APPLICATION OF LAPAN A3 SATELLITE DATA FOR THE IDENTIFICATION OF PADDY FIELDS USING OBJECT BASED IMAGE ANALYSIS (OBIA)

The role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed.

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Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110663 ◽

2020 ◽

Vol 9 (11) ◽

pp. 663

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Raihan Rafif ◽

Siti Saringatin ◽

Pramaditya Wicaksono

Keyword(s):

Spatial Data ◽

Vegetation Index ◽

Google Earth ◽

Paddy Fields ◽

Series Data ◽

Landsat 8 ◽

Spectral Indices ◽

Seamless Integration ◽

Synthetic Aperture Radar Data ◽

Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

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Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

Hrvatski geografski glasnik/Croatian Geographical Bulletin ◽

10.21861/hgg.2021.83.02.01 ◽

2021 ◽

Vol 83 (2) ◽

pp. 7-31

Author(s):

Josip Šetka ◽

◽

Petra Radeljak Kaufmann ◽

Luka Valožić ◽

◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Agricultural Land ◽

Confusion Matrix ◽

Hybrid Approach ◽

Land Cover Changes ◽

Landsat 8 ◽

Economic Changes ◽

Sparse Vegetation ◽

Landsat 7

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

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The Face of the Banjarbaru City Wetlands in Last Four Decades

Journal of Wetlands Environmental Management ◽

10.20527/jwem.v6i2.183 ◽

2019 ◽

Vol 6 (2) ◽

pp. 130

Author(s):

Syam'ani Syam'ani ◽

Abdi Fithria ◽

Eva Prihatiningtyas

Keyword(s):

Spatial Distribution ◽

Spatial Data ◽

Central Government ◽

Reduction Rate ◽

Schedule Algorithm ◽

Landsat 8 ◽

Linear Pattern ◽

The Past ◽

Object Based ◽

The Face

The change of Banjarbaru city status into the central government of South Kalimantan Province, has the potential to increase the need for land. This directly affects wetlands conversion activities into other forms of land closure. This research aims to map the spatial distribution of wetlands, and the spatial distribution of wetlands conversion existing in Banjarbaru City in every decade over the last four decades, ie from the 1970s to the present. Wetlands spatial data are extracted from multitemporal satellite imagery, Landsat 5 in 1973, Landsat 5 in 1989, Landsat 5 in 1997, Landsat 5 in 2007, and Landsat 8 in 2016. The method used to extract wetlands is Object Based Image Analysis (OBIA), with Full Lambda-Schedule algorithm. The research results show that over the past last decades, the total area of Banjarbaru City's wetlands has been reduced continuously. The average total reduction rate is 534.53 hectares per decade or about 53.5 hectares per year, with a linear pattern over the past four decades.

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Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

Remote Sensing ◽

10.3390/rs12030579 ◽

2020 ◽

Vol 12 (3) ◽

pp. 579

Author(s):

Athos Agapiou

Keyword(s):

Spatial Resolution ◽

Near Infrared ◽

Archaeological Site ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Object Based Image Analysis ◽

Segmentation Process ◽

Object Based ◽

Level 2 ◽

Sentinel 2

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.

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

10.21203/rs.3.rs-760171/v1 ◽

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.

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UAV, a Farm Map, and Machine Learning Technology Convergence Classification Method of a Corn Cultivation Area

Agronomy ◽

10.3390/agronomy11081554 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1554

Author(s):

Dong-Ho Lee ◽

Hyeon-Jin Kim ◽

Jong-Hwa Park

Keyword(s):

Machine Learning ◽

Agricultural Land ◽

Object Classification ◽

Kappa Coefficient ◽

Paddy Fields ◽

Classification Method ◽

Wide Area ◽

Object Based ◽

Uav Images ◽

F Measure

South Korea’s agriculture is characterized by a mixture of various cultivated crops. In such an agricultural environment, convergence technology for ICT (information, communications, and technology) and AI (artificial intelligence) as well as agriculture is required to classify objects and predict yields. In general, the classification of paddy fields and field boundaries takes a lot of time and effort. The Farm Map was developed to clearly demarcate and classify the boundaries of paddy fields and fields in Korea. Therefore, this study tried to minimize the time and effort required to divide paddy fields and fields through the application of the Farm Map. To improve the fact that UAV image processing for a wide area requires a lot of time and effort to classify objects, we suggest a method for optimizing cultivated crop recognition. This study aimed to evaluate the applicability and effectiveness of machine learning classification techniques using a Farm Map in object-based mapping of agricultural land using unmanned aerial vehicles (UAVs). In this study, the advanced function selection method for object classification is to improve classification accuracy by using two types of classifiers, support vector machine (SVM) and random forest (RF). As a result of classification by applying a Farm Map-based SVM algorithm to wide-area UAV images, producer’s accuracy (PA) was 81.68%, user’s accuracy (UA) was 75.09%, the Kappa coefficient was 0.77, and the F-measure was 0.78. The results of classification by the Farm Map-based RF algorithm were as follows: PA of 96.58%, UA of 92.27%, a Kappa coefficient of 0.94, and the F-measure of 0.94. In the cultivation environment in which various crops were mixed, the corn cultivation area was estimated to be 96.54 ha by SVM, showing an accuracy of 90.27%. RF provided an estimate of 98.77 ha and showed an accuracy of 92.36%, which was higher than that of SVM. As a result of using the Farm Map for the object-based classification method, the agricultural land classification showed a higher efficiency in terms of time than the existing object classification method. Most importantly, it was confirmed that the efficiency of data processing can be increased by minimizing the possibility of misclassification in the obtained results. The obtained results confirmed that rapid and reliable analysis is possible when the cultivated area of crops is identified using UAV images, a Farm Map, and machine learning.

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Remote Sensing for Spatio-temporal Mapping of Land surface temperature and Surface Energy Fluxes in the Bouregreg-Chaouia Region of Morocco

Journal of Environmental and Agricultural Studies ◽

10.32996/jeas.2021.2.1.4 ◽

2021 ◽

Vol 2 (1) ◽

pp. 23-35

Author(s):

Abdelouhed Farah ◽

Ahmed Algouti ◽

Abdellah Algouti ◽

Mohammed Ifkirne ◽

Abdellatif Rafik

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Spatial Data ◽

Land Surface ◽

Agricultural Land ◽

New Technologies ◽

Urban Expansion ◽

Environmental Parameters ◽

Landsat 8 ◽

The Impact

In recent decades, the Bouregreg Chaouia region has been subject to urban growth and a reduction in agricultural land in this region, which has changed its environmental variables and made it vulnerable to climate change. This work raises the spatiotemporal monitoring of land use and certain environmental parameters (vegetation cover, albedo, surface temperature from 1987 to 2015 by exploring intelligent spatial data in the region. The remote sensing products were computed from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 Oli/TIRS images obtained during the dry seasons 1987, 2000 and 2015. The results showed a reduction in NDVI vegetation index (∼0.86 in 1987 to ∼0. 56 in 2000 to ∼ 0.54 in 2015) and with an increase in surface albedo (0.51 in 1987 to 0.52 in 2000 to 0. 69 in 2015), temperature (∼67°C in 1987 to 54°C in 2000 to 40°C in 2015) and to understand the impact of urbanization on the variation of environmental parameters, the evolution of the built-up area has been followed as a determining factor. However, it recorded 3.27% surface area in 1987 to 7.45% in 2000 to 28.18% in 2015. Indeed, the contribution of new technologies (GIS and remote sensing) is essential for better management and monitoring of the impact of urban expansion on the state of the environment. The results obtained remain so promising and highlight the contribution and feasibility of intelligent spatial data to assess the evolution of the urban environment on a large scale.

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Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

Journal of Geographical Studies ◽

10.21523/gcj5.19030102 ◽

2020 ◽

Vol 3 (1) ◽

pp. 11-23 ◽

Cited By ~ 3

Author(s):

Abdulla Al Kafy ◽

Abdullah Al-Faisal ◽

Mohammad Mahmudul Hasan ◽

Md. Soumik Sikdar ◽

Mohammad Hasib Hasan Khan ◽

...

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface ◽

Agricultural Land ◽

Global Climate ◽

Urban Expansion ◽

Water Bodies ◽

Landsat 8 ◽

Lulc Changes ◽

The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

Remote Sensing ◽

10.3390/rs13122299 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2299

Author(s):

Andrea Tassi ◽

Daniela Gigante ◽

Giuseppe Modica ◽

Luciano Di Martino ◽

Marco Vizzari

Keyword(s):

Land Cover ◽

National Park ◽

Time Window ◽

Central Italy ◽

Google Earth ◽

Data Cube ◽

Landsat 8 ◽

Change Analysis ◽

Object Based ◽

Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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