Geli Zhang

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.

A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producers accuracy 86% and users accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images.

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between RiceLandsat and RiceNLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.

Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015

Due to rapid population growth and urbanization, paddy rice agriculture is experiencing substantial changes in the spatiotemporal pattern of planting areas in the two most populous countries-China and India-where food security is always the primary concern. However, there is no spatially explicit and continuous rice-planting information in either country. This knowledge gap clearly hinders our ability to understand the effects of spatial paddy rice area dynamics on the environment, such as food and water security, climate change, and zoonotic infectious disease transmission. To resolve this problem, we first generated annual maps of paddy rice planting areas for both countries from 2000 to 2015, which are derived from time series Moderate Resolution Imaging Spectroradiometer (MODIS) data and the phenology- and pixel-based rice mapping platform (RICE-MODIS), and analyzed the spatiotemporal pattern of paddy rice dynamics in the two countries. We found that China experienced a general decrease in paddy rice planting area with a rate of 0.72 million (m) ha/yr from 2000 to 2015, while a significant increase at a rate of 0.27mha/yr for the same time period happened in India. The spatial pattern of paddy rice agriculture in China shifted northeastward significantly, due to simultaneous expansions in paddy rice planting areas in northeastern China and contractions in southern China. India showed an expansion of paddy rice areas across the entire country, particularly in the northwestern region of the Indo-Gangetic Plain located in north India and the central and south plateau of India. In general, there has been a northwesterly shift in the spatial pattern of paddy rice agriculture in India. These changes in the spatiotemporal patterns of paddy rice planting area have raised new concerns on how the shift may affect national food security and environmental issues relevant to water, climate, and biodiversity.

Temporal Consistency Between Gross Primary Production and Solar-Induced Chlorophyll Fluorescence in the Ten Most Populous Megacity Areas over Years

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world’s ten most populous megacities during 2000–2014. The seasonal dynamics of GPPvpm during 2007–2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPPvpm during 2000–2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

Semi-natural areas of Tarim Basin in northwest China: Linkage to desertification

Semi-natural lands are not intensively managed lands, which have ecological significance in protecting artificial oasis and preventing desertification in arid regions. The significant shrinkage and degradation of semi-natural lands in the land-use intensification process have caused severe desertification. However, there is a knowledge gap regarding the spatio-temporal pattern and detailed classification of semi-natural lands and its quantitative relationship with desertification. Taking the Tarim Basin as an example, we proposed a comprehensive classification system to identify semi-natural lands for 1990, 2000, and 2010, respectively, using multi-source datasets at large scales. Spatio-temporal changes of semi-natural lands were then characterized by map comparisons at decade intervals. Finally, statistical relationships between semi-natural lands and desertification were explored based on 241 watersheds. The area of semi-natural lands in Tarim Basin was 10.77×104km2 in 2010, and desert-vegetation type, native-oasis type, artificial-oasis type, saline type and wetland type accounted for 59.59%, 14.65%, 11.25%, 9.63% and 4.88% of the total area, respectively. A rapid loss of semi-natural lands (9769.05km2) was demonstrated from 1990 to 2010. In the fragile watersheds, the semi-natural lands were mainly converted to desert; while in the watersheds with advanced oasis agriculture, artificial-oasis type reclaimed to arable land was the major change. The occurrence of desertification was closely related to the type, area proportion and combination patterns of semi-natural lands. Desertification was prone to occur in regions abundant in desert-vegetation type and saline type, while less serious desertification was observed in regions with high proportion of artificial-oasis type and wetland type. Policy intervention and reasonable water resource allocation were encouraged to prevent the substantial loss of semi-natural lands, especially for the water-limiting watersheds and periods.

Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016

Significance Strong variations in open-surface water body areas have impacted United States agriculture, economy, society, and ecosystems. This study presents the uneven water-resource distribution across the contiguous United States with the western half of the United States having less water body area but stronger interannual variability compared with the eastern half. Divergent trends of open-surface water body area in the last three decades, mainly driven by climate, indicated that the water-poor regions of the Southwest and Northwest United States were getting poorer, while the water-rich regions of Southeast and far north Great Plains were getting richer. Surface water body shrinkage in drought years led to massive groundwater mining and the rapid decrease of land water storage in California and the southern Great Plains. The contiguous United States (CONUS), especially the West, faces challenges of increasing water stress and uncertain impacts of climate change. The historical information of surface water body distribution, variation, and multidecadal trends documented in remote-sensing images can aid in water-resource planning and management, yet is not well explored. Here, we detected open-surface water bodies in all Landsat 5, 7, and 8 images (∼370,000 images, >200 TB) of the CONUS and generated 30-meter annual water body frequency maps for 1984–2016. We analyzed the interannual variations and trends of year-long water body area, examined the impacts of climatic and anthropogenic drivers on water body area dynamics, and explored the relationships between water body area and land water storage (LWS). Generally, the western half of the United States is prone to water stress, with small water body area and large interannual variability. During 1984–2016, water-poor regions of the Southwest and Northwest had decreasing trends in water body area, while water-rich regions of the Southeast and far north Great Plains had increasing trends. These divergent trends, mainly driven by climate, enlarged water-resource gaps and are likely to continue according to climate projections. Water body area change is a good indicator of LWS dynamics in 58% of the CONUS. Following the 2012 prolonged drought, LWS in California and the southern Great Plains had a larger decrease than surface water body area, likely caused by massive groundwater withdrawals. Our findings provide valuable information for surface water-resource planning and management across the CONUS.

Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000–2010

Monoculture rubber plantations have been replacing tropical rain forests substantially in Southern China and Southeast Asia over the past several decades, which have affected human wellbeing and ecosystem services. However, to the best of our knowledge on the extent of rubber plantation expansion and their stand ages is limited. We tracked the spatiotemporal dynamics of deciduous rubber plantations in Xishuangbanna, the second largest natural rubber production region in China, from 2000 to 2010 using time-series data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that rubber plantations have been expanding across a gradient from the low-elevation plains to the high elevation mountains. The areas of deciduous rubber plantations with stand ages ≤5, 6–10, and ≥11-year old were ~1.2 × 105 ha, ~0.8 × 105 ha, and ~2.9 × 105 ha, respectively. Older rubber plantations were mainly located in low-elevation and species-rich regions (500–900 m) and younger rubber trees were distributed in areas of relative high-elevation with fragile ecosystems. Economic and market factors have driven the expansion of rubber plantations, which is not only a threat to biodiversity and environmental sustainability, but also a trigger for climatic disasters. This study illustrates that the integration of microwave, optical, and thermal data is an effective method for mapping deciduous rubber plantations in tropical mountainous regions and determining their stand ages. Our results demonstrate the spatiotemporal pattern of rubber expansions over the first decade of this century.