Smallholder agroecological subzones (AEsZs) produce an array of crops occupying large areas throughout Africa but remain largely unmapped. We explored multisource satellite datasets to produce a seamless land-use and land-cover (LULC) and fragmentation dataset for upper midland (UM1 to UM4) AEsZs in central Kenya. Specifically, the utility of PlanetScope, Sentinel 2, and Landsat 8 images for mapping coffee-based landscape were tested using a random forest (RF) classifier. Vegetation indices, texture variables, and wavelength bands from all satellite data were used as inputs in generating four RF models. A LULC baseline map was produced that was further analyzed using FRAGSTAT to generate landscape metrics for each AEsZs. Wavelength bands model from Sentinel 2 had the highest overall accuracy with shortwave near-infrared and green bands as the most important variables. In UM1 and UM2, coffee was the dominant cover type, whereas annual and other perennial crops dominated the landscape in UM3 and UM4. The patch density for coffee was five times higher in UM4 than in UM1. Since Sentinel 2 is freely available, the approach used in our study can be adopted to support land-use planning in smallholder agroecosystems.
Globally, the common dry bean varieties (Phaseolus vulgaris L.) are regarded as valuable food crops. Due to diversion-farm and postharvest management requirements, quick, reliable, and cost-effective varietal discrimination is critical for optimal management during growth and after harvesting. The large number of valuable wavelengths that characterize hyperspectral remotely sensed datasets in concert with emerging robust discriminant analysis techniques offers great potential for on-farm dry bean varietal discrimination. In this study, an integrated approach of partial least-squares discriminant analysis (PLS-DA) on hyperspectral data was used to determine the bean’s optimal timing for on-farm varietal discrimination. Based on experimental plots underirrigated and rain-fed watering regimes, hyperspectral data were collected at three major phenological stages. Data at each stage were first used to generate PLS-DA models to determine variable (wavebands) importance in the projection (VIP) and the VIP bands used to generate VIP conditioned PLS-DA models. The study identified 6 weeks (branching and rapid vegetative growth) and 10 weeks (flowering and pod development) after seed sowing as optimal stages for varietal discrimination. The study offers insight into the optimal period to discriminate dry bean varieties using spectroscopy, valuable for on-farm and after-farm management and crop monitoring sensor development.
Imaging spectroscopy can provide real-time high throughput information on growing crops. The spectroscopic data can
be obtained from space-borne, air-borne and handheld sensors. Such data have been used for assessing the nutritional
status of some field crops (maize, rice, barely, potato etc.). In this study a handheld FieldSpec 3 spectroradiometer in
the 350 - 2500 nm range of the electromagnetic spectrum was evaluated for its use to estimate sugarcane leaf nitrogen
concentrations. Sugarcane leaf samples from one variety viz., N19 of two age groups (4-5 and 6-7 months) were
subjected to spectral and chemical measurements. Leaf reflectance data were collected under controlled conditions and
leaf nitrogen concentration was obtained using an automated combustion technique (Leco TruSpec N). The potential
of spectroscopic data for estimating sugarcane leaf nitrogen status was evaluated using univariate correlation and
regression analyses methods with the first-order reflectance across the spectral range from 400 to 2500 nm. The variables
that presented high correlation with nitrogen concentration were used to develop simple indices combining reflectances
of 2-wavelengths. Simple linear regression was then used to select a model that yielded the highest R2. These were the
R744 / R2142 index for the 4-5 months old cane crop and the (R2200 - R2025) / (R2200 + R2025) index for the 6-7 months old
cane crop, with R2 of 0.74 and 0.87, respectively.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.