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Abstract Land use land cover (LULC) maps are crucial for various applications, such as disaster management, natural resource conservation, biodiversity evaluation, climate modeling, etc. The Japan Aerospace Exploration Agency (JAXA) has released several high-resolution LULC maps for national and regional scales. Vietnam, due to its rich biodiversity and cultural diversity, is a target country for the production of high-resolution LULC maps. This study introduces a high-resolution and high-accuracy LULC map for Vietnam, utilizing a CNN approach that performs convolution over a time-feature domain instead of the typical geospatial domain employed by conventional CNNs. By using multi-temporal data spanning 6 seasons, the produced LULC map achieved a high overall accuracy of 90.5% ± 1.2%, surpassing other 10-meter LULC maps for Vietnam in terms of accuracy and/or the ability to capture detailed features. In addition, a straightforward and practical approach was proposed for generating cloud-free multi-temporal Sentinel-2 images, particularly suitable for cloudy regions. This study marks the first implementation of the time-feature CNN approach for the creation of a high-accuracy LULC map in a tropical cloudy country.

Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.

Abstract Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

We report the ground validation activity for the terrestrial ecology products (leaf area index, above-ground biomass, and fraction of absorbed photosynthetically active radiation) of the Second-generation Global Imager (SGLI) on JAXA’s satellite named “Global Change Observation Mission-Climate.” We gave special attention to quantifying the uncertainty propagating from errors in the ecological reference data (ERD) obtained by the field work. Specifically, for optimal design and practical implementation of the field work with small uncertainty and small cost, we proposed: 1) a practical target which defined the accuracy threshold of ERD as a quarter of the satellite accuracy threshold, and 2) a calculation method of the uncertainty quantification of ERD by accounting for the uncertainty propagating from the empirical regression equations (such as allometry equations) and the statistical distribution of the population. As a result, we obtained ERD for GCOM-C/SGLI in various plant functional types (a deciduous needle-leaved forest, a deciduous broad-leaved forest, an evergreen needle-leaved forest, and dry and wet grassland) with sufficient quality, especially with a coverage area of 500 m × 500 m which can include a footprint of the sensor (250 m × 250 m) in any situation. We demonstrated: 1) the accuracy target was the key decision to make the practical calibration/validation work, 2) the regression uncertainty had a large impact, although little literature provided sufficient ancillary data about the regression equations necessary for quantification of the uncertainty, and 3) the optimal protocols of ERD observation can change depending on situations (plant functional types, phenology stages, type of products, accuracy targets, resources, and development of observation instruments and techniques); hence the choice should be made on the basis of quantification of the uncertainties.

The photochemical reflectance index (PRI) and the chlorophyll carotenoid index (CCI) are carotenoid-sensitive vegetation indices, which can monitor vegetation’s photosynthetic activities. One unique satellite named “Global Change Observation Mission-Climate (GCOM-C)” is equipped with a sensor, “Second Generation Global Imager (SGLI)”, which has the potential to frequently and simultaneously observe PRI and CCI over a wide swath. However, the observation accuracy of PRI and CCI derived from GCOM-C/SGLI remains unclear in forests. Thus, we demonstrated their accuracy assessment by comparing them with in situ data. We collected in situ spectral irradiance data at four forest sites in Japan for three years. We statistically compared satellite PRI with in situ PRI, and satellite CCI with in situ CCI. From the obtained results, the satellite PRI showed poor agreement (the best: r=0.294 (p<0.05)) and the satellite CCI showed good agreement (the best: r=0.911 (p<0.001)). The greater agreement of satellite CCI is possibly because satellite CCI contained fewer outliers and satellite CCI was more resistant to small noise, compared to satellite PRI. Our results suggest that the satellite CCI is more suitable for practical use than the satellite PRI with the latest version (version 3) of GCOM-C/SGLI’s products.

The effects of climate change on plant phenological events such as flowering, leaf flush, and leaf fall may be greater in steep river basins than at the horizontal scale of countries and continents. This possibility is due to the effect of temperature on plant phenology and the difference between vertical and horizontal gradients in temperature sensitivities. We calculated the dates of the start (SGS) and end of the growing season (EGS) in a steep river basin located in a mountainous region of central Japan over a century timescale by using a degree-day phenological model based on long-term, continuous, in situ observations. We assessed the generality and representativeness of the modelled SGS and EGS dates by using phenological events, live camera images taken at multiple points in the basin, and satellite observations made at a fine spatial resolution. The sensitivity of the modelled SGS and EGS dates to elevation changed from 3.29 days (100 m)-1 (-5.48 days °C-1) and -2.89 days (100 m)-1 (4.81 days °C-1), respectively, in 1900 to 2.85 days (100 m)-1 (-4.75 days °C-1) and -2.84 day (100 m)-1 (4.73 day °C-1) in 2019. The long-term trend of the sensitivity of the modelled SGS date to elevation was -0.0037 day year-1 per 100 m, but the analogous trend in the case of the modelled EGS date was not significant. Despite the need for further studies to improve the generality and representativeness of the model, the development of degree-day phenology models in multiple, steep river basins will deepen our ecological understanding of the sensitivity of plant phenology to climate change.

Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes.

Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 106 ha (±0.16 × 106 ha) of natural forests and 3.89 × 106 ha (±0.11 × 106 ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam.

Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R2 and RMSE of 0.99 and 0.03, respectively. Using the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas.

Mongolian steppe is one of the largest and important ecosystems. The degradation of grassland and the expansion of desert are occurring due to drought and desertification processes. We attempted monitoring of the broad-scale vegetation in Mongolia by a space-borne precipitation radar, which may complement typical approaches of vegetation monitoring (such as NDVI). We utilized the Global Precipitation Mission’s (GPM) dual-frequency precipitation radar (DPR). We characterized backscatter (σ0) of GPM/DPR’s two microwave bands (Ku and Ka) with respect to the dominant vegetation zones (forest, grassland, desert). Both Ku and Ka radars’ σ0 values were investigated for incidence angle dependency and the seasonal variation. As a result, the use of multi-angle, multi-band observations of GPM/DPR could help to characterize the vegetation zones. Especially, the σ0 at incidence angles between 1° and 8° represented useful characteristics of vegetation. Based on it, by using unsupervised clustering, we produced annual maps describing vegetation zones from 2014 to 2018. The result indicated that Mongolia experienced extensive changes in grassland and desert areas during the study years.

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015−2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.

To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called “GPP capacity”) is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CIgreen(ρNIR/ρgreen-1), had a strong linear correlation with chlorophyll content at the leaf scale (R2 = 0.87, p < 0.001) and with GPP capacity at the canopy (R2 = 0.78, p < 0.001) and satellite scales (R2 = 0.72, p < 0.01). Therefore, CIgreen is a robust and suitable vegetation index for estimating global GPP capacity.

Cloud contamination is one of the severest problems for the time-series analysis of optical remote sensing data such as vegetation phenology detection. Sub-pixel clouds are especially difficult to identify and remove. It is important for accuracy improvement in various terrestrial remote sensing applications to clarify the influence of these residual clouds on spectral vegetation indices. This study investigated the noises caused by residual sub-pixel clouds on several frequently-used spectral indices (NDVI, EVI, EVI2, NDWI, and NDII) by using in situ spectral data and sky photographs at the satellite overpass time. We conducted in situ continuous observation at a Japanese deciduous forest for over a year and compared the MODIS spectral indices with the cloud-free in situ spectral indices. Our results revealed that residual sub-pixel clouds potentially contaminated about 40% of the MODIS data after cloud screening by the state flag of MOD09 product. These residual clouds significantly decreased NDVI values during the leaf growing season. However, such noises did not appear in the other indices. This result was thought to be caused by the different combination of wavelengths among spectral indices. Our results suggested that the noises by residual sub-pixel clouds can be reduced by using EVI, NDWI, or NDII in place of NDVI.

We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) “GRVI = 0” can be a site-independent single threshold fordetection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.

Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide.

In this study, we aimed to explore the rural landscape transformation through the analysis of land use/land cover (LULC) spatiotemporal dynamics in the entire Vietnamese Mekong Delta over the three decades, using Landsat imageries and a supervised random forest classifier. Results indicated that in the 1990s, dominant LULC types were arable cropland, wetland, and permanent crops, which covered approximately half of the delta, 28% and 8%, respectively. From 1990 to 2019, the wetland area decreased from 28% to 5% due to the expansion of aquaculture and the arable cropland. The aquaculture, however, expanded more rapidly than other land use conversions, namely, from 2% to 19%. Although mangrove forests slightly recovered by 58 km² from 2005 to 2019, the overall trend (1990-2019) showed an intense loss of 740 km² due to widespread conversions into aquaculture. The paper unravels the complex dynamics between land-cover change, socio-economic development and associated land-use policies, which have significant implications for the change in rural landscapes. [ABSTRACT FROM AUTHOR]