Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Variáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat.

Abstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived Landsat vegetation indices related to coffee plots in order to obtain relationships to yield of the same coffee plots. Biophisical variables and yield data were colected in interviews with farmers from four locations in the whole largest Brazilian coffee-exporting province. Vegetation indices and biophysical variables were selected through stepwise regression in order to obtain the best regression models to estimate coffee yield. Outcomes showed that general models and specific models obtained for Mundo Novo variety presented Pearson's correlation coeficients (r) from 0,64 to 0,71 while models for Catuaí variety showed better results (r = 0,85). Although coffee yield cannot be estimated exclusively from these models, they can be usefull coupled with agrometeorogical models for estimating coffee yield

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August, 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses

Variáveis multitemporais para o mapeamento de áreas de cultivo de café

The objective of this work was to propose a new methodology for mapping coffee cropping areas that includes multitemporal data as input parameters in the classification process, by using the Landsat TM NDVI time series, together with an object-oriented classification approach. The algorithm BFAST was used to analyze coffee, pasture, and native vegetation temporal profiles, allied to a geographic object-based image analysis (GEOBIA) for mapping. The following multitemporal variables derived from the R package greenbrown were used for classification: mean, trend, and seasonality. The results showed that coffee, pasture, and native vegetation have different temporal behaviors, which corroborates the use of these data as input variables for mapping. The classifications using temporal variables, associated with spectral data, achieved high-global accuracy rates with 93% hit. When using only temporal data, ratings also showed a hit percentage above 80% accuracy. Data derived from Landsat TM time series are efficient for mapping coffee cropping areas, reducing confusion between targets and making the classification process more accurate, contributing to a correct characterization and mapping of objects derived from a RapidEye image, with a high spatial solution.O objetivo deste trabalho foi propor uma nova metodologia para o mapeamento de áreas cafeeiras que inclui dados multitemporais como parâmetros de entrada no processo de classificação, por meio de uma série temporal NDVI do Landsat TM, juntamente com uma abordagem de classificação orientada a objeto. O algoritmo BFAST foi utilizado para a análise dos perfis temporais de café, pastagem e vegetação nativa, aliada à análise da imagem baseada em objetos geográficos. Para a classificação, utilizaram-se as seguintes variáveis multitemporais derivadas do pacote greenbrown R: média, tendência e sazonalidade. Os resultados mostraram que o café, a pastagem e a vegetação nativa têm comportamentos temporais distintos, o que corrobora o uso destes dados como variáveis de entrada para o mapeamento. As classificações com uso das variáveis temporais, associadas a dados espectrais, obtiveram altos índices de acurácia global com 93% de acerto. Quando utilizados somente os dados temporais, as classificações ainda mostraram um percentual de acerto acima de 80%. Dados oriundos de séries temporais do Landsat TM são eficientes para o mapeamento de áreas de cultivo cafeeiro, diminuindo a confusão entre os alvos e tornando o processo de classificação mais preciso, o que contribui para a caracterização e o mapeamento de objetos derivados de uma imagem RapidEye, com alta resolução espacial

Strategi Peningkatan Produktivitas Kopi serta Adaptasi terhadap Variabilitas dan Perubahan Iklim melalui Kalender Budidaya

Abstrak. Rendahnya produktivitas kopi merupakan salah satu permasalahan utama dalam sistem produksi kopi Indonesia. Hal ini diantaranya disebabkan tidak adanya perawatan kopi yang optimal dengan memperhatikan fase fenologi kopi, serta dampak variabilitas dan perubahan iklim. Berbagai teknologi adaptasi kopi sudah banyak dihasilkan namun langkah adaptasi dengan memanfaatkan prakiraan iklim dalam bentuk penyesuian kegiatan budidaya dengan fase fenologi atau disebut sebagai kalender budidaya belum dikembangkan. Tulisan ini memaparkan tentang dampak variabilitas dan perubahan iklim pada tanaman kopi, teknologi adaptasi kopi yang sudah tersedia, perlunya pengembangan kalender budidaya kopi sebagai bentuk strategi adaptasi dan peningkatan produktivitas serta potensi dan tantangan pengembangan kalender budidaya kopi di Indonesia. Hasil review ini menunjukkan kalender budidaya kopi berpotensi dikembangkan sebagai strategi peningkatan produktivitas serta adaptasi terhadap variabilitas dan perubahan iklim. Abstract. Low productivity is one of the main challenges in Indonesia's coffee production system .It is low due to cultivation management; most of the coffee farmer does not manage their plantation base on the coffee phenology phase.  Moreover climate variability and change also have important effect on coffee productivity. Various technologies on adaptation and measurement to climate change and variability have been identified. Unfortunately, the technology which use climate forecast through adjusting cultivation activity and coffee phenology called as cultivation calendar do not exist yet. This paper provides an overview on the impact of climate variability and change to coffee production, the existing adaptation strategy, and the importance of cultivation calendar as a strategy for adapting and increasing productivity, and the potential and challenges to develop cultivation calendar in Indonesia. This review reveals that coffee cultivation calendar is a potential strategy for increaseing productivity and adapting climate change and variability

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

Remote Sensing Based Yield Estimation in a Stochastic Framework – Case Study of Durum Wheat in Tunisia

Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by the information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument.JRC.H.4-Monitoring Agricultural Resource

ESTIMASI PRODUKTIVITAS KOPI DENGAN INDEKS VEGETASI MENGGUNAKAN CITRA SPOT-7

Barang perdagangan paling berharga kedua di dunia adalah kopi, yang membantu devisa negara. Menurut data BPS (Badan Pusat Statistik) tahun 2021) terkait produksi kopi, Provinsi Jawa Timur menduduki peringkat ke-16 se-Indoneia untuk produksi kopi. Kebun Bangelan di Desa Bangelan, Kabupaten Malang, merupakan salah satu perkebunan kopi di Jawa Timur. Sebagai kebun percobaan, perkebunan ini didirikan pada tahun 1901. Pemantauan produksi kopi secara terus menerus diperlukan karena hasil Kebun Bangelan bervariasi dari tahun ke tahun. Dalam bidang pertanian, penginderaan jauh dapat digunakan untuk memperkirakan produktivitas tanaman kopi. Tindakan mengumpulkan informasi tentang suatu objek menggunakan perangkat yang tidak bersentuhan fisik langsung dengannya dikenal sebagai penginderaan jauh. Salah satu manfaat penginderaan jauh adalah perolehan data yang cepat, terutama di daerah yang menantang untuk dipelajari secara terestrial. Untuk penelitian ini, digunakan data Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Vegetation Index (MNDVI), dan Green Normalized Difference Vegetation Index (GNDVI). Untuk mengatasi kesulitan piksel campuran, Multiple Endmember Spectral Mixture Analysis (MESMA) adalah pengklasifikasi yang digunakan dalam penelitian ini. Tujuan dari penelitian ini adalah untuk menilai ketepatan klasifikasi tutupan lahan MESMA dan mempelajari seberapa produktivitas kopi di Kebun Bangelan. Dalam penelitian ini estimasi produktivitas dihitung dengan menggunakan regresi linier sederhana, polinomial, dan linier berganda. Model estimasi terbaik, sebagaimana ditentukan oleh perhitungan regresi linier sederhana, dihasilkan oleh NDVI, yang memiliki standar deviasi 505,875 kg/Ha dan produktivitas 34,396.369 kg/Ha. Model estimasi terbaik dihasilkan oleh NDVI dengan standar deviaasi sebesar 464,158 kg/Ha dan produktivitas sebesar 34,387.395 kg/Ha, menurut perhitungan menggunakan regresi polinomial. Model estimasi terbaik untuk perhitungan menggunakan regresi linier berganda adalah NDVI, yang memiliki standar deviasi 352,414 kg/Ha dan produktivitas 34,394.658 kg/Ha

Comparative analysis of MODIS time-series classification using support vector machines and methods based upon distance and similarity measures in the Brazilian cerrado-caatinga boundary

We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011–2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data