InSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley,Mexico

This paper illustrates how InSAR alone can be used to delineate potential ground fractures related to aquifer system compaction. An InSAR-derived ground fracturing map of the Toluca Valley, Mexico, is produced and validated through a field campaign. The results are of great interest to support sustainable urbanization and show that InSAR processing of open-access Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellites can lead to reliable and cost-effective products directly usable by cities to help decision-making.The Toluca Valley Aquifer (TVA) sustains the water needs of two million inhabitants living within the valley, a growing industry, an intensively irrigated agricultural area, and 38% of the water needs of the megalopolis of Mexico City, located 40 km east of the valley. Ensuring water sustainability, infrastructure integrity, along with supporting the important economic and demographic growth of the region, is a major challenge for water managers and urban developers. This paper presents a long-term analysis of ground fracturing by interpreting 13 years of InSAR-derived ground displacement measurements. Small Baseline Subset (SBAS) and Persistent Scatterer Interferometry (PSI) techniques are applied over three SAR datasets totalling 93 acquisitions from Envisat, Radarsat-2, and Sentinel-1A satellites and covering the period from 2003 to 2016.From 2003 to 2016, groundwater level declines of up to 1.6 m/yr, land subsidence up to 77 mm/yr, and major infrastructure damages are observed. Groundwater level data show highly variable seasonal responses according to their connectivity to recharge areas. However, the trend of groundwater levels consistently range from −0.5 to −1.5 m/yr regardless of the well location and depth. By analysing the horizontal gradients of vertical land subsidence, we provide a potential ground fracture map to assist in future urban development planning in the Toluca Valley.     

Lithology-controlled subsidence and seasonal aquifer response in the Bandung basin,Indonesia, observed by synthetic aperture radar interferometry

Land subsidence in the Bandung basin, West Java, Indonesia, is characterized based on differential interferometric synthetic aperture radar (DInSAR) and interferometric point target analysis (IPTA). We generated interferograms from 21 ascending SAR images over the period 1 January 2007 to 3 March 2011. The estimated subsidence history shows that subsidence continuously increased reaching a cumulative 45 cm during this period, and the linear subsidence rate reached ∼12 cm/yr. This significant subsidence occurred in the industrial and densely populated residential regions of the Bandung basin where large amounts of groundwater are consumed. However, in several areas the subsidence patterns do not correlate with the distribution of groundwater production wells and mapped aquifer degradation. We conclude that groundwater production controls subsidence, but lithology is a counteracting factor for subsidence in the Bandung basin. Moreover, seasonal trends of nonlinear surface deformations are highly related with the variation of rainfall. They indicate that there is elastic expansion (rebound) of aquifer system response to seasonal-natural recharge during rainy season.     

Identifying natural and anthropogenically-induced geohazards from satellite ground motion and geospatial data: Stoke-on-Trent,UK

Determining the location and nature of hazardous ground motion resulting from natural and anthropogenic processes such as landslides, tectonic movement and mining is essential for hazard mitigation and sustainable resource use. Ground motion estimates from satellite ERS-1/2 persistent scatterer interferometry (PSI) were combined with geospatial data to identify areas of observed geohazards in Stoke-on-Trent, UK. This investigation was performed within the framework of the EC FP7-SPACE PanGeo project which aimed to provide free and open access to geohazard information for 52 urban areas across Europe. Geohazards identified within the city of Stoke-on-Trent and neighbouring rural areas are presented here alongside an examination of the PanGeo methodology.A total of 14 areas experiencing ground instability caused by natural and anthropogenic processes have been defined, covering 122.35 km². These are attributed to a range of geohazards, including landslides, ground dissolution, made ground and mining activities. The dominant geohazard (by area) is ground movement caused by post-mining groundwater recharge and mining-related subsidence (93.19% of total geohazard area), followed by landsliding (5.81%). Observed ground motions along the satellite line-of-sight reach maxima of +35.23 mm/yr and −22.57 mm/yr. A combination of uplift, subsidence and downslope movement is displayed.‘Construction sites’ and ‘continuous urban fabric’ (European Urban Atlas land use types) form the land uses most affected (by area) by ground motion and ‘discontinuous very low density urban fabric’ the least. Areas of ‘continuous urban fabric’ also show the highest average velocity towards the satellite (5.08 mm/yr) and the highest PS densities (1262.92 points/km²) along with one of the lowest standard deviations. Rural land uses tend to result in lower PS densities and higher standard deviations, a consequence of fewer suitable reflectors in these regions. PSI is also limited in its ability to identify especially rapid ground motion. As a consequence the supporting geospatial data proved especially useful for the identification of landslides and some areas of ground dissolution. The mapped areas of instability are also compared with modelled potential geohazards (the BGS GeoSure dataset).     

Mexico City land subsidence in 2014–2015 with Sentinel-1 IW TOPS: Results using the Intermittent SBAS (ISBAS) technique

Differential Interferometric Synthetic Aperture Radar (DInSAR) can be considered as an efficient and cost effective technique for monitoring land subsidence due to its large spatial coverage and high accuracy provided. The recent commissioning of the first Sentinel-1 satellite offers improved support to operational surveys using DInSAR due to regular observations from a wide-area product. In this paper we show the results of an intermittent small-baseline subset (ISBAS) time-series analysis of 18 Interferometric Wide swath (IW) products of a 39,000 km² area of Mexico acquired between 3 October 2014 and 7 May 2015 using the Terrain Observation with Progressive Scans in azimuth (TOPS) imaging mode. The ISBAS processing was based upon the analysis of 143 small-baseline differential interferograms. After the debursting, merging and deramping steps necessary to process Sentinel-1 IW products, the method followed a standard approach to the DInSAR analysis. The Sentinel-1 ISBAS results confirm the magnitude and extent of the deformation that was observed in Mexico City, Chalco, Ciudad Nezahualcóyotl and Iztapalapa by other C-band and L-band DInSAR studies during the 1990s and 2000s. Subsidence velocities from the Sentinel-1 analysis are, in places, in excess of −24 cm/year along the satellite line-of-sight, equivalent to over ∼40 cm/year vertical rates. This paper demonstrates the potential of Sentinel-1 IW TOPS imagery to support wide-area DInSAR surveys over what is a very large and diverse area in terms of land cover and topography.     

20 years of active deformation on volcano caldera: Joint analysis of InSAR and AInSAR techniques

InSAR (interferometric synthetic aperture radar) techniques are applied to investigate last two decades of surface deformation of the Cerro Blanco/Robledo Caldera (CBRC). The objective is the identification of deforming patterns that alter the shape of these complex structures when they show low or null activity. The joint analysis between results by using different methods over a long time span, represents a unique opportunity to improve knowledge of volcanic structures located in remote area and, for this, poorly or not monitored.In this work we identify displacement patterns over the volcanic area, by using both classical differential InSAR analysis, and A-InSAR (advanced InSAR) analysis based on SAR data acquired by ERS-1/2 and ENVISAT sensors during the 1996–2010 time interval. The satellite-derived information allows us to characterize the deformation pattern that affected the CBRC and shows that the actively deforming CBRC is subsiding in the observed period. In order to figure out the deformation history of CBRC, we analyzed the four sub-periods 1992–1996, 1996–2000, and 2005–2010 by using standard differential InSAR technique, and the interval 2003–2007 by adopting an A-InSAR technique.Subsidence velocities of the CBRC caldera are about 2.6 cm/yr in the time interval 1992–1996 (measured with ERS descending data), 1.8 cm/yr in 1996–2000 (ERS descending data), 1.2 cm/yr in 2003–2007 (ENVISAT descending data), and finally, 0.87 cm/yr in 2005–2010 (ENVISAT ascending data). Moreover, outside the caldera and in particular in the NW area, we observe the presence of positive velocity values. Results show that: (a) a decreasing subsidence rate might be related to the reduction of volcanic activity in correspondence of the CBRC; (b) positive velocity signal, decreasing with time, might be interpreted as follows: – evidence of volcano structure lateral spreading, according to the velocity pattern distribution in this area and to the relative local flanks topographic convexity of the volcano structure; – uplift signal of this sector of mountain chain; – combination of the two mechanisms above.     

Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing

Soil organic carbon (SOC) plays an important role in climate change regulation notably through release of CO2 following land use change such a deforestation, but data on stock change levels are lacking. This study aims to empirically assess SOC stocks change between 1991 and 2011 at the landscape scale using easy-to-access spatially-explicit environmental factors. The study area was located in southeast Madagascar, in a region that exhibits very high rate of deforestation and which is characterized by both humid and dry climates. We estimated SOC stock on 0.1 ha plots for 95 different locations in a 43,000 ha reference area covering both dry and humid conditions and representing different land cover including natural forest, cropland, pasture and fallows. We used the Random Forest algorithm to find out the environmental factors explaining the spatial distribution of SOC. We then predicted SOC stocks for two soil layers at 30 cm and 100 cm over a wider area of 395,000 ha. By changing the soil and vegetation indices derived from remote sensing images we were able to produce SOC maps for 1991 and 2011. Those estimates and their related uncertainties where combined in a post-processing step to map estimates of significant SOC variations and we finally compared the SOC change map with published deforestation maps. Results show that the geologic variables, precipitation, temperature, and soil-vegetation status were strong predictors of SOC distribution at regional scale. We estimated an average net loss of 10.7% and 5.2% for the 30 cm and the 100 cm layers respectively for deforested areas in the humid area. Our results also suggest that these losses occur within the first five years following deforestation. No significant variations were observed for the dry region. This study provides new solutions and knowledge for a better integration of soil threats and opportunities in land management policies.     

Land subsidence characteristics of Jakarta between 1997 and 2005, as estimated using GPS surveys

Jakarta is the capital city of Indonesia with a population of about 12 million people, inhabiting an area of about 625 km². It is well known that several areas in Jakarta are subsiding rapidly. There are four different types of land subsidence that can be expected to occur in the Jakarta basin, namely: subsidence due to groundwater extraction, subsidence induced by the load of constructions (i.e., settlement of high compressibility soil), subsidence caused by natural consolidation of alluvial soil and tectonic subsidence. In addition to the leveling method, Global Positioning System (GPS) survey methods have been used to study land subsidence in Jakarta. In this paper, we characterize subsidence in the Jakarta basin using eight episodic/campaign GPS surveys between 1997 and 2005. The estimated subsidence rates are 1–10 cm/year. The observed subsidence rates in several locations show a positive correlation with known abstraction volumes of groundwater extraction. These basin-wide series of GPS measurements show how this type of measurement can play an important role in multiple public policy decision making in this rapidly growing area.     

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.     

3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar

We developed a method to produce a 3-D voxel-based solid model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target into voxels. The voxel size was 0.5 cm × 0.5 cm × 0.5 cm. Then, we used differences in the spatial distribution of voxels to separate the stem and large branches (diameter > 1 cm) from small branches (diameter ⩽ 1 cm). We classified the voxels into sets corresponding to the stem and to each large branch and then interpolated voxels to fill out their surfaces and their interiors. We then merged the stem and large branches with the small branches. The resultant solid model of the entire tree was composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches, and a cloud of voxels equivalent to small branches that were discretely scattered in mainly the upper part of the target. Using this model, we estimated the woody material volume by counting the number of voxels in each part and multiplying the number of voxels by the unit voxel volume (0.13 cm³). The percentage error of the volume of the stem and part of a large branch was 0.5%. The estimation error of a certain part of the small branches was 34.0%.     

Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands,Colombia

Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river discharges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to estimate and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea, and the rainfall patterns in the Magdalena River watershed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River watershed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001–2014 (p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r = 0.57–0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed (r = 0.63–0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Niña conditions while the opposite was the case during El Niño conditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.     

Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R² = 0.96), shrub cover (adj. R² = 0.83), grass cover (adj. R² = 0.71), bare soil (adj. R² = 0.88), stone cover (adj. R² = 0.83) and litter cover (adj. R² = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.     

Mapping and estimating land change between 2001 and 2013 in a heterogeneous landscape in West Africa: Loss of forestlands and capacity building opportunities

In West Africa, accurate classification of land cover and land change remains a big challenge due to the patchy and heterogeneous nature of the landscape. Limited data availability, human resources and technical capacities, further exacerbate the challenge. The result is a region that is among the more understudied areas in the world, which in turn has resulted in a lack of appropriate information required for sustainable natural resources management. The objective of this paper is to explore open source software and easy-to-implement approaches to mapping and estimation of land change that are transferrable to local institutions to increase capacity in the region, and to provide updated information on the regional land surface dynamics. To achieve these objectives, stable land cover and land change between 2001 and 2013 in the Kara River Basin in Togo and Benin were mapped by direct multitemporal classification of Landsat data by parameterization and evaluation of two machine-learning algorithms. Areas of land cover and change were estimated by application of an unbiased estimator to sample data following international guidelines. A prerequisite for all tools and methods was implementation in an open source environment, and adherence to international guidelines for reporting land surface activities. Findings include a recommendation of the Random Forests algorithm as implemented in Orfeo Toolbox, and a stratified estimation protocol − all executed in the QGIS graphical use interface. It was found that despite an estimated reforestation of 10,0727 ± 3480 ha (95% confidence interval), the combined rate of forest and savannah loss amounted to 56,271 ± 9405 ha (representing a 16% loss of the forestlands present in 2001), resulting in a rather sharp net loss of forestlands in the study area. These dynamics had not been estimated prior to this study, and the results will provide useful information for decision making pertaining to natural resources management, land management planning, and the implementation of the United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD).     

SAR interferometry and optical remote sensing for analysis of co-seismic deformation,source characteristics and mass wasting pattern of Lushan (China,April 2013) earthquake

Co-seismic deformation associated with the Lushan (China) earthquake that occurred along the south-western segment of the Longmenshan Fault Zone (LFZ) on the 20th April 2013 has been estimated by differential interferometric SAR (DInSAR) technique using Radarsat-2 data. The Lushan earthquake resulted in the deformation of the Sichuan basin and the Longmenshan ranges in proximity to the LFZ. The line of sight (LOS) displacement values obtained from DInSAR technique mainly range between −4.0 cm to +3.0 cm. The western Sichuan basin shows oblique westward movement with predominant downward component in areas farther from LFZ and predominant westward component over the downward movement in areas closer to the source fault. Inversion modelling has been used to derive the seismic source characteristics from DInSAR derived deformation values using elastic dislocation source type. The linear inversion model converged at a double-fault source solution consisting of a deeper, steep, NW dipping fault plane-1 of 60 km × 16 km dimension and a shallower, gentle, NW dipping fault plane-2 of 60 km × 15 km dimension, with distributed slip values varying between 0 to 2.26 m. These fault planes (fault planes-1 and -2) coincide with the Dachuan-Shuangshi fault and the buried Range Front Fault, respectively. The inversion model gives a moment magnitude of 6.81 and the geodetic moment of 2.07 × 10¹⁹ Nm, comparable to those given in literature, derived using teleseismic body wave data. Thus DInSAR technique helped to quantify the co-seismic deformation and to retrieve the source characteristics from the estimated deformation values. The study also evaluated the distribution pattern of earthquake induced landslides (EIL) triggered fresh or re-activated during the Lushan earthquake and found that they show spatial association with the seismic source zone and also with various pre-conditioning factors of slope instability.     

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (H_R, and N_Rp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and H_R = 0.1, whereas values of ω = 0.06 − 0.08 and H_R = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, H_R and N_Rp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, H_R = 0.4 and N_Rp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and H_R varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m³ m⁻³, ubRMSE = 0.062 m³ m⁻³ for the IGBP-based calibration; against R = 0.54, bias = −0.034 m³ m⁻³ and ubRMSE = 0.070 m³ m⁻³ for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product.     

Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.     

Land subsidence lagging quantification in the main exploration aquifer layers in Beijing plain,China

Land subsidence is rapidly developing across the Beijing Plain, China. Long-term intense overexploitation of groundwater is the main reason for land subsidence in Beijing. In this study, an optimized Small Baseline Subset (SBAS) interferometry method was developed to process 46 RADATSAT-2 images from 2011 to 2015 to investigate the spatial and temporal dynamics of land subsidence in the Beijing Plain. The lag time between land subsidence and groundwater exploitation was first analyzed by the Continuous Wavelet Transform (CWT) and Cross Wavelet Transform (XWT) methods Our study found that the maximum subsidence rate reached 141 mm per year. The analysis of the areas and volumes of the annual subsidence rates indicated that the overall deformation trend slowed down from 2011 to 2015. Our results indicate that the subsidence center is always located in the southeast of Chaoyang District from 2011 to 2015. The lag time between the observed subsidence and the groundwater level drops in the main exploration aquifer layers was 0.57–1.76 months. This information is helpful to reveal the mechanism of land subsidence and build hydrogeological model.     

Next generation of global land cover characterization,mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).     

Radar interferometry time series analysis of Mashhad subsidence

A large agricultural area located in 20 km north of the city of Mashhad in the north-east of Iran is subject to land subsidence. The subsidence rate was achieved in a couple of sparse points by precise leveling between 1995 and 2005, and continuous GPS measurements obtained from 2005 to 2006. In order to study the temporal behavior of the deformation in high spatial resolution, the small baseline subset (SBAS) algorithm was used to generate the interferometric SAR time series analysis. Time series analysis was performed using 19 interferograms calculated from 12 ENVISAT ASAR data spanning between 2003 and 2006. The time series results exhibited that the area is subsiding continuously without a significant seasonal effect. Mean LOS deformation velocity map obtained from time series analysis demonstrated a considerable subsidence rate up to 24 (cm/yr). In order to evaluate the time series analysis results, continuous GPS measurements as a geodetic approach were applied. The comparisons showed a great agreement between interferometry results and geodetic technique. Moreover, the information of various piezometric wells distributed in the area corresponding to 1995 to 2005 showed a significant decline in water table up to 20 meters. The correlation between the piezometric information and the surface deformation at well’s locations showed that the subsidence occurrence in Mashhad is due to the excess groundwater withdrawal.     

Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China

The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011–2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China.The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = −0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = −0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain.