Phenology-based Algorithm Research Articles

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

Large-scale mapping of new mixed rice cropping patterns in southern China with phenology-based algorithm and MODIS dataset

Large-scale mapping of new mixed rice cropping patterns in southern China with phenology-based algorithm and MODIS dataset

Continual expansion of Spartina alterniflora in the temperate and subtropical coastal zones of China during 1985–2020

Biological invasions are considerably altering ecosystem structure and functions, especially in coastal ecosystems that are subject to intensive anthropogenic disturbances. Spartina alterniflora has been recognized as the most serious invasive species in coastal China, which has received considerable attention from the government and the public. There is urgent need to control this invasive species at regional and national scales, but such efforts were impeded by lack of time-series data of Spartina spread. Here, we assessed the pixel- and phenology-based algorithm for mapping Spartina saltmarshes, and applied this algorithm to generate annual Spartina saltmarsh maps (30-m spatial resolution) from 1985 to 2020 by using time series Landsat 5/7/8 images. The resulting maps suggest that Spartina has been expanding since 1990 in coastal China, with three noticeable phases (rapid, moderate, and rapid). Along the latitudinal gradient, Spartina exhibited a longer invasion history and more frequent changes at low latitudes. Although human interventions caused the decline of Spartina in certain areas, rapid natural spread was primarily responsible for its extensive and continual invasion. These results provide insights for efficiently managing this invasive species, enhancing the conservation of coastal wetlands, and promoting the sustainability of coastal wetlands.

The dynamics of tea plantation encroachment into forests and effect on forest landscape pattern during 1991–2021 through time series Landsat images

Tea plantations encroachment into forests has occurred in the major tea planting area of Yunnan province, China in the past decades. However, the dynamics of tea plantation encroachment into forests and the effect on forest landscape pattern is not clear. In this study, we proposed a method to evaluate the dynamic effects of tea plantation encroachment on forest landscape pattern change by using time series Landsat 5/7/8 data from 1991 to 2021 on the Google Earth Engine (GEE) platform. First, a pixel- and phenology-based algorithm was applied to generate tea plantations encroachment into forests maps for seven historical periods. Second, three indices including the tea plantations encroachment index (TPEI), the growth area of tea plantations encroachment into forests (GATEF), and the growth index of tea plantations encroachment into forests (GITEF) were used to quantify the tea plantations encroachment into forests of seven historical periods. Third, the effects of forests landscape pattern resulting from tea plantations encroachment were evaluated in class metrics and landscape metrics respectively. Our results show a total of 13,721 ha of GATEF from the early 1990s to the early 2020s. The GITEF increased from the early 1990s to the early 2010s and decreased from the early 2010s to the early 2020s in our study area. And the TPEI indicates that the edge encroachment mode dominated in our study period. Correlation analysis indicates that tea plantations encroachment level had significant correlation coefficients with landscape pattern indices (such as CONTAG (r = −0.91), ED (r = 0.95), DIVISION (r = 0.85), and SHEI (r = 0.86)), which indicates that the level of tea plantations encroachment affects significantly the forest landscape pattern. This study provides a promising approach to quantifying the effect of forest landscape pattern results from tea plantations encroachment.

Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine

Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.

Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data

Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus. This study aims to quantify the rice yield at various phenological stages from hyper-temporal satellite-derived-vegetation indices computed from time series Sentinel-II images. Different vegetation indices (viz. NDVI, EVI, SAVI, and REP) were used to predict paddy yield. The predicted yield was validated through RMSE and ME statistical techniques. The integration of PLSR and sequential time-stamped vegetation indices accurately predicted rice yield (i.e., maximum R2 = 0.84 and minimum RMSE = 0.12 ton ha−1 equal to 3% of the mean rice yield). Moreover, our results also established that optimal time spans for predicting rice yield are late vegetative and reproductive (flowering) stages. The output would be useful for the farmer and decision makers in addressing food security.

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring.

Satellite observed rapid green fodder expansion in northeastern Tibetan Plateau from 2010 to 2019

The livestock product consumption per capita in China has almost doubled in the past three decades. The planting of green fodder has increased in the agropastoral ecotone of China to meet the increasing demand for livestock feed, and the green fodder expansion can have subsequent consequences on the environment. However, information on the area and distribution of green fodder is very limited. Here, we developed a pixel- and phenology-based algorithm to map green fodder and track its dynamics in the northeastern Tibetan Plateau, a typical alpine pasture region in China, using all the available Landsat images and Google Earth Engine (GEE). We developed a simple approach for the rapid identification of green fodder fields by using a new green fodder index that considers the unique phenology of green fodder, which has higher greenness and water content in the late growing season than other vegetation. A total of 858 Landsat images were used to generate green fodder maps in northeastern Tibetan Plateau (including Zeku, Guinan, and Tongde Counties) in three periods (circa 2010, 2015, and 2019). The overall accuracies of our green fodder maps were 93–97% and the Matthews correlation coefficients were 0.76–0.83. We found a rapid expansion of green fodder from 16.3 km2 in 2010 to 136.1 km2 in 2019. Newly cultivated green fodder occurred in both existing croplands and natural grasslands. Our study demonstrated the potential of the phenology-based approach, all the available Landsat images, and GEE for tracing the historical dynamics of green fodder at 30-m resolution in alpine regions. Our findings advance our understanding of changes in forage area, production, the supply–demand gap, and the ecological and climatic consequences of green fodder expansion.

Data for: Developing a phenology- and pixel-based algorithm for mapping rapeseed at 10m spatial resolution using multi-source data

Abstract. As a major oilseed crop, large-scale and high-resolution maps of rapeseed (Brassica napus L.) are critical for predicting annual production and ensuring global energy security. However, such free maps are still unavailable in large areas. We designed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting area (2017–2019) over 33 countries at 10-m spatial resolution based on the multiple data. The product showed a good consistence (R2 = 0.88) with the official statistics (Food and Agricultural Organization of the United Nations, FAO) at national level. Rapeseed maps achieved at least 0.81 F1-scores of spatial consistency when comparing with the Cropland Data Layer (CDL) of America, Annual Crop Inventory (ACI) in Canada and Crop Map of England (CROME) in England. Moreover, their F1-scores ranged 0.84–0.92 based on the independent validation samples, implying a good consistency with ground truth. Furthermore, we found that rapeseed crop rotation is ≥2 years in almost all countries. Our derived maps with high accuracy suggest the robustness of pixel- and phenology-based algorithm in identifying rapeseed over large regions with various climate and landscapes. The derived rapeseed planting areas freely downloaded can be applied to predict rapeseed production and optimize planting structure. The product is available publicly at http://dx.doi.org/10.17632/ydf3m7pd4j.3 (Han et al., 2021).

Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm

Mapping rice cropping systems is important for grain yield prediction and food security assessments. Both single- and double-season rice are the dominant rice systems in central China. However, because of increasing labor shortages and high costs, there has been a gradual decline in double-season rice. Ratoon rice (RR) has been proposed as an alternative system that balances the productivity, cost, and labor requirements of rice cultivation. RR has been expanding in central China, encouraged by the improved cultivars, machinery, and favorable policies. However, to our knowledge, the distribution of RR has not been mapped with remote sensing techniques. This study developed a phenology-based algorithm to map RR at a 10 m resolution in Hubei Province, Central China, using dense time stacks of Sentinel-2 images (cloud cover <80%) in 2018. The key in differentiating RR from the other rice cropping systems is through the timing of maturity. We proposed to use two contrast vegetation indices to identify RR fields. The newly-developed yellowness index (YI) calculated with the reflectance of blue, green, and red bands was used to detect the ripening phase, and the enhanced vegetation index (EVI) was used to detect the green-up of the second-season crop to eliminate the misclassification caused by stubbles left in the field. The RR map demonstrated that RR was mainly distributed in the low alluvial plains of central and southern Hubei Province. The total planting area of RR in 2018 was 2225.4 km2, accounting for 10.03% of the total area of paddy rice fields. The overall accuracy of RR, non-RR rice fields, and non-rice land cover types was 0.76. The adjusted overall accuracy for RR and non-RR was 0.91, indicating that the proposed YI and the phenology-based algorithm could accurately identify RR fields from the paddy rice fields.

Application of phenology-based algorithm and linear regression model for estimating rice cultivated areas and yield using remote sensing data in Bansloi River Basin, Eastern India

Delineation of paddy (Oryza sativa L.) cultivating areas using modern technologies like remote sensing and geographical information system is an important way for managing food security, agricultural production and water resource. This paper aims to identify the Aman paddy cultivating areas and estimate yield of Aman paddy using phenological algorithm based on different vegetation indices extracting from Landsat OLI images in Bansloi River basin, Eastern India. Different vegetation indices (viz. NDVI, EVI, LSWI and RGVI) were used to identify paddy cultivating areas and yield. Phenological phases of paddy growth were recognized considering daily air temperature, rainfall and field survey data. After running the linear regression between different vegetation indices and paddy yield collected during the field survey, the paddy yield was predicted for the identified paddy cultivated areas. The estimated paddy cultivated areas were validated using user, producer, overall accuracy and kohen's kappa test. On the other hand, predicted paddy yield was validated through RMSE and ME statistical techniques. All the validating methods are showing good accuracy of estimated paddy cultivated areas and predicted yield. The recognized total rice cultivating area, average yield and total production was 107173 ha, 2.51 tons/ha and 269004.23 tons respectively from 2015 to 2017. The estimation of paddy yield before harvest can provide valuable information to a planner about export, import and storage of food for the future.

Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995–2018

The rampant encroachment of Spartina alterniflora into coastal wetlands of China over the past decades has adversely affected both coastal ecosystems and socio-economic systems. However, there are no annual or multi-year epoch maps of Spartina saltmarsh in China, which hinders our understanding and management of Spartina invasion. In this study, we selected Chongming island, China, where Spartina saltmarsh had expanded rapidly since its introduction in the 1990s. We investigated phenology of Spartina, Phragmites and Scirpus saltmarshes, and the time series vegetation indices derived from Landsat images showed that Spartina saltmarsh did not green-up in April–May and stayed green in December–January, which differed from the phenology of Phragmites and Scirpus saltmarshes. We developed a pixel- and phenology-based algorithm that used time series Landsat data to identify and map Spartina saltmarsh, and we applied it to quantify the temporal dynamics (expansion and removal) of Spartina saltmarsh on Chongming island during 1995–2018. The resultant maps showed that Spartina saltmarsh area on Chongming island increased from ~4 ha in 1995 to ~2067 ha in 2012 but dropped substantially to ~729 ha in 2016 after a large-scale ecological engineering project (US$ 186 million) was started to remove Spartina during 2013–2016. Chongming island still had ~1315 ha Spartina saltmarsh in 2018, and majority of it was distributed outside the Chongming Dongtan National Nature Reserve, which could serve as the sources for reinvasion in the near future. This study demonstrates the feasibility of using time series Landsat images, pixel- and phenology-based algorithm, and GEE platform to identify and map Spartina saltmarsh over years in the region, which is useful to the management of invasive plants in coastal wetlands.

Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands is unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7474.6 km2 of coastal wetlands in China in 2018, which included 5379.8 km2 of tidal flats, 1856.4 km2 of deciduous wetlands, and 238.3 km2 of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine

Mountains provide essential ecosystem services to billions of people and are home to a majority of the global biodiversity hotspots. However, mountain ecosystems are particularly sensitive to climate and environmental changes. The protection and sustainable management of mountain ecosystems are thus of great importance and are listed as a Sustainable Development Goal (SDG 15.4) of the United Nations 2030 Agenda for sustainable development. The mountain green cover index (MGCI, SDG 15.4.2), which is defined by measuring the changes of green vegetation in mountain areas, is one of the two SDG indicators for monitoring the conservation status of mountain environments. However, as a country indicator, it is challenging to use the current MGCI data to quantify the detailed changes in highly heterogeneous mountain areas within each country, and correspondingly, the measures is limited when supporting sustainable development and protection strategy decision-making for mountain environments. In this paper, a new global high resolution gridded-MGCI calculation method that depicts the varying details in the MGCI from both the spatial and temporal domains was proposed based on 30-m Landsat-8 Operational Land Imager (OLI) images and the Google Earth Engine (GEE) cloud computing platform. In the method, first, a grid-based MGCI calculation model was proposed by that considers the true surface area instead of the planimetric area of each mountain pixel. The global green vegetation cover was then extracted using all available 30-m Landsat-8 satellite observations within the calendar year on the GEE platform via a new frequency- and phenology-based algorithm. The mountain true surface area was finally calculated and introduced into the MGCI calculation model for global MGCI mapping. The results showed that the green vegetation cover extracted from 30 m Landsat images can reach an overall accuracy of 95.56%. In general, 69.73% of the global mountain surface had 1.05 times more surface area than planimetric area. The average difference between the MGCIs considering the surface area and planimetric area can reach 11.89%. According to the statistics of the global grid MGCI, 68.79% of the global mountain area had an MGCI higher than 90%, 16.94% of the global mountain area had no vegetation cover and 3.81% of mountain area had an MGCI lower than 10%. The proposed MGCIs were further aggregated at the country level and compared with the Food and Agriculture Organization (FAO) MGCI baseline data from 2017. The comparison indicated good consistency between the two datasets, with an R2 of 0.9548 and a mean absolute difference of 4.26%. The new MGCI calculation method was based all available Landsat-8 observations from a year, which reduced the dependence of the MGCI on the updating frequency of the land cover product. Furthermore, the method has great potential for getting the spatio-temporal continuous MGCI with a high spatial resolution for characterizing explicit mountain vegetation dynamics and vegetation-climate change interactions to advance our understanding of global mountain changes. The new MGCI data will be available on the CASEarth data-sharing platform.

Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform

Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates.

Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

Cropping intensity has undergone dramatic changes worldwide due to the effects of climate changes and human management activities. Cropping intensity is an important factor contributing to crop production and food security at local, regional and national scales, and is a critical input data variable for many global climate, land surface, and crop models. To generate annual cropping intensity maps at large scales, Moderate Resolution Imaging Spectroradiometer (MODIS) images at 500-m or 250-m spatial resolution have problems with mixed land cover types within a pixel (mixed pixel), and Landsat images at 30-m spatial resolution suffer from low temporal resolution (16-day). To overcome these limitations, we developed a straightforward and efficient pixel- and phenology-based algorithm to generate annual cropping intensity maps over large spatial domains at high spatial resolution by integrating Landsat-8 and Sentinel-2 time series image data for 2016–2018 using the Google Earth Engine (GEE) platform. In this pilot study, we report annual cropping intensity maps for 2017 at 30-m spatial resolution over seven study areas selected according to agro-climatic zones in China. Based on field-scale sample data, the annual cropping intensity maps for the study areas had overall accuracy rates of 89–99%, with Kappa coefficients of 0.76–0.91. The overall accuracy of the annual cropping intensity maps was 93%, with a Kappa coefficient of 0.84. These cropping intensity maps can also be used to enable identification of various crop types from phenological information extracted from the growth cycle of each crop. These algorithms can be readily applied to other regions in China to generate annual cropping intensity maps and quantify inter-annual cropping intensity variations at the national scale with a greatly improved accuracy.

Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?

Updated information of rubber plantations is essential for assessing socioeconomic and environmental impacts, especially in the emerging region of northern tropics. Here, a phenological method was modified to detect rubber plantations using Landsat Operational Land Imager (OLI) imagery in Phongsaly Province of northern Laos, where it begun a rubber boom in the mid-2000s due to geo-economic cooperation. It highlighted the landscape and pixel differences of deciduous rubber plantations in the tri-temporal phases (i.e., pre-defoliation, defoliation, and foliation) during the dry season due to phenological changes. Six commonly used vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Burn Ratio (NBR and NBR2) derived from OLI imagery during 2013–2016 were compared to determine the most suitable VI for discriminating the phenological differences of rubber plantations from natural forests. Then, the Differences of Normalized Burn Ratio (DNBR) was applied to generate the 30 m map of rubber plantations in 2016, by combining two masks of Landsat-derived forest and suitable elevation for rubber trees cultivation. The resultant map of rubber plantations had a classification accuracy of 93.7% and the Kappa coefficient of 0.848. Our study demonstrated the usefulness of the Landsat-derived tri-temporal phenological DNBR approach in an emerging region of northern Laos, despite requiring more scenes compared with single- and double temporal window methods.

How Did Deciduous Rubber Plantations Expand Spatially in China's Xishuangbanna Dai Autonomous Prefecture During 1991–2016?

Rubber plantations have experienced continuous expansion in Xishuangbanna, in southwestern China, since the 1950s. However, the question of how the establishment of adventive rubber trees has spatially expanded in the margin of the Asian tropics in recent decades is not clearly understood. Here, a robust phenology-based algorithm—namely the change rate of the Landsat-derived normalized burn ratio—was modified based on tri-windows (i.e., predefoliation, defoliation, and foliation) and then used to discriminate deciduous rubber plantations from other land cover types by applying a threshold of 1.0 and combining Landsat-based forest masks every five years during 1991–2016. Deciduous rubber plantations increased more than 6.6 times, approximately 3074 km2 in 2016, or at an annual rate of about 8% (104 km2/year) in Xishuangbanna over that period, showing two typical expansion trends toward both higher (over 1000 m) and lower (below 600 m) elevations and increasingly spread to borderlands with Laos and Myanmar.

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.