A cultivated planet in 2010 – Part 1: The global synergy cropland map

Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series

A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets

Irrigated agriculture has undergone rapid developments in China, which has greatly increased food production but overexploited water resources as well. Spatial information on irrigated cropland is critical to balance irrigation yield gains against the negative impact on water resources. However, remote-sensing-based maps on irrigated areas with short temporal coverage often suffer from undermined accuracy in humid areas and inconsistency with statistics, which limit their applications in food policy and water management. The following study integrates existing irrigation maps, observed data on irrigated cropping system, and statistics by a synergy approach to map irrigated areas in China from 2000 to 2019. We also incorporate past information on actual irrigation to avoid divergence between observations and statistics from its fluctuation. Afterwards, 614 reference samples across mainland China have been used to validate resultant maps, which show that outperformance was above overall accuracy and Kappa coefficients. Moreover, our maps share a similar spatial pattern with Irrimap-Syn maps rather than remote-sensing-based maps (CCI-LC). Irrigated areas have grown rapidly from 55.42 Mha in 2000 to 71.33 Mha in 2019 but with different growth trends in different regions. Simultaneous large-scale expansion and abandonment occur in the Huang-Huai-Hai Plain and Yangtze River Basin, while the Northwest Inland Region and the Northeast Plain are the two largest net area gains. Rainfed croplands are dominant sources of expansion, followed by pastures, respectively, with over 70% and 20% contributions in total gains. This not only is a shift from rainfed to irrigated systems but also indicates an intensification of agriculture, which might contribute to agricultural drought reductions in the north and wide soil suitability. Other efforts on agricultural sustainability also have been detected, such as geographical shifts from vulnerable to relatively suitable areas, grain for green, cropland protection, and cropland protection in the competition of urbanization

Spectroscopic Sensor Data Fusion to Improve the Prediction of Soil Nutrient Contents

This study aims to advance the understanding and application of spectroscopic sensor data fusion for improving soil nutrient content predictions. In addition to presenting an extensive review of studies on the spectroscopic sensor data fusion, a research investigation was conducted to assess the effectiveness of five fusion algorithms in predicting three primary nutrients (Nitrogen, Phosphorous, and Potassium) and two secondary nutrients (Calcium and Magnesium) in soil using Visible and Near-Infrared, Mid-Infrared, and X-ray Fluorescence data. Among the five fusion algorithms, one was a low-level fusion involving data concatenation. Two were mid-level fusions, incorporating feature extraction by applying (i) Principal Component reduction and (ii) Partial Least Squares reduction. The other two were high-level fusions, namely (i) Simple Averaging and (ii) Granger Ramanathan Averaging. The results indicate that Low-Level Fusion may not be suitable for inherently incompatible data. Mid-level fusion improved the R² by 0.1-18%, RMSE by 0-8%, and RPIQ by 0-11.5%, while high-level fusion enhanced the R² by 0-12.5%, RMSE by 0-12%, and RPIQ by 2.3-13.4%, depending on the nutrients and fusion algorithms. Despite these improvements, predictions were only satisfactory for primary nutrients, and none of the algorithms could notably enhance predictions for Phosphorous. The study also finds that fusion algorithms do not significantly improve bias. The study provided evidence of improvement in prediction accuracy with data fusion which can aid in delineating management zones for precision agriculture. It also encourages further research on novel approaches of sensor fusion and algorithms that can effectively handle non-linearity introduced due to the fusion of data. Advisor: Yufeng G

Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures

Land Use Cover Datasets and Validation Tools

This open access book represents a comprehensive review of available land-use cover data and techniques to validate and analyze this type of spatial information. The book provides the basic theory needed to understand the progress of LUCC mapping/modeling validation practice. It makes accessible to any interested user most of the research community's methods and techniques to validate LUC maps and models. Besides, this book is enriched with practical exercises to be applied with QGIS. The book includes a description of relevant global and supra-national LUC datasets currently available. Finally, the book provides the user with all the information required to manage and download these datasets

Explotación sinérgica de datos multiespectrales y radar para la estimación de variables biofísicas de la vegetación mediante tecnologías de sensoramiento remoto

Las variables biofísicas de la vegetación (VBV) son indicadores directos del crecimiento y productividad de los cultivos. Los sistemas de observación de la Tierra (EO–Earth observation) presentan oportunidades sin precedentes para el monitoreo de las variables biofísicas del trigo. Sentinel–2 (S2) es una constelación de satélites que forma parte de las misiones Sentinel del programa Copernicus de EO. El período de revisita, así como su resolución espacial y espectral, han convertido a S2 en un sistema de EO trascendental para el monitoreo de VBV. Los sistemas ópticos de EO se ven limitados con frecuencia por las condiciones climáticas tales como nubosidad o precipitaciones. En este sentido, la tecnología radar, presenta nuevas oportunidades para el monitoreo de VBV que deben explorarse en profundidad. Sentinel–1 (S1) es una constelación radar de la familia Sentinel. Debido a la complejidad de la interacción de la señal radar con las superficies cultivadas y al ruido aditivo inherente de speckle, la estimación de VBV con tecnología radar aún sigue siendo un desafío. El objetivo de esta tesis doctoral es desarrollar modelos de estimación de variables biofísicas del trigo, en una zona irrigada de cultivo intensivo al sureste de Argentina, basados en medidas in situ de la vegetación, a partir de: i) datos multiespectrales de S2; ii) datos radar de S1; y iii) la sinergia S1 & S2. Para abordar la problemática planteada, se desarrollaron en primer lugar, modelos de estimación del índice de área foliar, del contenido de clorofila de la cubierta vegetal y del contenido de agua del trigo, utilizando una base de datos multitemporal de VBV tomadas in situ, algoritmos de aprendizaje automático, una base de datos de espectros de reflectividad bidireccional de la vegetación simulados con un modelo de transferencia radiativa y datos multiespectrales de S2. Se obtuvieron modelos híbridos de estimación de estas VBV que se ajustaron con alta precisión a los datos de campo y se logró reconstruir con éxito la curva fenológica del cultivo de trigo. En segundo lugar, se implementó un modelo de estimación de LAI basado en datos radar de S1 adquiridos en diferentes geometrías de adquisición. Se probó que la estructura tridimensional de la vegetación cuando es observada desde ángulos de incidencia local diferentes proporciona información muy valiosa que puede ser utilizada para mejorar los modelos existentes. Por último, se desarrolló una estrategia de fusión de datos de S1 & S2 para reconstruir series temporales de VWC. Se aplicaron varios modelos de procesos Gaussianos de salidas múltiples para analizar la correlación cruzada existente, en el dominio de la frecuencia, entre los canales ópticos y radar. La combinación sinérgica de datos radar y ópticos mostró ser un novedoso enfoque para abordar el monitoreo de variables biofísicas del trigo en regiones intensamente cultivadas con frecuente nubosidad

Humans on Earth: Global extents of anthropogenic land cover from remote sensing

This review provides a perspective of the current state of the art in remote sensing of anthropogenic land cover and human-modified landscapes at global scales. The fact that humans have adapted to almost all of Earth’s environments, yet remain strongly clustered within each of these environments influences both the nature of anthropogenic impact on Earth’s landscapes and the challenges of mapping it. Remote sensing provides a consistent synoptic view of these environments by mapping the land cover associated with the anthropogenic land uses of settlement and food production, as well as their complement in forest cover. We give brief descriptions and illustrative comparisons of several current land cover products representing the global extents of settlements, agriculture and forests derived from remote sensing. To accommodate the challenges inherent to mapping any land cover at widely varying scales, we compare size distributions of spatially contiguous land cover (rather than total area) for several global land cover products. Despite the use of different sensors and different mapping criteria, there is remarkable consistency in the size distributions of these products – both within and across land cover class. Rank-size distributions of settlements, agricultural areas and forests are all well-described by power laws spanning more than four orders of magnitude in both area and number. This consistency in the form of the distributions suggests fundamental similarities among different types of land cover. The observed similarities can be explained by depicting land cover mosaics as co-evolving spatial networks sharing common processes of nucleation, growth and connection

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

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

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics