MAIZE YIELD ESTIMATION IN KENYA USING MODIS

Abstract. Monitoring staple crop production can support agricultural research, business such as crop insurance, and government policy. Obtaining accurate estimates through field work is very expensive, and estimating it through remote sensing is promising. We estimated county-level maize yield for the 37 maize producing countries in Kenya from 2010 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Support Vector Regression (SVR) and Random Forest (RF) were used to fit models with observed county level maize yield as a function of vegetation indices. The following five MODIS vegetation indices were used: green normalized difference vegetation index, normalized difference vegetation index, normalized difference moisture index, gross primary production, and fraction of photosynthetically active radiation. The models were evaluated with 5-fold leave one year out cross-validation. For SVR, R2 was 0.70, the Root Mean Square Error (RMSE) was 0.50 MT/ha and Mean Absolute Percentage Error (MAPE) was 27.6%. On the other hand for RF these were 0.69, 0.51 MT/ha and 29.3% respectively. These results are promising and should be tested in specific applications to understand if they are good enough for use

Towards integrating climate security in the National Climate Change Action Plan (NCCAP) 2023-2027

The UNDP & Life Peace Institute (LPI) report on mapping climate security adaptations highlights that the link between climate and conflict depends on the interplay of exposure to climate hazards, vulnerability, and coping capacity of states and communities. Therefore, due increasing impacts of Climate Change (CC), it is crucial for the Kenyan governments to identify and mitigate climate security1 risks both at national and county levels. The National Climate Action Plan (NCCAP) outlines the Kenyan government’s strategy on mitigation and adaptation to climate change and its effects. With the expiry of the 2017–2022 NCCAP, preparation of a new plan is underway based on the current climate change challenges and lessons learnt from the previous NCCAP phase. One of the issues that stood out in the 2017–2022 NCCAP was the omission of climate security strategies. In this regard, a workshop was co-organized by the Alliance of Bioversity International and CIAT (ABC) and Kenya Red Cross Society at Panari Hotel, Nairobi from 24th — 25th July 2023 to integrate Climate Security. The workshop participants comprised of takeholders drawn from various sectors including government ministries, research organizations, humanitarian organizations, and local and regional economic blocs

Spatial Analysis: CGIAR Climate Security Observatory.

The Climate Security Observatory (CSO) is an online platform for stakeholder decision-making that provides access to a range of global analyses related to climate and security. The CSO is based on an integrated climate security framework that helps understand the complexity of the climate-security interface. As part of the CSO Methods Paper Series, this report details the method used for Spatial Analysis

Towards a common vision of climate security in Guatamela

CGIAR’s Climate Resilience Initiative, also known as ClimBeR, is working to address these needs. On the 21st and 22nd of February, we ran in Guatemala City the first climate security workshop in Central America: Towards a common vision on the relationship between climate, conflict, and human security in Guatemala. The workshop was organized by the Alliance of Bioversity and CIAT along with the CGIAR’s Climate Resilience Initiative; the Fragility, Conflict, and Migration Initiative; the regional integrated initiative AgriLAC Resiliente; and the CGIAR FOCUS Climate Security and benefited from the participation of 45 individuals from 20 different organizations, including regional & local organizations, government institutions, UN agencies, and national universitie

Hacia una visión compartida sobre la seguridad climática en Guatemala

Este infore explora las interconexiones entre el cambio climático, la seguridad humana y los conflictos en Guatemala basándose en el Taller sobre Seguridad Climática celebrado en Ciudad de Guatemala los días 21 y 22 de febrero de 2023. Las implicaciones del cambio climático para la seguridad, comúnmente conocidas como el nexo clima-seguridad, han sido ampliamente discutidas tanto en círculos políticos como académicos. La seguridad climática se refiere las amenazas y riesgos de seguridad a estados, sociedades e individuos causados directa o indirectamente por los efectos del cambio climático. Los riesgos de seguridad en este documento son entendidos de una manera amplia enfocándose no solo en los riesgos de seguridad nacional vistos desde el punto de vista de los estados sino, principalmente, en los riesgos de seguridad humana enfocados en los retos para la supervivencia y los medios de vida de la población que incluye la seguridad económica, alimentaria, sanitaria, medioambiental, personal, comunitaria y política (UNTFHS, 2016)

Spatial-temporal Dynamic Conditional Random Fields crop type mapping using radar images

The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while the global demand for food has grown twofold. There is need for continuous monitoring and spatial information update of agriculture activities. This will support decision and policy making organs to take necessary actions towards enhancing food security. However, economic factors, farm management, natural aspects (such as weather, soils e.t.c.) and government policy for instance, influence types of crops planted in a season. Therefore, data acquisition and mapping methods need to consider these dynamics. The study adopts microwave remote sensing with synthetic aperture radar (SAR) for data acquisition. Microwave remote sensing is daylight and weather independent thus guarantees the highest temporal density of images regardless of climatic zones. This also means that images at different phonological stages can be captured by radar sensors. Crop phenology is dynamic as it changes spatially in different times of the year. Such biophysical processes also look spectrally different to radar sensors. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, crop mapping methods using single-date remote sensing images can not offer optimal results in case of crops with similar phenology. In addition, methods stacking images within a cropping season for classification limits discrimination to a single high dimensional feature space vector that can suffer from overlapping classes. However, phenology can aid discrimination of crops since their backscatter varies with time. Therefore, this research seeks to fill this gap by developing a crop sequence classification method using multitemporal SAR images. The method is built to use spatial and temporal context. The study designed first order and higher order undirected Dynamic Conditional Random Fields (DCRFs) for spatial-temporal crop classification. Basically, the DCRFs model has a repeated structure of temporally connected conditional random fields (CRFs). Each node in the sequence is connected to its temporal neighbours via conditional probability matrix. The matrix is computed using posterior class probabilities estimated by random forest classifier. We use the matrix on one hand to encode expert and image based phenological information in higher order DCRFs. On the other hand, the matrix integrates only image based phenological information in first order DCRFs. When compared to independent epoch classification, the designs improved crop discrimination at each epoch with higher order DCRFs having the highest accuracy in the sequence. However, stakeholders and policy makers need to know the quantity and spatial coverage of crops in a given season so as to ensure food security and a balanced ecosystem. Therefore, we went an extra step to develop a DCRFs ensemble classifier. The DCRFs ensemble considers a set of computed posterior crop type probabilities at each epoch in order to generate an optimal label of a node. This is done by maximizing over posterior crop type probabilities selected from the sequence based on maximum F1-score and weighted by user accuracy. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using maximum likelihood classifier (MLC) and CRFs. So far it outperforms MLC and CRFs using crop type posterior probabilities estimated by both first and higher order DCRFs

Spatial-temporal Dynamic Conditional Random Fields crop type mapping using radar images

The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while the global demand for food has grown twofold. There is need for continuous monitoring and spatial information update of agriculture activities. This will support decision and policy making organs to take necessary actions towards enhancing food security. However, economic factors, farm management, natural aspects (such as weather, soils e.t.c.) and government policy for instance, influence types of crops planted in a season. Therefore, data acquisition and mapping methods need to consider these dynamics. The study adopts microwave remote sensing with synthetic aperture radar (SAR) for data acquisition. Microwave remote sensing is daylight and weather independent thus guarantees the highest temporal density of images regardless of climatic zones. This also means that images at different phonological stages can be captured by radar sensors. Crop phenology is dynamic as it changes spatially in different times of the year. Such biophysical processes also look spectrally different to radar sensors. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, crop mapping methods using single-date remote sensing images can not offer optimal results in case of crops with similar phenology. In addition, methods stacking images within a cropping season for classification limits discrimination to a single high dimensional feature space vector that can suffer from overlapping classes. However, phenology can aid discrimination of crops since their backscatter varies with time. Therefore, this research seeks to fill this gap by developing a crop sequence classification method using multitemporal SAR images. The method is built to use spatial and temporal context. The study designed first order and higher order undirected Dynamic Conditional Random Fields (DCRFs) for spatial-temporal crop classification. Basically, the DCRFs model has a repeated structure of temporally connected conditional random fields (CRFs). Each node in the sequence is connected to its temporal neighbours via conditional probability matrix. The matrix is computed using posterior class probabilities estimated by random forest classifier. We use the matrix on one hand to encode expert and image based phenological information in higher order DCRFs. On the other hand, the matrix integrates only image based phenological information in first order DCRFs. When compared to independent epoch classification, the designs improved crop discrimination at each epoch with higher order DCRFs having the highest accuracy in the sequence. However, stakeholders and policy makers need to know the quantity and spatial coverage of crops in a given season so as to ensure food security and a balanced ecosystem. Therefore, we went an extra step to develop a DCRFs ensemble classifier. The DCRFs ensemble considers a set of computed posterior crop type probabilities at each epoch in order to generate an optimal label of a node. This is done by maximizing over posterior crop type probabilities selected from the sequence based on maximum F1-score and weighted by user accuracy. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using maximum likelihood classifier (MLC) and CRFs. So far it outperforms MLC and CRFs using crop type posterior probabilities estimated by both first and higher order DCRFs