The outlining of agricultural plots based on spatiotemporal consensus segmentation

Producción CientíficaThe outlining of agricultural land is an important task for obtaining primary information used to create agricultural policies, estimate subsidies and agricultural insurance, and update agricultural geographical databases, among others. Most of the automatic and semi-automatic methods used for outlining agricultural plots using remotely sensed imagery are based on image segmentation. However, these approaches are usually sensitive to intra-plot variability and depend on the selection of the correct parameters, resulting in a poor performance due to the variability in the shape, size, and texture of the agricultural landscapes. In this work, a new methodology based on consensus image segmentation for outlining agricultural plots is presented. The proposed methodology combines segmentation at different scales—carried out using a superpixel (SP) method—and different dates from the same growing season to obtain a single segmentation of the agricultural plots. A visual and numerical comparison of the results provided by the proposed methodology with field-based data (ground truth) shows that the use of segmentation consensus is promising for outlining agricultural plots in a semi-supervised manner.Agencia Estatal de Investigación (AEI) y Fondo Europeo de Desarrollo Regional (FEDER) a través del proyecto ARTeMISat-2: Procesamiento Avanzado de Datos de Teledetección para el Seguimiento y Gestión Sostenible de Recursos Marinos y Terrestres en Ecosistemas Vulnerables (project CTM2016-77733-R)Centro de Investigaciones Hídricas para la Agricultura y la Minería (project CONICYT–FONDAP–1513001)Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA026P17

Multi-scale targeting of land degradation in northern Uzbekistan using satellite remote sensing

Advancing land degradation (LD) in the irrigated agro-ecosystems of Uzbekistan hinders sustainable development of this predominantly agricultural country. Until now, only sparse and out-of-date information on current land conditions of the irrigated cropland has been available. An improved understanding of this phenomenon as well as operational tools for LD monitoring is therefore a pre-requisite for multi-scale targeting of land rehabilitation practices and sustainable land management. This research aimed to enhance spatial knowledge on the cropland degradation in the irrigated agro-ecosystems in northern Uzbekistan to support policy interventions on land rehabilitation measures. At the regional level, the study combines linear trend analysis, spatial relational analysis, and logistic regression modeling to expose the LD trend and to analyze the causes. Time series of 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), summed over the growing seasons of 2000-2010, were used to determine areas with an apparent negative vegetation trend; this was interpreted as an indicator of LD. The assessment revealed a significant decline in cropland productivity across 23% (94,835 ha) of the arable area. The results of the logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table, land-use intensity, low soil quality, slope, and salinity of the groundwater. To quantify the extent of the cropland degradation at the local level, this research combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping based on multitemporal Landsat TM images from 1998 and 2009. Spatial distribution of fields with decreased vegetation cover is mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison of the Landsat-based map with the LD trend map yielded an overall agreement of 93%. The proposed methodological approach is a useful supplement to the commonly applied trend analysis for detecting LD in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. To contribute to land rehabilitation options, a GIS-based multi-criteria decision-making approach is elaborated for assessing suitability of degraded irrigated cropland for establishing Elaeagnus angustifolia L. plantations while considering the specific environmental setting of the irrigated agro-ecosystems. The approach utilizes expert knowledge, fuzzy logic, and weighted linear combination to produce a suitability map for the degraded irrigated land. The results reveal that degraded cropland has higher than average suitability potential for afforestation with E. angustifolia. The assessment allows improved understanding of the spatial variability of suitability of degraded irrigated cropland for E. angustifolia and, subsequently, for better-informed spatial planning decisions on land restoration. The results of this research can serve as decision-making support for agricultural planners and policy makers, and can also be used for operational monitoring of cropland degradation in irrigated lowlands in northern Uzbekistan. The elaborated approach can also serve as a basis for LD assessments in similar irrigated agro-ecosystems in Central Asia and elsewhere.Multisclare Bewertung der Landdegradation in Nord-Uzbekistan unter der Verwendung von Satellitenfernerkundung Die zunehmende Landdegradation (LD) in den bewässerten Agrarökosystemen in Usbekistan behindert die nachhaltige Entwicklung dieses vorwiegend landwirtschaftlich geprägten Landes. Bis heute sind nur wenige und veraltete Informationen über die aktuellen Bodenbedingungen der bewässerten Anbauflächen verfügbar. Ein besseres Verständnis dieses Phänomens sowie operationelle Werkzeuge für LD-Monitoring sind daher Voraussetzung für ein nachhaltiges Landmanagement sowie für Landrehabilitationsmaßnahmen. Ziel dieser Studie war es, das räumliche Verständnis der Degradierung von Anbaugebieten in den bewässerten Agrarökosystemsn des nördlichen Usbekistans zu verbessern, um staatliche Interventionen in Bezug auf Landrehabilitationsmaßnahmen zu unterstützen Auf der regionalen Ebene kombiniert die Studie lineare Trendanalyse, räumliche relationale Analyse sowie logistischer Regressionsmodellierung, um den LD-Trend darzustellen und Gründe zu analysieren. Zeitreihen von 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) Bildern wurden für den Zeitraum der Anbauperioden zwischen 2000-2010 untersucht, um Bereiche mit einem offensichtlich negativen Vegetationstrend zu ermitteln. Dieser negative Trend kann als Indikator für LD interpretiert werden. Die Untersuchung ergab eine signifikante Abnahme der Bodenproduktivität auf 23% (94,835 ha) der Anbaufläche. Zudem deuten die Ergebnisse der logistischen Modellierung darauf hin, dass das räumliche Muster des beobachteten Trends überwiegend mit der Höhe des Grundwasserspiegels, der Landnutzungsintensität, der geringen Bodenqualität, der Hangneigung sowie der Grundwasserversalzung zusammenhängt. Um das Ausmaß der Degradation der Anbauflächen auf der lokalen Ebene zu quantifizieren, kombiniert diese Studie objektbasierte Erkennung von Veränderungen und spektrale Mischungsanalyse für die Abnahme der Vegetationsbedeckung auf der Grundlage von multitemporalen Landsat-TM-Bildern im Zeitraum von 1998 bis 2009. Die räumliche Verteilung der Felder mit abnehmender Vegetationsbedeckung hängt überwiegend mit verlassenen Anbauflächen sowie mit nährstoffarmen Böden in den Randbereichen des Bewässerungssystems und an den Grenzen zu natürlichen Sandwüsten zusammen. Ein Vergleich mit der Karte des LD-Trends ergab insgesamt eine Übereinstimmung von 93%. Der vorgeschlagene Ansatz ist eine nützliche Ergänzung zu der häufig angewendeten Trendanalyse für die Ermittlung von LD in Regionen, für die keine Satellitenbildzeitreihen mit hoher Auflösung verfügbar sind. Als Beitrag zu Landrehabilitationsmöglichkeiten, wird ein GIS-basierter Multi-Kriterien-Ansatz zur Einschätzung der Eignung von degradierten bewässerten Anbauflächen für Elaeagnus angustifolia L. Plantagen beschrieben, der gleichzeitig die spezifischen Umweltbedingungen der bewässerten Agrarökosysteme berücksichtigt. Dieser Ansatz beinhaltet Expertenwissen, Fuzzy-Logik und gewichtete lineare Kombination, um eine Eignungskarte für die bewässerten degradierten Anbauflächen herzustellen. Die Ergebnisse zeigen, dass diese Flächen ein überdurchschnittliches Eignungspotenzial für die Aufforstung mit E. angustifolia aufweisen. Diese Studie trägt zu einem verbesserten Verständnis der räumlichen Variabilität der Eignung von solchen Flächen für E. angustifolia bei. Die Ergebnisse dieser Studie können als Entscheidungshilfe für landwirtschaftliche Planer und politische Entscheidungsträger sowie für verbesserte Landrehabilitationsmaßnahmen und operationelles Monitoring der Degradation von Anbauflächen im nördlichen Usbekistan eingesetzt werden. Zudem kann der beschriebene Ansatz als Grundlage für LD-Untersuchungen in ähnlichen bewässerten Agrarökosystemen in Zentralasien und anderswo dienen

INTEGRATING MULTI-SOURCE DATA TO QUANTIFY CHANGES IN BIOMASS AND SOIL ORGANIC CARBON DUE TO LAND-USE CHANGE IN THE BOREAL PLAINS ECOZONE, CANADA

Land use and cover change (LUCCs) is the second largest source of global carbon emission and there has been a growing interest in LUCCs to mitigate climate change effects. Global land-use change associated with cropland expansion, which is a major carbon source, was dominant in the last century. Abandoned cropland typically is a carbon sink and was observed in many regions in the recent decades. However, there has been little research on carbon balance resulting from LUCCs in agricultural landscapes, especially under abandoned cropland in Canada. Information on carbon balance resulting from LUCCs is necessary for national greenhouse gas (GHG) inventories as well as emission mitigation options. The primary objective of the study is to quantify carbon stocks and dynamics as consequences of LUCCs in the Boreal Plains Ecozone, Canada. Field measurement on carbon stocks in abandoned cropland was assessed at field sites in Saskatchewan. Vegetation C ranged from 7.6 to 90.1 Mg C ha-1 and increased linearly with stand age. Ecosystem C increased from 74.2 to 137.6 Mg C ha-1 after 41 years of abandonment (or net C sink of 1.9 Mg C ha-1 yr-1). In the agriculture region of the Boreal Plains Ecozone, land-use change accounted for 6.5% of the total area during the 1990-2000 period. Forest to cropland conversion was dominant on well-drained Chernozemic and Luvisolic soil orders. Abandoned cropland occurred mainly on poorly drained and acidic parent materials. LUCCs in agriculture region was estimated to be a net C sink of 0.76 ±0.3 Mg C ha-1 yr-1 during this period. In the agriculture-forest transition region of the Boreal Plains Ecozone, substantial land-use changes occurred in pasture (+76%) and summer fallow (-87.8%) over a 27-year period (1984 - 2011). The shrub and forest area was reduced -31.6% and -16.4%, respectively. Forest disturbances occurred mainly during 2005 – 2011. Substantial changes of summer fallow to annual cropland took place on the higher soil capability land and annual cropland to pasture conversion was more likely on lower capability soil classes. We estimated that LUCCs in the region was a net C source of approximately 552.7 Gg C across the research period or 0.07 Mg C ha-1 yr-1

Detection of grassland mowing frequency using time series of vegetation indices from Sentinel-2 imagery

5openInternationalItalian coauthor/editorManagement intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity, but the potential of using vegetation indices from Sentinel-2 imagery for its retrieval has not been fully exploited. In this work we developed on the Google Earth Engine platform a four-phases algorithm to identify mowing frequency, including i) vegetation index time-series computing, ii) smoothing and resampling, iii) mowing detection, and iv) majority analysis. Mowing frequency during 2020 of 240 ha of grassland fields in the Italian Alps was used for algorithm optimization and evaluation. Six vegetation indexes (EVI, GVMI, MTCI, NDII, NDVI, RENDVI783.740) were tested as input to the proposed algorithm. The Normalized Difference Infrared Index (NDII) showed the best performance, resulting in mean absolute error of 0.07 and 93% overall accuracy on average at the four sites used for optimization, at pixel resolution. A slightly lower accuracy (mean absolute error = 0.10, overall accuracy = 90%) was obtained aggregating the maps to management parcels. The algorithm showed a good generalization ability, with a similar performance between global and local optimization and an average mean absolute error of 0.12 and an overall accuracy of 89% on average on the sites not used for parameters optimization. The lowest accuracies occurred in intensively managed grasslands surveyed by one satellite orbit only. This study demonstrates the suitability of the proposed algorithm to monitor very fragmented grasslands in complex mountain ecosystems. Google Earth Engine was used to develop the model and will enable researchers, agencies and practitioners to easily and quickly apply the code to map grassland mowing frequency for extensive grasslands protection and conservation, for mowing event verification, or for forage system characterization.openAndreatta, Davide; Gianelle, Damiano; Scotton, Michele; Vescovo, Loris; Dalponte, MicheleAndreatta, D.; Gianelle, D.; Scotton, M.; Vescovo, L.; Dalponte, M

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius–Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely-used convolutional network, U-Net, achieving averaged modified Kling–Gupta Efficiency of 0.88 and 0.94 on the test data set for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Remote sensing based assessment of small wetlands in East Africa

Small wetlands in East Africa have in the past few decades become focal points of a broad spectrum of agricultural production and other land-uses. Climate change and population growth are the major factors attributing to increasing use and change of the wetlands. This study aimed at detecting the distribution and extent of small wetlands in Tanzania and Kenya, classifying them into different types, identifying their use patterns and quantifying changes that have taken place from 1976 to 2003. Field and aerial surveys were conducted; microwave (ALOS-PALSAR, ENVISAT-ASAR, and TerraSAR-X) and optical (LANDSAT and aerial photographs) data, were used to detect spatial distribution of the wetlands using automated and semi automated techniques. Time series LANDSAT images were applied in classification and change detection by post classification comparison (PCC), change vector analysis (CVA) and land use change mapper (LCM). Maps and socio-economic data were also gathered. Driving forces of change were determined qualitatively using group discussions with key informants. Two types of small wetlands were mainly identified, inland valleys located in the humid highlands and covering 87% of the total surveyed area as well as floodplains in sub-humid lowlands and semi-arid highlands covering the remaining 13%. Eight major land cover and uses were identified with accuracies between 82.76 and 95.17%. Cropland was a dominant land use occupying 57% of the inland valleys and 35% in the flood plains; others included open water, floating vegetation, permanent papyrus swamps, semi-natural vegetation, grazing, shrubs, settlements and bare land. The cover and uses are unevenly distributed between the types and sites. The major change detected was expansion of cropped land at the expense of natural vegetation. This accounted for 56% of the change in the highland flood plain and 52% in the lowland floodplain. Shrubs proliferated in all wetlands, which is indicated by more than 50% compared to their area coverage in 1976. Climate change, population increase, unemployment, market access, wetland physical access and insufficient knowledge on the use are among the proximate causes of the wetland changes. Underlying factors like poor enforcement of wetland law and policy in Kenya and lack of the same in Tanzania have accelerated these changes. Combinations of remote sensing data and image processing methods played an important role in achieving the objectives of the study. Optical data proved to be very useful in delineation of small wetlands while microwave data delineated larger areas. The spatial resolution of the images has also proved to be a key factor in studies of small wetlands. To ameliorate the wetlands, it is recommended that a balance is attained between the use and conservation. Policy formulation and law enactment in Tanzania and enforcement of the existing policy and law in Kenya is seen to support wise use. Awareness creation is also important to lessen the over and inappropriate utilization of the wetlands

Modeling grassland productivity through remote sensing products

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour l’Observation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources