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Fruit planting plays an essential role in achieving zero hunger, ensuring food security, and increasing the income of local people. As one of China's principal fruit-planting areas, southern Xinjiang possesses more than 80% of the total fruit-planting areas of Xinjiang. However, the spatial distribution, extent, and types of fruit trees remain unknown, generating uncertainty in calculating the economic benefits of orchards in this area. In this paper, we used walnut and jujube orchards under fruit tree–crop intercropping conditions in the Hotan Oasis in southern Xinjiang as the research object. Walnut and jujube orchards were precisely characterized using object-oriented and decision tree classification methods. Then, the economic benefits for farmers were estimated based on field surveys and statistical data. From 2003 to 2020, the area of jujube orchards rapidly increased from 1.91 × 103 ha to 33.59 × 103 ha, while that of walnut orchards steadily increased from 18.05 × 103 ha to 40.24 × 103 ha. The expansion areas of walnut orchards were mainly transformed from other orchards in the oasis, while the expansion areas of jujube orchards mainly originated from newly cultivated land in the desert. The increases in areas and yields largely offset the increase in planting costs and the decrease in purchase prices, resulting in an increasing trend in the total net income of the orchards. From 2003 to 2020, the total net income of walnut orchards increased by 68.96%, and the total net income of jujube orchards significantly increased by 23.37 times. However, the output/input ratios of walnut orchards under intercropping conditions were approximately two times higher than those of jujube orchards under monocropping conditions. The increase in investment slightly offset the decreases in yields and purchase prices, resulting in increases of 5.24% and 18.64% in the output/input ratios of walnuts and jujubes, respectively, in orchards exhibiting standardized cultivation. In the future, it is necessary to significantly expand the area of orchards involving standardized cultivation and improve the yield and quality of fruits, thereby increasing the yields and income levels of local farmers. [ABSTRACT FROM AUTHOR]

Monitoring the spatiotemporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on Earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from this problem since the early 2000s. Several efforts have been made to mitigate it. However, invasive plants, such as the water hyacinth, are still present in seed banks at the bottom of the river and can germinate even more than a decade after. In this article, we propose an automatic methodology, based on remote sensing and deep learning techniques, to monitor the water hyacinth in the Guadiana river. Specifically, a multitemporal analysis was carried out during two years using images collected by ESA's Sentinel-2 satellite, analyzed with a convolutional neural network. We demonstrate that, with our strategy, the river can be monitored every few days, and we are able to automatically detect the water hyacinth. Three experiments have been carried out to predict the presence of water hyacinth from a few scattered training samples, which represent invasive plants in different phenological stages and with different spectral responses.

Multispectral remote sensing is very effective for the detection of different types of land cover. Satisfactory results have been obtained in the task of monitoring different types of vegetation covers using satellite images, including areas conquered by invasive aquatic plants such as water hyacinth. Several countries around the world are suffering from negative effects caused by the quick spread of these plants. For instance, several strategies have been carried out for their control and elimination in the Guadiana River, Spain. In this letter, we develop a new methodology capable of automatically finding the geolocation of the most frequent areas of accumulation of invasive aquatic plants in the Guadiana River. Our strategy exploits multispectral time series acquired by European Space Agency (ESA) Sentinel-2 satellite. Once the invasive plants have been detected using deep learning [a convolutional neural network (CNN)], a subsequent analysis is carried out using geographic information systems (GIS) to map the areas where water hyacinth patches are most frequently found. In this way, we demonstrate that the management of invasive aquatic plants in the Guadiana River can be successfully carried out. [ABSTRACT FROM AUTHOR]

Mangrove forests provide a range of ecosystem services but may be increasingly threatened by climate change in the North Atlantic due to high-intensity storms. Hurricane Irma (Category 5) hit the northern coast of Cuba in September 2017, causing widespread damage to mangroves; losses have not yet been extensively documented due to financial and logistical constraints for local scientists. Our team estimated Irma's impacts on Cuban ecosystems in a coastal and upland study area spanning over 1.7 million ha. We developed a multi-resolution time series "vegetation anomaly" approach, where post-disturbance observations in photosynthetically active vegetation (Enhanced Vegetation Index, EVI) were normalized to the reference period (dry season mean over a historical time series). The Hurricane Disturbance Vegetation Anomaly (HDVA) was used to estimate the extent, severity, and temporal patterns of ecological changes with Sentinel-2 and MODIS data and used vicarious validation with microsatellite interpretation (Planet). HDVA values were classed to convey qualitative labels useful for local scientists: (1) Catastrophic, (2) Severe, (3) Moderate, (4) Mild, and (5) No Loss. Sentinel-2 had a limited reference period (2015–2017) compared to MODIS (2000–2017), yet the HDVA patterns were similar. Mangrove and wetlands (>265,000 ha) sustained widespread damages, with a staggering 78% showing damage, largely severe to catastrophic (0–0.81 HDVA; >207,000 ha). The damaged area is 24 times greater than impacts from Irma as documented elsewhere. Caguanes National Park (>8400 ha, excluding marine zones) experienced concentrated, severe mangrove and wetland damages (nearly 4000 ha). The phenological declines from Irma's impacts took up to 17 months to fully actualize, a much longer period than previously suggested. In contrast, dry forests saw rapid green flushes post-hurricane. With the increase of high-intensity storm events and other threats to ecosystems, the HDVA methods outlined here can be used to assess intense to low-level damages. [ABSTRACT FROM AUTHOR]

Monitoring large areas of the Earth's surface is possible thanks to the availability of remote sensing data obtained by a large collection of diverse satellites orbiting the Earth. Specifically, multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques supply the structural community with time series of line-of-sight displacements of terrain or structures, such as bridges or buildings, resulting from causes such as thermal expansion, contraction, and terrain deformation. The analysis of the different deformation signals observed is crucial in order to identify the different phenomena that cause the deformation. In this article, we explore the possibility of applying blind source separation algorithms to MT-InSAR, with the aim of developing methods towards automatic identification of different deformation patterns both in buildings and structures. We validate the proposed methodology using both synthetically generated datasets and real MT-InSAR data. We also provide a comparison with other similar methods. Our results demonstrate that InSAR time-series analysis can benefit from the use of the proposed blind source separation approach. Furthermore, the proposed technique is robust to the noise which is usually present in MT-InSAR data. This opens the door for monitoring infrastructure at scale over very large areas, helping to monitor civil infrastructures, and providing relevant insights to asset owners regarding the performance of such structures over time.

Due to interference with remote imaging by some natural factors, the multitemporal analysis ability is limited by the spectral drift between images. In this article, a new approach to optimize the existing multitemporal analysis system is proposed: multitemporal intrinsic image decomposition (MIID). The MIID method is designed to extract common spectral reflectance from multitemporal images. With MIID, multitemporal classification, changing detection, and index extracting will become extremely easy and more accurate. Firstly, without considering land cover change, the general MIID framework is proposed by adding local temporal–spatial energy constraints in traditional intrinsic images decomposition. On this basis, an improved MIID method with change detection (CD) (CD-MIID) capability is proposed to make the model adapt to the land cover change situation. Finally, specific steps of how to use MIID methods in the multitemporal analysis are given. Multitemporal multispectral/hyperspectral remote sensing images from GF-1, GF-2, GF-5, Landsat TM, and two groups of captured datasets with reflectance truth map are used to evaluate the performance. The experimental results show the following two points: first, the MIID methods achieve better extraction results of spectral reflectance. Second, the proposed MIID methods have better performance both on multitemporal classification and CD. [ABSTRACT FROM AUTHOR]

Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12’). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12’ was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12’) and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution.

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices.

Abstract We present a case study of a long-term integrated monitoring of a flood event which affected part of the Strymonas dammed river basin, a transboundary river with source in Bulgaria, which flows then through Greece to the Aegean Sea. The event, which affected the floodplain downstream the Kerkini dam, started at the beginning of April 2015, due to heavy rain upstream of the monitored area, and lasted for several months, with some water pools still present at the beginning of September, due to the peculiar geomorphological conditions of the watershed. We collected a multi-temporal dataset consisting of a high-resolution, X-band COSMO-SkyMed, and several C-band Sentinel-1 SAR and optical Landsat-8 images of the area. The results allow following the event in time, sketching a multi-temporal map of the post-flood evolution, with relatively high temporal resolution. We then use hydrological modeling to mimic the dynamics of the flooded area against post event weather patterns and thus explain the observed flood extent evolution. We show how integrating remote sensing-derived maps of flooded areas, geomorphological analyses of the landscape and simplified hydrological modeling allows accurate inference about long-term dynamics of flooded areas, very important in the post event in anthropogenic highly modified areas, where recovery time after the flood event is considerable, and long term water persistence may lead to large consequences, carrying economic damages and medical emergencies. Graphical abstract Unlabelled Image Highlights • Geomorphological history of the Strymonas river basin controls spatial flood inundation. • Multitemporal satellite images allowed retrieving several inundation maps at different times. • Simplified hydrological modeling allowed inference about long-term dynamics of flooded areas. • Integration of geomorphology, remote sensing and hydrological modeling allowed reconstructing post flood dynamics. [ABSTRACT FROM AUTHOR]

The Pantanal is located in the center of South America in a tectonically active sedimentary basin of Quaternary age. Even though the relief is flat and the diversity of the sediments is low, its vegetation cover has high variability resulting from seasonal fluctuations in water levels and the presence of four surrounding biomes. Changes in elevation of less than 1 m influence the length and intensity of floods, powerfully affecting the vegetation. Faults with small vertical displacement can generate abrupt vegetation changes and, consequently, expressive vegetation lineaments. This study characterizes a lineament in the northern Pantanal, considering Land Surface Phenology, estimates of precipitation, and floristic survey. The phenological metrics, obtained from a 15-year time series from the Moderate Resolution Imaging Spectroradiometer processed by TIMESAT software, discriminate evergreen forests in the NW of this lineament from savanna-like physiognomies in the SE region. Plant taxonomic identification shows two distinct regional strata with a clear separation between species adapted to prolonged floods in the NW and typical species of the Cerrado biome, mostly xeromorphic, in the SE. Data from the Tropical Rainfall Measuring Mission complemented the analysis, showing different dependence on local rains on different sides of the lineament. The entire dataset defines this geological structure as a driver of the Pantanal's plant communities, being a boundary for the extensive establishment of propagules of the Amazon biome. This research, in addition to advancing knowledge of this singular region, which is essential for management studies, can be a stimulus to biological and forest investigations. [ABSTRACT FROM AUTHOR]

Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors’ knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data. [ABSTRACT FROM PUBLISHER]

This paper presents a novel iterative active learning (AL) technique aimed at defining effective multitemporal training sets to be used for the supervised detection of land-cover transitions in a pair of remote sensing images acquired on the same area at different times. The proposed AL technique is developed in the framework of the Bayes' rule for compound classification. At each iteration, it selects the pair of spatially aligned unlabeled pixels in the two images that are classified with the maximum uncertainty. These pixels are then labeled by an external supervisor and included in the training set. The uncertainty of a pair of pixels is assessed by the joint entropy defined by considering two possible different simplifying assumptions: 1) class-conditional independence and 2) temporal independence between multitemporal images. Accordingly, different algorithms are introduced. The proposed joint-entropy-based AL algorithms for compound classification are compared with each other and with a marginal-entropy-based AL technique (in which the entropy is computed separately on single-date images) applied to the postclassification comparison method. The experimental results obtained on two multispectral and multitemporal data sets show the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

China's Horquin area in the northern farming-pastoral transition zone is undergoing rapid land degradation and rangeland modification that is impacting far broader areas as the source of material for dust storms. Multi-temporal Landsat images of the Horquin core area were used to generate a time series of land use covering about a 30-year period, 1975-2003. We show that the physical environment in Horquin deteriorated between 1975 and 2000, although this situation was more controlled after 2000. The main driving forces of land use and land cover change (LUCC) appear not to be population growth and agricultural expansion but policy-relevant issues, including market distortions, unwise farming-pastoral management, and institutional uncertainty. Results suggest that planning for the recovery and rebuilding of the grassland natural ecological system is a viable option to control dust storms in northern China. [ABSTRACT FROM AUTHOR]

Abstract: Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India. [Copyright &y& Elsevier]

Multitemporal remote sensing provides a unique tool to track lake dynamics at the pan-Arctic scale but requires precise registration of thousands of satellite images. This is a challenging task owing to a dearth of stable features to be used as tie points [(TPs), i.e., control points] in the dynamic landscapes. This letter develops an automated method to precisely register images in the lake-rich Arctic. The core premise of the method is that the centers of lakes are generally stable even if their shorelines are not. The proposed procedures first extract lakes in multitemporal satellite images, derive lake centroids and match them between images, and then use the centroids of stable lakes as TPs for image registration. The results show that this approach can achieve subpixel registration accuracy, outcompeting the conventional manual methods in both efficiency and accuracy. The proposed method is fully automated and represents a feasible way to register images for lake change detection at the pan-Arctic scale. [ABSTRACT FROM PUBLISHER]

Abstract: A new multitemporal technique is presented that allows monitoring of vegetation dynamics in coarse multispectral remote sensing data. This technique, relative spectral mixture analysis (RSMA), provides information about the amount of green vegetation (GV), nonphotosynthetic vegetation (NPV) plus litter, and snow relative to a reference time. The RSMA indices of specific ground components are defined so that they are positive when the fractional cover of a ground component is greater than that at the reference time and negative when the fractional cover is less than that at the reference time. The rationale for the new technique and its mathematical underpinnings are discussed. Example RSMA timeseries from the southern–central United States are presented based on four years of MODIS MOD43 nadir BRDF adjusted reflectance (NBAR) data. This timeseries shows that the RSMA GV index, X GV, is robust in the presence of snow. Spectral simulations show that X GV is also robust with different soil backgrounds. The RSMA index of NPV/litter cover, X NPV/litter, provides information about the dynamics of the nonphotosynthetic portion of organic matter at or above the surface. The RSMA index of total vegetation plus litter, X TV, provides information about the dynamics of the non-soil/non-snow portion of ground cover. Because it mirrors the bare ground cover, X TV may be particularly useful in remote sensing applications aimed at the study of soil erosion. [Copyright &y& Elsevier]

Disturbance events, such as fire, have a major impact on boreal forest dynamics, succession, and the global carbon cycle. Methods using satellite imagery are well established for detecting forest fires in real time and mapping the burned area (fire scars) within one year of the fire. This paper focuses on the detection of older fire disturbance-regeneration patterns in the boreal forests of Canada. Previous work found that shortwave-infrared segmentation proved particularly good at creating uniform regions that were easy to associate with fire scars. Our findings suggest it is possible to detect fire scars up to ten years old using SPOT-VEGETATION data from a single year and that the use of a vegetation index based on near- and shortwave-infrared reflectance is critical to this success. We demonstrate how the use of short-term multitemporal imagery can enhance segmentation results and present a threshold-based procedure for a posteriori identification of fire scar segments. The resulting fire scar probability map showed a good correspondence with records of fire sears mapped by the Canadian Forest Service for 1980-1992 and "hot spats" from the FireM3 Information System for 1994-1998. [ABSTRACT FROM AUTHOR]

Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12'). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12' was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12') and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices. [ABSTRACT FROM AUTHOR]