MAPPING CULTIVATED AREA IN WEST AFRICA USING MODIS IMAGERY AND AGROECOLOGICAL STRATIFICATION

ABSTRACT The northern fringe of sub-Saharan Africa is a region that is considered particularly vulnerable to climate variability and change, and food security remained there a major challenge. To address this issue, major international research efforts are being deployed, coordinated by the ongoing project AMMA (African Monsoon Multidisciplinary Analyses). Its aim is to better understand the West African Monsoon and its variability, and to improve the predictions of the impacts of this variability on West African societies. One of the preliminary stages necessary for analysing such impacts on agriculture and food security is a reliable estimation of the cultivated domain at national level, a scale compatible with climate change studies. The opportunity of using satellite remote sensing for agricultural statistics has been explored by the research community as well as by national departments of agriculture during the last few decades In this study, we develop a methodology for extracting cultivated areas based on their temporal behaviour as captured in time-series of moderate resolution remote sensing images. We tested this methodology in Senegal and Mali at national scale. -First, 46 MODIS 16-days composite NDVI images (MOD13Q1/V04 product, 250 m spatial resolution) were acquired for 2004 and 2005 and NDVI time series were generated. These products include a NDVI quality band (QB). Although MODIS images have already been radiometrically corrected, we noticed some radiometric defects and noises. For dates with a Vegetation Indices Usefulness Index value in the QB data set lower than "good" quality, NDVI values were replaced by linearly interpolated values from the two closest surrounding dates with "good", "high", or "perfect" quality. The required set of tools was developed with ID

A Survey of Satellite Biological Sensor Application for Terrestrial and Aquatic Ecosystems

The state of the ecosystems can be inferred in two ways, known as bioinference. One way (ground-based) is the use of some organisms to determine the environmental conditions within an ecosystem. The other is the use of multiband airborne or satellite imagery to identify the vegetation cover status, and also to track the biological diversity in marine ecosystems such as coral reef status, resources variation, and pollution. The standard example for the first state is the plankton as they represent a primary tool for ecologists to assess the health state of the marine environment. Their fast responses to the variability of the ecosystem, their nonexploitation as commercial organisms, and their favoring of subtle environmental conditions have suggested them to be bioindicators of climate variability. These organisms can be used to identify many environmental problems including water acidification, eutrophication, and pollution. Remote sensing technique is being widely used today to solve many environmental problems due to the broad view and accuracy of the results and its participation in determining the environmental conditions of different ecosystems. For example, remote sensing applications are used in vegetation and mangrove ecosystem management. Moreover, it is used to assess eutrophication problems by multiband spectrum remote sensing

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results

Quantification of regional carbon stocks in the ecoregions of Cross River State, Nigeria

Quantification of above-ground biomass over the Cross River State, Nigeria using Sentinel 2 data: Higher-resolution wall-to-wall carbon monitoring in tropical Africa across a range of woodland types is necessary in reducing uncertainty in the global carbon budget and improving accounting for REDD+. This study uses Sentinel-2 multispectral imagery combined with climatic and edaphic variables to estimate the regional distribution of above-ground biomass (AGB) for the year 2020 over the CRS, a tropical forest region in Nigeria, using the Random Forest (RF) machine learning. Forest Inventory plots were collected over the whole state for training and testing of the RF algorithm, and spread over undisturbed and disturbed tropical forests, and woodlands in croplands and plantations. The maximum plot AGB was estimated to be 588 t/ha with an average of 121.98 t/ha across the entire CRS. The AGB was estimated using Random Forest and yielded an R2 of 0.88, RMSE of 40.9 t/ha, a relRMSE of 30 %, bias of +7.5 t/ha and a total woody AGB of 0.246 Pg for CRS. These results compare favourably to previous tropical AGB products; with total AGB of 0.290, 0.253, 0.330 and 0.124 Pg, relRMSE of 49.69, 57.09, 24.06 56.24 % and -41, -48, -17 t/ha bias over the CRS for the Saatchi, Baccini, Avitabile and ESA CCI maps respectively. These are all compared to the current REDD+ estimate of total AGB over the Cross River State of 0.268 Pg. This study shows that obtaining independent reference plot datasets, from a variety of woodland cover types, can reduce uncertainties in local to regional AGB estimation compared with those products which have limited tropical African and Nigerian woodland reference plots. Though REDD+ biomass in the region is relatively larger than the estimates of this study, REDD+ provided only regional biomass rather than pixel-based biomass and used estimated tree height rather than the actual tree height measurement in the field. These may cast doubt on the accuracy of the estimated biomass by REDD+. These give the biomass map of this current study a comparative advantage over others. The 20 m wall-to-wall biomass map of this study could be used as a baseline for REDD+ Monitoring, Evaluation and Reporting for equitable distribution of payment for carbon protection benefits and its management. Digital mapping of soil organic carbon from sentinel-2 data in the tropical ecosystem of Cross River State, southeast-Nigeria: Digital mapping of Soil organic carbon (SOC) is fundamental in achieving the mandates of the REDD project. As an essential climate variable, SOC is a constituent of the ecological system that supports chemical, biological and physical processes and can be used to infer the quality of the ecosystem. In Nigeria, estimates revealed that 40 percent of greenhouse gas (GHG) emissions comes from the forestry and land use sector. On the strength of this, the quantification of the total SOC stock in CRS Nigeria, will aid in putting in place land use policies that will achieve the twin goal of SOC protection and enhance the living conditions of those whose livelihood is nature dependent. This study used random forest (RF) regression; a machine learning algorithm to identify key predictors of SOC through the integration of field, Sentinel 2A (S2) derived vegetation indices, selected reanalysis climate variables with topography. Three land cover types (LCTs); undisturbed, disturbed and croplands were purposively mapped out, and 72 soil samples collected at soil depth of 20 cm across the study area. 70 % of points data were used to train the RF model while the remaining 30 % was used to validate the predicted SOC model. We estimated 0.147 Pg with mean of 72.94 t/ha of SOC compared to African Soil Information Service (fSIS) 0.124 Pg and Innovative Solution for Digital Agriculture (ISDA) 0.217 Pg of SOC over the area. Model analysis indicates that key predictors (topography, rainfall, maximum air temperature, OSAVI, EVI and NDVI) achieved a high prediction accuracy with lower uncertainty unlike the global and continental SOC maps over the study area (R2 of 0.82, RMSE of 22.54 (t/ha), and uncertainty of 39.4 % compared to AfSIS; RMSE=35.29 t/ha, uncertainty=61.69 % and iSDA; RMSE= 38.58 t/ha, uncertainty=57.21 %). Our results showed lower uncertainty compared to the coarse spatial resolution maps of AfSIS (30 m) and ISDA (250 m). The final model output was used to spatialize the SOC distribution across the CRS subregion using ArcGIS package. The 20 m resolution SOC map of this study could be referenced in the REDD+ Monitoring, Evaluation and Reporting for equitable distribution of payment for carbon protection benefits and its management. Livelihood impacts of forest carbon protection in the context of redd+ in Cross River State, southeast Nigeria: The rate of landcover change linked to deforestation and forest degradation in tropical environments has continued to surge despite series of forest governance policy instruments over the years. These informed the launch of one of the most important international policies called Reducing Emission from Deforestation and Forest Degradation Plus (REDD+) to combat forest destruction. REDD+ assumes that communities will have increased access to natural capital which will enhance their livelihood portfolio and mitigate the effects of climate variability and change across biomes. The aim of this study is to ascertain the livelihoods impacts of forest carbon protection within the context of REDD+ in Cross River State, Nigeria. Six forest communities were chosen across three agroecological zones of the State. Anchored on the Sustainable Livelihood Framework, a set of questionnaires were administered to randomly picked households. The results indicate that more than half of the respondents aligned with financial payment and more natural resources as the perceived benefits of carbon protection. More so, a multinomial logistic regression showed that income was the main factor that influenced respondent’s support for forest carbon protection. Analysis of income trends from the ‘big seven’ non-timber forest resources in the region showed increase in Gnetum africanum, Bushmeat, Irvingia gabonensis, Garcinia kola, while carpolobia spp., Randia and rattan cane revealed declining income since inception of REDD+. The recorded increase in household income was attributed to a ban in logging. It is recommended that the forest communities should be more heavily involved in the subsequent phases of the project implementation to avoid carbon leakages

Assessing complex interactions between human and agro-ecosystem using satellite information. A case study in Katuk Odeyo, Western Kenya.

The objective of this study is to integrate socioeconomic, biophysical, and remote-sensing information to enhance the understanding of climate change, agriculture and food security within and between CCAFS sites. The purpose is to assess the agricultural production system in the CCAFS site Katuk Odeyo, Nyando (Western Kenya) to explore potential indicators that can be long-term monitored. Ecosystem health determines energy supply and demand by sustaining the productive capacity of the landscape. The study uses a pixel-based RapidEye satellite image classification and assessment of agroecosystem health (for agricultural practices and landscape health relations) to characterise Katuk Odeyo into four functional agro-zones: highly intensive agro-zone condition (IAC), good agricultural condition (GAC), potential agricultural condition (PAC), and semi agricultural condition (SAC)

Using multi-resolution remote sensing to measure ecosystem sensitivity and monitor land degradation in response to land use and climate variability

Climate change and land degradation, which is defined as the decline in the productive capacity of the land, have profound implications for resource-based livelihoods and food security. In this dissertation, I use remote sensing to improve understanding of how climate variability affects the productivity of global pasturelands and to quantify the spatial and temporal patterns of land degradation in the Southern Cone region (SCR) of South America. In the first chapter, I characterize the sensitivity of global pastureland productivity to climate variability by analyzing the relationship between MODIS enhanced vegetation index and gridded precipitation data. Results show that pasturelands are least capable of withstanding precipitation deficits in Australia, while pasturelands in Latin America recover more slowly after drought compared to other regions. In the second chapter, I use Landsat observations to measure the magnitude, geography, and rate of change in the amount of bare ground, herbaceous and woody vegetation in the SCR since 1999. Paraguay experienced the highest proportional increase in herbaceous cover as a result of agricultural expansion and intensification, while Uruguay experienced the highest proportional increase in woody cover as a result of afforestation. Argentina, the largest and most heterogeneous country in the SCR, experienced widespread land cover changes from deforestation, reforestation, afforestation, and desertification, each of which varied in extent and magnitude by ecoregion. In the third chapter, I assess patterns of land degradation in the SCR using the United Nations Sustainable Development framework. My results show that 67.5% of the SCR experienced changes in land cover properties in the 21st century, with widespread improvement (i.e., increased productive capacity), along with substantial hotspots of degradation caused by expansion of agriculture and systematic decreases in precipitation. Monitoring degradation is necessary to assess ecosystem services, ensure food security, and develop land use policies designed to increase the resilience of land systems to the joint stresses imposed by climate change and a growing global population. The methods, datasets, and results from this dissertation provide an improved basis for creating such policies in some of the world’s most vulnerable and food insecure regions

Using satellite remote sensing and hydrologic modeling to improve understanding of crop management and agricultural water use at regional to global scales.

Thesis (Ph. D.)--Boston UniversityCroplands are essential to human welfare. In the coming decades , croplands will experience substantial stress from climate change, population growth, changing diets, urban expansion, and increased demand for biofuels. Food security in many parts of the world therefore requires informed crop management and adaptation strategies. In this dissertation, I explore two key dimensions of crop management with significant potential to improve adaptation pathways: irrigation and crop calendars. Irrigation, which is widely used to boost crop yields, is a key strategy for adapting to changes in drought frequency and duration. However, irrigation competes with household, industrial, and environmental needs for freshwa t er r esources. Accurate information regarding irrigation patterns is therefore required to develop strategies that reduce unsustainable water use. To address this need, I fused information from remote sensing, climate datasets, and crop inventories to develop a new global database of rain-fed, irrigated, and paddy croplands. This database describes global agricultural water management with good realism and at higher spatial resolution than existing maps. Crop calendar management helps farmers to limit crop damage from heat and moisture stress. However, global crop calendar information currently lacks spatial and temporal detail. In the second part of my dissertation I used remote sensing to characterize global cropping patterns annually, from 2001-2010, at 0.08 degree spatial resolution. Comparison of this new dataset with existing sources of crop calendar data indicates that remote sensing is able to correct substantial deficiencies in available data sources. More importantly, the database provides previously unavailable information related to year-to-year variability in cropping patterns. Asia, home to roughly one half of the Earth's population, is expected to experience significant food insecurity in coming decades. In the final part of my dissertation, I used a water balance model in combination with the data sets described above to characterize the sensitivity of agricultural water use in Asia to crop management. Results indicate that water use in Asia depends strongly on both irrigation and crop management, and that previous studies underestimate agricultural water use in this region. These results support policy development focused on improving the resilience of agricultural systems in Asia

Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development

environmental economics; biodiversity; sustainable developmen

Earth resources: A continuing bibliography with indexes (issue 61)

This bibliography lists 606 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors, and economic analysis