scholarly journals Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

2019 ◽  
Vol 11 (5) ◽  
pp. 496 ◽  
Author(s):  
Shupeng Gao ◽  
Xiaolong Liu ◽  
Yanchen Bo ◽  
Zhengtao Shi ◽  
Hongmin Zhou
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rubber Plantation ◽  
Optical Remote Sensing ◽  
Rubber Plantations ◽  
Sensing Data ◽  
Spatio Temporal

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

scholarly journals Tree height inversion combining light detection and ranging and optical remote sensing data

2020 ◽  
Vol 165 ◽  
pp. 03020
Author(s):  
Kunlin Wang ◽  
Yi Ma ◽  
Fangrong Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Edge Length ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Inversion Model ◽  
Feature Parameters

Tree barriers in transmission line corridors are an important safety hazard.Scientific prediction of tree height and monitoring tree height changes are essential to solve this hidden danger. In this paper, the advantages of airborne lidar and optical remote sensing data are combined to research the method of tree height inversion. Based on glas data of lidar,waveform parameters such as waveform length, waveform leading edge length and waveform trailing edge length were extracted from waveform data by gaussian decomposition method.Terrain feature parameters were extracted from aster gdem data.The tree crown information was extracted from the optical remote sensing image by means of the mean shift algorithm. Then extract the vegetation index with high correlation with tree height.Finally, the extracted waveform feature parameters, topographic feature parameters, and crown index and vegetation index with high correlation are used as model input variables. The tree height inversion model was established using four regression methods, including multiple linear regression (mlr), support vector machine (svm), random forest (rf), and bp neural network (bpnn). The accuracy evaluation was conducted, and it was concluded that the tree height inversion model based on random forest obtained the best accuracy effect.

scholarly journals Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

2017 ◽  
Vol 21 (12) ◽  
pp. 6235-6251 ◽  
Author(s):  
Guiomar Ruiz-Pérez ◽  
Julian Koch ◽  
Salvatore Manfreda ◽  
Kelly Caylor ◽  
Félix Francés
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Automatic Calibration ◽  
Calibration And Validation ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

scholarly journals Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4057
Author(s):  
Liya Zhao ◽  
Qi Yang ◽  
Qiang Zhao ◽  
Jingwei Wu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Electrical Conductance ◽  
Remote Sensing Data ◽  
Groundwater Table ◽  
Irrigation District ◽  
Cropping Patterns ◽  
Sensing Data

Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the K-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from 4.7 × 105 ha to 7.1 × 105 ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately 4 × 105 ha) and the abandoned salinized farmland decreased (reduced from 0.45 × 105 ha to 0.19 × 105 ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization.

scholarly journals Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinyi Guo ◽  
Qing Guo ◽  
Zhongkui Feng
Keyword(s):  
Remote Sensing ◽  
Vegetation Change ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
High Mountain ◽  
Optical Remote Sensing ◽  
Subtle Change ◽  
Sensing Data ◽  
Significant Downward Trend ◽  
Spatio Temporal

It is vital to monitor the post-seismic landslides economically and effectively in high-mountain regions for the long term. The landslide creep could cause a subtle change of the overlying vegetation after the earthquake, which will lead to the change of vegetation spectral characteristics in optical remote sensing data. The optical remote sensing technique can be used to monitor the landslide creep areas with dense vegetation in a large range at a low cost because it is easy to obtain multi-temporal, multiple-scale, and multi-spectral information. We identified and extracted the vegetation change area before the 2018 Baige landslide by the high-resolution optical remote sensing data. Firstly, the image fusion method was used to improve the accuracy of change detection. Then, vegetation coverage before the landslide was calculated. The vegetation change was identified, and qualitative and quantitative methods were used to analyze the spatio-temporal changes of vegetation coverage. Our results indicate that the creep distance of the landslide is about 50 m and the vegetation in the back scarp area and the main sliding area display a significant downward trend with time closing to the landslide comparing with that in the reference area. The vegetation change in the remote sensing image has an excellent spatio-temporal correlation with the landslide creep. This study provides a possible way and perspective for monitoring post-seismic landslide disasters.

scholarly journals Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

2019 ◽  
Vol 11 (4) ◽  
pp. 449 ◽  
Author(s):  
Yang Song ◽  
Jing Wang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
A Priori ◽  
Remote Sensing Data ◽  
A Priori Knowledge ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Phenological Metrics ◽  
Crop Planting

Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

scholarly journals Application of Optical Remote Sensing in Rubber Plantations: A Systematic Review

2021 ◽  
Vol 13 (3) ◽  
pp. 429
Author(s):  
Fathin Ayuni Azizan ◽  
Adhitya Marendra Kiloes ◽  
Ike Sari Astuti ◽  
Ammar Abdul Aziz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Traditional Practices ◽  
Optical Remote Sensing ◽  
Area Index ◽  
Rubber Plantations ◽  
Sensing Data ◽  
Sensing Applications ◽  
Subject Areas

Rubber (Hevea brasiliensis) is a tropical tree crop cultivated for the industrial production of latex. The trees are tall, perennial and long-lived, and are typically grown in plantations. In most rubber-producing countries, smallholders account for more than 85% of plantation area. Traditional practices mean that it can be difficult to monitor rubber plantations for management purposes. To overcome issues associated with monitoring traditional practices, remote sensing approaches have been successfully applied in this field. However, information on this is lacking. Therefore, this study aims to document the current status, history, development and prospects for remote sensing applications in rubber plantations by using the PRISMA framework. The review focuses on the application of optical remote sensing data in rubber. In this paper, we discuss the current role of remote sensing on specific subject areas, namely mapping, change detection, stand age estimation, carbon and biomass assessment, leaf area index (LAI) prediction and disease detection. In addition, we elaborate on the benefits gained and challenges faced while adapting this technology. These include the availability and free access to satellite imagery as the greatest benefit and the presence of clouds as one of the toughest challenges. Finally, we highlighted four potential areas where future work can be done: (1) Advancements in remote sensing data, (2) algorithm enhancements, (3) emerging processing platforms, and (4) application to less studied subject areas. This paper gives insight into strengthening the potential of remote sensing for delivering efficient and long-term services for rubber plantations.

scholarly journals Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2078
Author(s):  
Leandro Sosa ◽  
Ana Justel ◽  
Íñigo Molina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Sensing Data ◽  
Hail Damage ◽  
Efficient Detection

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

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