Classification and mapping of paddy rice by combining Landsat and SAR time series data

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach

Land Change History of Oil Palm Plantations in Northern Bengkulu Province, Sumatra Island, Reconstructed from Landsat Satellite Archives

The aim of this study is to reconstruct the history of land conversion to oil palm plantation in tropical Asia using multi-temporal satellite data. A new method was constructed with a newly developed computer model, Land Change Detection and Land Definition Model (LC/LD Model) to map out spatio-temporal distribution of land changes. A comprehensive, cloud-free Landsat dataset was created from all the available Landsat data from 1988 to 2015. The pixel-based dataset was converted into a polygon-based dataset by applying the multi-temporal image segmentation method. The representation of the spectral information was also reduced to a single index of IB45, the ratio of the near-infrared (Band 4) to mid-infrared (Band 5) bands, which was the most suitable index for detecting and tracking the transformation of land to oil palm plantation. To extract targeted land changes and land uses from a given temporal profile, land change scenarios were assumed and temporal segmentation method was developed for Land Change Detection Model (LCM). The segmented profiles were then evaluated by using bio-physical metrics in the Land Definition Model (LDM) to determine the land uses. The two-tiered LC/LD Model could detect not only large-scale land changes caused by private companies but also small-scale changes caused by smallholders, which is supposedly the most uncertain factor for the future development of oil palm plantation. Relationships between local factors and two land change phenomena, conversion to oil palm plantation and deforestation, were investigated using quantitative assessments such as Logistic Regression analysis. The results explicitly showed the positive impacts of proximities to 1) pre-existing oil palm plantations and 2) nearest mill and negative impacts of 1) elevation and 2) slope on the occurrence of small oil palm plantations. These findings strongly imply that oil palm development in the neighborhood initiated further development in nearby areas. The accessibility to mills also increased the chance of oil palm development. From topographical aspects, flat and low altitude land was more favored than steep and high altitude one. The results also indicated that large size enterprise plantations were more responsible for directly converting untouched natural land than smallholders and were the main contributor to deforestation. In contrast, smallholders mainly converted preexisting farmland to oil palm plantations

Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice

The comprehensive relationship of backscattering coefficient (σ0) values from two current X-band SAR sensors (COSMO-SkyMed and TerraSAR-X) with canopy biophysical variables were investigated using the SAR images acquired at VV polarization and shallow incidence angles. The difference and consistency of the two sensors were also examined. The chrono-sequential change of σ0 in rice paddies during the transplanting season revealed that σ0 reached the value of nearby water surfaces a day before transplanting, and increased significantly just after transplanting event (3 dB). Despite a clear systematic shift (6.6 dB) between the two sensors, the differences in σ0 between target surfaces and water surfaces in each image were comparable in both sensors. Accordingly, an image-based approach using the “water-point” was proposed. It would be useful especially when absolute σ0 values are not consistent between sensors and/or images. Among the various canopy variables, the panicle biomass was found to be best correlated with X-band σ0. X-band SAR would be promising for direct assessments of rice grain yields at regional scales from space, whereas it would have limited capability to assess the whole-canopy variables only during the very early growth stages. The results provide a clear insight on the potential capability of X-band SAR sensors for rice monitoring

Oil Palm Mapping Over Peninsular Malaysia Using Google Earth Engine and Machine Learning Algorithms

Oil palm plays a pivotal role in the ecosystem, environment, economy and without proper monitoring, uncontrolled oil palm activities could contribute to deforestation that can cause high negative impacts on the environment and therefore, proper management and monitoring of the oil palm industry are necessary. Mapping the distribution of oil palm is crucial in order to manage and plan the sustainable operations of oil palm plantations. Remote sensing provides a means to detect and map oil palm from space effectively. Recent advances in cloud computing and big data allow rapid mapping to be performed over large a geographical scale. In this study, 30 m Landsat 8 data were processed using a cloud computing platform of Google Earth Engine (GEE) in order to classify oil palm land cover using non-parametric machine learning algorithms such as Support Vector Machine (SVM), Classification and Regression Tree (CART) and Random Forest (RF) for the first time over Peninsular Malaysia. The hyperparameters were tuned, and the overall accuracy produced by the SVM, CART and RF were 93.16%, 80.08% and 86.50% respectively. Overall, the SVM classified the 7 classes (water, built-up, bare soil, forest, oil palm, other vegetation and paddy) the best. However, RF extracted oil palm information better than the SVM. The algorithms were compared and the McNemar's test showed significant values for comparisons between SVM and CART and RF and CART. On the other hand, the performance of SVM and RF are considered equally effective. Despite the challenges in implementing machine learning optimisation using GEE over a large area, this paper shows the efficiency of GEE as a cloud-based free platform to perform bioresource distributions mapping such as oil palm over a large area in Peninsular Malaysia

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Remote sensing analysis of croplands, woody plant encroachment and carbon fluxes of woody savanna

Since 1990s, much attention has been paid to Land use/land cover change (LULCC) studies because it is an important component of global change. The vegetation change is a critical factor of land cover changes, which interacts with climate, ecosystem processes, biogeochemical cycles and biodiversity. Remote sensing is a good tool to detect the changes of land use and land cover. To date, most of studies on vegetation changes have been conducted at biome scales, but have not examined changes at the species level. This lack of studies on species inhibits analysis of ecosystem functions caused by the shifts of vegetation types. This dissertation aims to explore the potential of remote sensing images to produce long-term products on specific vegetation type and study the interactions between vegetation type, climate and gross primary production. In Chapter 2, a simple algorithms was developed to identify paddy rice by selecting a unique temporal window (flooding/transplanting period) at regional scale using time series Landsat-8 images. A wheat-rice double-cropped area from China was selected as the study area. The resultant paddy rice map had overall accuracy and Kappa coefficient of 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map had a better detection of the changes in the paddy rice fields. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems. Chapter 3 developed a pixel and phenology-based mapping algorithm, and used it to analyze PALSAR mosaic data in 2010 and all the available Landsat 5/7 data during 1984-2010. This study analyzed 4,233 images covering more than 10 counties in the central region of Oklahoma, and generated eastern redcedar forest maps for 2010 and five historical time periods: the late 1980s (1984-1989), early 1990s (1990-1994), late 1990s (1995-1999), early 2000s (2000-2004), and late 2000s (2005-2010). The resultant maps clearly illustrated an increase in red cedar encroachment within the study area at an annual rate of ~8% during 1984-2010. Chapter 4 investigates the dynamics of juniper encroachment on the grasslands of Oklahoma by generating multi-period maps of juniper encroachment from 1984 to 2010 at a 30-m spatial resolution. The juniper forest maps in 1984 to 2010 were produced by the algorithms developed in Chapter 3. The resultant maps revealed the spatio-temporal dynamics of juniper forest encroachment at state and county scales. This study also characterized the juniper forest encroachment by geographical pattern and soil conditions. The resultant maps can be used to support studies on ecosystem processes, sustainability, and ecosystem services. Chapter 5 compared dynamics of major climatic variables, eddy covariance tower-based GPP, and vegetation indices (VIs) over the last decade in a deciduous savanna and an evergreen savanna under a Mediterranean climate. The relationships were also examined among VIs, GPP, and major climatic variables in dry, normal, and wet hydrological years. GPP of these two savanna sites were also simulated using a light-use efficiency based Vegetation Photosynthesis Model (VPM). The results of this study help better understanding the eco-physiological response of evergreen and deciduous savannas, and also suggest the potential of VPM to simulate interannual variations of GPP in different types of Mediterranean-climate savannas