Land Cover Classification Map Research Articles

Added utility of temperature zone information in remote sensing-based large scale crop mapping

Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. When making large-scale crop classification, regional harvest time and phenological growth differences occur due to varying temperatures along the study area, and using regional temperature differences while performing crop cover classification may increase the map accuracy. Therefore, in this study, we investigated for the first time the contribution of temperature information over large areas to the classification of agricultural products. Agricultural crop mapping is performed over Türkiye using Sentinel-2 Level-2A images with 10-m spatial resolution acquired between March 15, 2019, and October 15, 2019. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-m temperature data is averaged to divide the study area into three temperature zones Low (LTZ), Medium (MTZ), and High-Temperature Zone (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and an OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes.

Feature Optimization-Based Machine Learning Approach for Czech Land Cover Classification Using Sentinel-2 Images

The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic. This study aims to develop a practical approach for land cover classification in the Czech Republic, with the goal of efficiently acquiring spatial distribution information regarding its forest resources. This approach is based on multi-level feature extraction and selection, integrated with advanced machine learning or deep learning models. To accomplish this goal, the study concentrated on two typical experimental regions in the Czech Republic and conducted a series of classification experiments, using Sentinel-2 and DEM data in 2018 as the main data sources. Initially, this study extracted various features, including spectral, vegetation, and terrain features, from the study area, then assessed and selected key features based on their importance. Additionally, this study also explored multi-level spatial contextual features to improve classification performance. The extracted features include texture and morphological features, as well as deep semantic information learned by utilizing a deep learning model, 3D CNN. Finally, an AdaBoost ensemble learning model with the random forest as the base classifier is designed to produce land cover classification maps, thus obtaining the spatial distribution of forest resources. The experimental results demonstrate that feature optimization significantly enhances the extraction of high-quality features of surface objects, thereby improving classification performance. Specifically, morphological and texture features can effectively enhance the discriminability between different features of surface objects, thereby improving classification accuracy. Utilizing deep learning networks enables more efficient extraction of deep feature information, further enhancing classification accuracy. Moreover, employing an ensemble learning model effectively boosts the accuracy of the original classification results from different individual classifiers. Ultimately, the classification accuracy of the two experimental areas reaches 92.84% and 93.83%, respectively. The user accuracies for forests are 92.24% and 93.14%, while the producer accuracies are 97.71% and 97.02%. This study applies the proposed approach for nationwide classification in the Czech Republic, resulting in an overall classification accuracy of 90.98%, with forest user accuracy at 91.97% and producer accuracy at 96.2%. The results in this study demonstrate the feasibility of combining feature optimization with the 3D Convolutional Neural Network (3DCNN) model for land cover classification. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features.

Abandoned cropland mapping and its influencing factors analysis: A case study in the Beijing-Tianjin-Hebei region

Abandoned cropland (AC) is one of the important types of land cover and land use change that poses a threat to food security, nature conservation, and ecosystem protection. Despite its significance, detailed spatial distribution patterns of AC across expansive geographic regions remain under-explored. Therefore, this study 1) proposes a novel framework designed to precisely determine the timing and extent of AC, and evaluates its effectiveness in the Beijing-Tianjin-Hebei region from 2000 to 2020; 2) analyses the spatial and temporal evolution of AC; 3) investigates the relationship between AC and both physical geographic and socio-economic factors. The proposed AC recognition framework integrates the method of change detection. Initially, regions changing are pinpointed, followed by categorizing pre- and post-change images to ascertain the magnitude and timing of these shifts, enabling the pinpointing of AC locations and their temporal onset. The results showed that the accuracy of land cover classification mapping ranged from 80.83% to 99.08%. Specifically, the user's accuracy of the proposed method varied between 79.29% and 89.38%. We discerned that AC was mainly distributed in the northwest water conservation belt, adopting an inverted “L” pattern. Additionally, physical geographic factors predominantly influenced AC. The use of remote sensing plays a crucial role in assessing AC, which is of great significance for understanding the evolution of abandoned landscapes and analyzing the relationship between abandonment and geology, environment, and socio-economy.

Use of machine learning-based classification algorithms in the monitoring of Land Use and Land Cover practices in a hilly terrain.

The current high rate of urbanization in developing countries and its consequences, like traffic congestion, slum development, scarcity of resources, and urban heat islands, raise a need for better Land Use Land Cover (LULC) classification mapping for improved planning. This study mainly deals with two objectives: 1) to explore the applicability of machine learning-based techniques, especially the Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm as the potential classifiers for LULC mapping under different scenarios, and 2) to prepare a better LULC classification model for mountain terrain by using different indices with combination of spectral bands. Due to differences in topography, shadows, spectral confusion from overlapping spectral signatures of different land cover types, and a lack of access for ground verification, classification in mountainous terrain is difficult task compared to plain terrain classification. An enhanced LULC classification model has been designed using two popular machine learning (ML) classifier algorithms, SVM and RF, explicitly for mountainous terrains by taking into consideration of a study area of Gopeshwer town in the Chamoli district of Uttarakhand state, India. Online-based cloud platform Google Earth Engine (GEE) was used for overall processing. Four classification models were built using Sentinel 2B satellite imagery with 20m and 10m resolutions. Two of these models (Model 'i' based on RF algorithm and Model 'ii' based on SVM algorithm) were designed using spectral bands of visible and infrared wavelengths, and the other two (Model 'iii' based on RF algorithm and Model 'iv' based on SVM algorithm) with the addition of indices with spectral bands. The accuracy assessment was done using the confusion matrix based on the output results. Obtained result highlights that the overall accuracy for model 'i' and model 'ii' were 82% and 86% respectively, whereas these were 87.17% and 87.2% for model 'iii' and model 'iv' respectively. Finally, the study compared the performance of each model based on different accuracy metrics for better LULC mapping. It proposes an improved LULC classification model for mountainous terrains, which can contribute to better land management and planning in the study area.

Regulating ecosystem services in a local forest: Navigating supply, trade-offs, and synergies

The management of ecosystem service supply in local forests is closely linked to the ecological well-being of residents. This study aimed to estimate the supply, tradeoffs, and synergies of local forest-regulating ecosystem services using a land cover classification map (LCCM) and a forest type map (FTM). Rigorous literature reviews and expert Delphi analyses were conducted to elicit a list of key regulating services and evaluate the potential of each ecosystem type and category of the 1:5,000 LCCM and FTM to provide the selected services. The Getis-Ord Gi* analysis was combined with the land-sue scoring method to propose a method for estimating the supply, trade-offs, and synergies of local forest ecosystem services. The results revealed that the ranking order (1st to 5th) of the supply of regulating ecosystem services was as follows: erosion prevention, air quality regulation, heat island mitigation, water quality regulation, and carbon storage. When analyzing the correlation between the defined services of the entire city, almost all services showed a synergistic effect. However, when analyzed locally, spatial tradeoffs (between heat island mitigation and air quality regulation; carbon storage and air quality regulation; water quality regulation and air quality regulation) were observed in the eastern and northwestern forest areas. This suggests the need to consider not only the synergies and trade-offs between specific ecosystem services at the regional level but also at the local level when managing regulating ecosystem services of local forests. The results of this study can serve as important baseline references for planners and policymakers during the planning phase of policies aimed at managing the supply of regulating ecosystem services for forests with restricted access. Moreover, the study results can help refine the estimation of the supply of regulating ecosystem services by integrating additional data sources.

Assessment of Land Cover Status and Change in the World and "the Belt and Road" Region from 2016 to 2020.

The assessment of land cover and changes will help to understand the temporal and spatial pattern of land cover in the world and the Belt and Road (B&R) region, and provide reference information for global sustainable development and the Belt and Road construction. In this paper, the 1 km global land cover classification maps of 2016 and 2020 with a high accuracy of 88% are mapped using the Moderate Resolution Imaging Spectroradiometer (MODIS) time series surface reflectance products. Based on the maps, the land cover status of the world and the Belt and Road region, the land cover change from 2016 to 2020, and the mutual transformation characteristics between various types, are analyzed. The research results indicate that from 2016 to 2020, the global change rates of cropland, forest, grassland, and impervious surface are 0.25%, 0.22%, 0.08% and 3.41%, respectively. In the Belt and Road region, the change rates of cropland, forest, grassland, and impervious surface are 0.42%, 0.60%, -0.55% and 2.98% respectively. The assessment results will help to clarify the spatial pattern of land cover change in the five years from 2016 to 2020, so as to provide valuable scientific information for the global realization of sustainable development goals and the construction of the B&R.

The effects of climate and forest cover variability on the hydrological regulation of an eastern Andean Cusiana river sub-basin

The hydrologic regulation analysis on a basin scale requires the understanding of the spatio-temporal heterogeneity of the climate regime and the land cover. The Cusiana river sub-basin (4992 km2) is part of the Colombian Orinoco River basin and its important because of the ecosystem services it provides. However, there is limited knowledge about the factors that govern its hydrological functioning. In this study we aimed at describing the relation between the forest cover and the hydroclimatic tendencies with the hydrologic regulation of this basin. For this, we evaluated rainfall and evapotranspiration spatial variability and the temporal tendencies of these variables and streamflow employing the Mann-Kendall test for a dataset covering from 1980 till 2020. Streamflow data was obtained from various gauging sites at different points along the main streambed between 290 and 3135 m a.s.l. We also evaluated the basin's forest cover changes between 2003 and 2019 using official land cover classification maps. With the hydroclimatic and land cover analyzes we created potential scenarios of extreme dry and wet years and of deforestation and restoration. These extreme scenarios were forced in the previously calibrated and validated Seasonal Water Yield module of the InVEST model to evaluate their effects on base and quickflows. Results show that there are no homogeneous tendencies in the rainfall, evapotranspiration, or streamflow within this basin. Furthermore, streamflow tends to decrease in the basin segments that show an increase in forest cover. The model outputs suggest that the magnitude of the baseflow decrease under a deforestation scenario is up to 3.6 times larger than the increase in baseflow under a forest cover restoration scenario, and that the quickflows can increase up to 1.4 times more in a combined wet year and deforestation scenario, than in an average year under current forest cover. From our results we identify that the spatial relation between land cover, climate and hydrologic regulation must be explicitly evaluated for the design of water resources management plans for any Andean River basin.

Patch‐based nonlocal estimation of scattering mechanism vector in PolInSAR

AbstractNowadays, polarimetric interferometric synthetic aperture radar (PolInSAR) has attracted increasing attention for the simultaneous acquisition of scattering and terrain information. The scattering mechanism vectors corresponding to the optimal coherences have shown great effect in land cover classification and forest mapping. To extract scattering mechanism vectors, different optimization algorithms are proposed but always accompanied with spatial discontinuities. This letter proposes a method to optimize scattering mechanism vectors based on nonlocal estimations. A pixel‐by‐pixel optimization model is established by calculating patch‐based nonlocal similarity, which effectively reduces the uncertainty of scattering mechanism vector estimation while maintaining high resolution. The phase recovery experiment is conducted on simulated data and the root mean square errors (RMSEs) of three algorithms are compared. The proposed method performs better with a RMSE of 0.22 in the whole image, against 0.25 and 1.27 for other algorithms. The recovery results in the target region (RMSE 0.11 against 0.21 and 0.99) and linear edge region (RMSE 0.26 against 0.49 and 1.52) also indicate strong ability in detail preservation. Then the observed ESAR data is utilized for further assessment. It demonstrates that the proposed method has a significant improvement to reduce singularities and distinguish artificial buildings.

A comparative study of different deep learning models for land use and land cover mapping of flood detention basin

Flood detention basin (FDB) is an important part of the flood control system in the basin. It is of great significance for scientific flood control to obtain the land use and land cover (LULC) classification map of FDB with higher accuracy quickly and accurately. In recent years, deep learning has shown great potential in LULC classification. In this study, we make LULC training dataset and explore three state-of-the-art (SOTA) DL architectures: Unet++, ResUnet++, DeepLab v3+ across Mengwa FDB. The experiments show all methods used in this study are available for LULC classification, which overall accuracy is ResUnet++(95.11%), Unet++(91.92%), DeepLab v3+ (91.04%) and kappa coefficient is ResUnet++(0.91), Unet++(0.85), DeepLab v3+ (0.83). Deep learning methods can be used for automatic extraction of LULC from high-resolution satellite images, providing data support for economic loss assessment and post disaster construction in flood storage areas.

Physiographic Study for Hydrology of Embung Hendrosari Region, Gresik Regency

This study aims to determine physiographic characteristics based on the slope and land use which affect water control in Embung Hendrosari area, Gresik. The data used in this study are DEM data, Landsat 8 image data, and river data. The slope data processing is carried out through slope classification analysis. The Landsat 8 data processing is used for land use classification analysis. The slope classification maps and land use classification maps are used to classify flow coefficients. There are five land classifications obtained in land cover classification map including agriculture, settlements, industrial areas, plantations, and waters. There are 5 classifications obtained on the slope classification map, including flat, sloping, slightly steep, steep and very steep. The flow coefficient analysis is performed from the combination of land use data and slope data. The flow coefficient is influenced by the characteristics of the land cover and the slope of the slope. The average flow coefficient of Embung Hendrosari area is 0.31, which classified as medium flow coefficient. This indicates that 31% of the water is run off on the ground surface and 69% of the water is absorbed below the soil surface. There are changes in land function in Embung Hendrosari area, for example, agriculture area which changed into settlement and/or industry, which will significantly affect the value of the flow coefficient. The change in land use causes more water to run off the surface. This runoff water management requires a water reservoir that can be directed toward reservoir. The results of this study show such an initial step in hydrological engineering by its development, improvement, utilization, and control of water flow by utilizing the function of Hendrosari Gresik reservoir.

Evaluation of typhoon-induced inundation losses associated with LULC using multi-temporal SAR and optical images

We utilized a two-branch end-to-end network (MultiSenCNN) for land use and land cover (LULC) classification and flood event mapping using multispectral (MS), panchromatic (Pan) and synthetic aperture radar (SAR) images, where flooding was induced by typhoon Lekima in August 2019. Flood damages were assessed by considering both the LULC and flood maps. We defined three strategies to compare the MS + SAR and MS + Pan images to demonstrate the ability of the MultiSenCNN algorithm for LULC classification. The three strategies yielded an average overall accuracy of ∼98% and an average Kappa of ∼0.98 for LULC classification. The overall accuracy of the fused MS + SAR images is slightly higher than the MS + Pan images when using the same model training samples. The flood mapping shows an overall accuracy of 97.22% and a Kappa of 0.94, with a flood inundation area of 101 km2 that mainly inundated cropland and urban areas. Compared to other LULC types, the flooded cropland has caused more loss of ecosystem service values during typhoon Lekima, accounting for 81.19% of the total. Using SAR mages can well monitor the start/end states of flood events and the inundated areas, providing the flood status information to rescuers and governments for making timely decisions.

Surface runoff estimation using geographic information system and soil conservation service-curve number method for sub catchments of Karamadai, Tamil Nadu

Water conservation becomes essential as the resource becomes scarcer. The most important step in managing water resources is estimating watershed runoff generated from rainfall, as the runoff and rainfall are the key factors in determining water availability for surface storage and groundwater recharge. So, this study is mainly focused on estimating the surface runoff generated from the three sub-catchments of Karamadai, Tamil Nadu, India, using the heavy to extreme daily rainfall events received in the study area within the span of 20 years (2000–2019). The study was performed in the ArcGIS environment using remote sensing data. The SCSCN (Soil Conservation Service-Curve Number) method was used to estimate surface runoff. The changes in the land use in each sub-catchment were analysed in each decade and studied for their impact on the runoff depth. The land use and land cover classification map of the study area was prepared from LISS III satellite imagery for the years 2006 and 2016 by using supervised classification. The curve number was assigned based on land use as well as the hydrologic soil group. The weighted curve number was calculated from the area under each land use and then used to calculate storm runoff. The maximum runoff occurred in 2011 in all the catchments of the Karamadai block. It was found that more runoff occurred in the Mandrai Pallam catchment compared to Periya Pallam and Pare Pallam, as the Mandrai Pallam catchment had less soil moisture retention capacity than the other two catchments. So, more priority must be given to this catchment while planning to implement the soil and water conservation measures.

Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models

Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration estimation has been designed. First, instead of using single Landsat scenes, the utilization of multiple Landsat scenes of the same location over time is considered. Second, to generate a preferred feature set as input to a regression model, a number of feature extraction methods are motivated and compared. Third, to find a preferred regression model, a variety of approaches are implemented and compared for accuracy. In this research, 11 Landsat-8 images from 2013 to 2017 of Gulin County, Sichuan China, and 138 soil samples with lab-measured Cu concentrations collected from the area in 2015 are used. A variety a metrics under cross-validation are used for comparison. The results indicate that multi-temporal images increase accuracy compared to single Landsat images. The preferred feature extraction varies based on the regression model used; however, the best results are obtained using support vector regression and the original data. The final soil Cu map generated using the recommended data processing pipeline shows a consistent spatial pattern with a ground-truth land cover classification map. These results indicate that machine learning has the ability to perform large-scale soil heavy metal mapping from widely available satellite remote sensing images.

Investigating the changes in agricultural land use and actual evapotranspiration of the Urmia Lake basin based on FAO’s WaPOR database

The Urmia Lake Basin (ULB) is a critical region in terms of water and environment in Iran. The purpose of this study was to investigate the relationship between agricultural land-use changes and the decrease of the Lake Urmia water level, calculating the actual evapotranspiration and interception (ETIa) in agricultural and non-agricultural lands, investigating the trend of temperature changes and its relationship with ETIa, determining the water balance, and estimating net and gross biomass water productivity in the ULB. So, Land Cover Classification (LCC) maps, ETIa and water productivity were prepared from FAO’s WaPOR product (FWP) from 2009 to 2020. Daily precipitation and temperature were collected from 24 meteorological stations in the ULB. To validate FWP land cover classification, 354 control points were collected and the error matrix and Kappa coefficient were used. Then, based on the ETIa and LCC estimated by FWP, the volume of water consumed in irrigated, rainfed, fallow and non-agricultural lands, as well as the water balance of the basin, was calculated. The overall accuracy (OA) and Kappa coefficient for LCC were 86% and 0.81, respectively. The results showed that until 2013, the area of irrigated and rainfed agricultural lands had been increased by 48%, which could be regarded as an influential factor in reducing the water level of Lake Urmia. The average annual precipitation and ETIa of the basin were 15.24 × 109 and 15.91 × 109 m3, respectively. Also, the average annual ETIa of agricultural lands, grasslands and evaporation from the surface of Lake Urmia were 4.88 × 109, 8.18 × 109 and 2.85 × 109 m3, respectively. Therefore, a negative balance of 0.67 × 109 m3 was observed. The results also showed that the average annual temperature had been increased by 1.1 °C from 2009 to 2020. Finally, the rise of the temperature and area of irrigated croplands, the decrease of precipitation and the negative water balance should be considered as warning signals calling for the proper management of water resources.

Applying multispectral UAV imagery to delineate in and near stream cover along a small urban stream

AbstractTraditional remote sensing methods are rarely applied to headwater streams because of a mismatch in spatial scale. With improved technology, remote sensing using unoccupied aerial vehicles enables imagery to be collected at high spatial and temporal resolution. However, these innovations have been used less frequently to detect changes within the water column. In addition, it is unclear whether classification methods developed along a single reach can be transferred through space (to other reaches) and through time, or how much information is needed to perform such classifications. This study combines methods of remote sensing, image processing, and machine learning to classify land cover and submerged aquatic vegetation along four urban stream reaches (170–325 m). A linear discriminant analysis (LDA) model was developed to provide land and water cover classification maps using training data. This method proved to be robust when classifying land cover along a single reach with minimal training data required. Using 200 pixels per land cover class resulted in 100% classification confidence for more than 93% of the pixels; when reducing this to only 50 pixels per land cover class, LDA yielded 100% classification confidence for nearly 80% of the total pixels. Through attempts to transfer relationships across reaches and flight dates, we found a greater percentage of misclassified pixels (upwards of 45% misclassification for flights on one date) when classifying across reaches on a single date as opposed to classification along a single reach for multiple dates. Overall, we recommend passive optical approaches, like the one used here, consider lighting conditions, reach orientation, and shading in data acquisition planning to mitigate the uncertainty they may introduce in delineating water column quality and cover.

Combination of Time Series of L-, C-, and X-Band SAR Images for Land Cover and Crop Classification

The availability of new Earth observation satellites operating radar sensors at different frequencies enables the combination of multiple dimensions of the data (time, frequency, polarimetry and interferometry) in many applications. Image classification is expected to benefit from the diversity of observation. This work illustrates classification experiments carried out with series of images acquired by ALOS-2 PALSAR (L-band), Sentinel-1 (C-band) and TanDEM-X (X-band) in two application domains: land cover classification and crop-type mapping. Their usage, both separately and in combination, serves to identify the complementarity of information. In this work we propose a new colour representation of the pair-wise class separability in the case of using three frequency bands, which help identify which bands (or combinations of them) provide the best performance. Results in terms of accuracy scores (overall and class-specific) show that the use of the three frequency bands always outperforms the individual bands and their pairs. In addition, for both land classification and crop-type mapping the accuracy of using coherence time series is lower than the one obtained with the intensity time series, but there is complementarity in terms of sensitivity when both coherence and intensity time series are used together. The classes which are most benefited at each particular case of study have been identified. Finally, a partial trade-off has been found between the use of multiple frequency bands and the length of the available time series.

VALIDATION OF LAND DEGRADATION CARDS ON THE BASIS OF GEOSPATIAL DATA

Today there is a lot of satellite data and products based on it in the public domain. By integrating them with heterogeneous socio-economic information and soil maps, model biophysical data using modern machine learning methods and modern approaches to geospatial data processing, it becomes possible to create maps of land degradation. Considering that classification maps, productivity maps and deforestation maps are the main intellectual components to create a degradation map, it is these products that affect the overall reliability of the results. For their validation, the necessary quality metrics are determined in the work, and the corresponding calculations are made. To evaluate the land cover map, independent test data were used to build a confusion matrix, and the obtained areas of the main crops were compared with statistical data. Agricultural land productivity was estimated using time series land cover classification maps and Crop Growth Modeling System (CGMS) biophysical plant development, as well as biophysical plant growth parameters using satellite data and biophysical plant development models. The LAI Map Accuracy Assessment (based on CGMS) is based on the comparison of Leaf Area Index (LAI) values modeled using the CGMS software framework with LAI ground measurement data collected through ground surveys. Numerous experiments were carried out to assess the quality of models and the results of deforestation maps on an independent test sample, which was not used at the neural network training stage. Degradation maps for several years were also analyzed and their validation was carried out with respect to productivity, in particular for the region that has undergone significant changes for the territory of Ukraine.

Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling

Abstract In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.

Regional Physiographic Study for the Hydrology of Kali Lamong Watershed Area

This study aims to determine the effect of physiography based on slope and land cover for water control in Kali Lamong watershed. The data used in this research are DEM data and Landsat 8 imagery data. The process of processing slope data is through conversion coordinates system, DEM clip, create slope, reclassify, dissolve shapefile, and slope classification analysis. Landsat 8 data processing goes through a process through conversion coordinates system, composite band, crop composite, extent shapefile, sharpen band, unsupervised classification, and land cover classification analysis. Slope classification maps and land cover classification maps are used for flow coefficient classification for physiographic analysis based on slope and land cover for water control in Kali Lamong watershed. On the land cover classification map, five land classifications were obtained, namely agriculture (158413000 m2), settlements (72701400 m2), industrial land (11571600 m2), plantations (46017800 m2), and waters (15268500 m2). On the slope classification map obtained 5 classifications, as flat with a slope of 0-8% (288469544 m2), as slope with a slope of 8-15% (7656738 m2), as rather steep with a slope of 15-25% (1905360 m2), as steep with a slope of 25-45 (526614 m2), and as very steep with a slope of more than 45% (32148 m2). From the combination of Landsat 8 image data and slope data, flow coefficient analysis was carried out. The flow coefficient is influenced by land cover and slope. From this research, the classification of low flow coefficient is less than 0.25, medium flow coefficient is 0.25-0.5, and high flow coefficient is more than 0.75. The average flow coefficient of Kali Lamong watershed is 0.49 with a moderate flow coefficient classification value. This shows that 49% of the runoff water is in Kali Lamong watershed. The higher the flow coefficient value, the water runs off the surface. So that it can be used as an initial study for the technical planning of Kali Lamong hydrology and the development, improvement, utilization, and control of water flow in Kali Lamong.

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

Timely and accurate rice distribution information is needed to ensure the sustainable development of food production and food security. With its unique advantages, synthetic aperture radar (SAR) can monitor the rice distribution in tropical and subtropical areas under any type of weather condition. This study proposes an accurate rice extraction and mapping framework that can solve the issues of low sample production efficiency and fragmented rice plots when prior information on rice distribution is insufficient. The experiment was carried out using multitemporal Sentinel-1A Data in Zhanjiang, China. First, the temporal characteristic map was used for the visualization of rice distribution to improve the efficiency of rice sample production. Second, rice classification was carried out based on the BiLSTM-Attention model, which focuses on learning the key information of rice and non-rice in the backscattering coefficient curve and gives different types of attention to rice and non-rice features. Finally, the rice classification results were optimized based on the high-precision global land cover classification map. The experimental results showed that the classification accuracy of the proposed framework on the test dataset was 0.9351, the kappa coefficient was 0.8703, and the extracted plots maintained good integrity. Compared with the statistical data, the consistency reached 94.6%. Therefore, the framework proposed in this study can be used to extract rice distribution information accurately and efficiently.