007/s11769-020-1119-yThe original publication is available at https://www.springer.com/journal/11769This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.https://link.springer.com/article/10.1007/s11769-020-1119-yPublishers versio

Monitoring Rice Cropping Pattern and Fallows in Central and Western Part of India

India has the largest area under rice cultivation and holds the second position all over the world as it is one of the principal food crops. Rice-fallow croplands areas are those areas where rice is grown during the Kharif growing season (June- October) followed by fallow during Rabi season (November-February). These croplands are not suitable to grow in Rabi season rice due to their high water needs, but are suitable for short season (≤ 3months). According to national statistics there is an increase in the rice areas in Central and Western states of India. The goal of this project is to monitor the rice-fallow cropland areas & mapping the expansion of rice areas. This study is conducted in Central and Western states of India where different rice eco-systems exist. Time series Moderate Resolution Imaging Spectroradiometer (MODIS) 16days Normalized Difference Vegetation Index (NDVI) at 250m spatial resolution and season wise intensive ground survey data was used. We have applied hierarchical classification and Spectral Matching Techniques (SMT) to map rice areas and the fallows there after (rabi-fallows), in Central and Western states of India. And change detection was carried during 2000-2015 and 2010-2015. The resultant rice maps are compared with available national and sub-national level statistics

Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice

Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice

Uso de séries temporais Sentinel 1 na identificação de culturas agrícolas utilizando modelos de machine learning

Na área agrícola o Sensoriamento Remoto vem sendo uma opção de baixo custo, no entanto o aumento da disponibilidade de imagens com alta resolução espacial e temporal gratuitas, veio para contribuir de modo significativo para com esses estudos. Mais especificamente as imagens do RADAR/SAR Sentinel-1A e 1B, o qual é capaz de alcançar uma resolução temporal de até 6 dias. As imagens de RADAR são de fundamental importância para compreensão do comportamento de culturas agrícolas e sua identificação, uma vez que independem das condições atmosféricas, favorecendo a aquisição de imagens em quaisquer situações, resultando em séries temporais mais completas e refinadas. Neste estudo buscou-se avaliar o desempenho de três modelos de classificadores de Machine Learning, Random Forest (RF), Support Vector Machine (SVM) e K-Nearest Neighbor (KNN), utilizando séries temporais Sentinel-1/SAR, com a finalidade de identificar os tipos de culturas presentes na região do Panambi, Bahia, no período de safras que compreendem a 2016/2017, 2017/2018. Os procedimentos metodológicos consistiram no pré-processamento das imagens no Software Sentinel’s Application Platform (SNAP); empilhamento de imagens para construção do cubo temporal; filtragem espacial utilizando o método de Análise de Componentes de Densidade de Probabilidade (ACDP); técnicas de Transformação Minimum Noise Fraction (MNF) e MNF Invertido para extração do ruído na frequência das imagens e reconstrução da mesma; e classificação do cubo temporal. Os melhores resultados foram obtidos na filtragem para a polarização VH, com capacidade de melhor separar os alvos agrícolas e para o classificador KNN, alcançando um Kappa de 0,85 e um índice de Exatidão Global de 0,88, seguido do RF com 0,78 e 0,83 e então o SVM com o menor Kappa, 0,59 e 0,67 respectivamente, com melhores respostas na polarização VV. A imagens SAR possuem um alto potencial para identificação de culturas utilizando os modelos propostos em ambas as polarizações, com destaque para o KNN, alcançando uma acurácia geral neste estudo de 96,7%. Entretanto, mais estudos devem ser direcionados para estes fins utilizando imagens Sentinel-1/SAR, fazendo ainda, uso da junção de ambas as polarizações, VV e VH, para alcançar uma maior precisão nas classificações.CAPESIn agricultural field, Remote Sensing has been a low-cost option, however the increase in the availability of free and temporal high-resolution imagery has contributed significantly to these studies. For instance, the images from Sentinel-1A and 1B Synthetic-aperture radar (SAR) are capable of achieving a temporal resolution of up to 6 days. SAR images are pivotal for understanding the behavior of agricultural crops and their identification, since they are independent of atmospheric conditions, favoring the acquisition of images in any situation, resulting in more complete and refined time series. In this study, we evaluate the performance of three models of Machine Learning classifiers, Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), using Sentinel-1 time series, aiming to identify the types of crops present in the region of Panambi, Bahia, during the harvesting (2016/2017 and 2017/2018). We adopted the following methodological procedures: pre-processing the images in the Sentinel’s Application Platform (SNAP) Software; stacking images for the construction of the temporal cube; spatial filtering using the Probability Density Component Analysis (ACDP) method; Minimum Noise Fraction (MNF) and Inverted MNF Transformation techniques for extracting noises in the image frequency and reconstructing them; and classification of the temporal cube. Our best result was obtained in the filtering for the VH polarization, with the ability to better separate the agricultural targets and for the KNN classifier, reaching a Kappa coefficient of 0.85 and a Global Accuracy index of 0.88, followed by the RF with 0.78 and 0.83 and then the SVM with the lowest Kappa coefficient, 0.59 and 0.67 respectively, with better responses in the VV polarization. SAR images have a high potential for identifying cultures using the models proposed in both polarizations, with emphasis on KNN, reaching a general accuracy in this study of 96.7%. However, further studies should focus on these purposes, using Sentinel-1/SAR images and combining both polarizations (VV and VH) as a means to achieve greater accuracy in the classifications

Sistema informático cédula agroindustrial y gestión de la información estadística de la producción del arroz en la DRASAM, 2021

La investigación tuvo como objetivo general determinar la relación entre el sistema informático Cédula Agroindustrial y la gestión de la información estadística de la producción del arroz en la DRASAM, 2021. Estudio de tipo básica, nivel descriptivocorrelacional y diseño no experimental; con una muestra de 60 trabajadores de los molinos registrados; la recolección de datos se ejecutó la técnica de la encuesta, utilizando el cuestionario como instrumento. El trabajo concluyó que, el manejo del sistema informático Cedula Agroindustrial es regular (43%) evidenciándose gran dificultad en el tiempo que tarde el sistema para proveer información requerida, además, se identifica irregularidades en la relevancia de la información el personal que se encarga de su manejo y la carencia de características tecnológicas. Asimismo, la gestión de la información estadística de la producción del arroz es regular (47%) de igual manera sucede con el flujo de información y la gestión documentaria, ello ha dificultado muchas veces el análisis de la información y la toma de decisiones sobre la misma. Por último, el SICA se relaciona significativa (sig.=0.000) y considerablemente (rs=0.824) con la gestión de la información estadística de la producción del arroz

衛星リモートセンシングを用いた水稲作付圃場の広域マッピング手法の開発

Tohoku University課

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research