1. Introduction
International agreements like the Rio Convention, as well as national legislation and regional policies, require the management of rural areas both for agricultural production and for other uses (reduction of greenhouse gas emission, carbon sequestration, biodiversity conservation,
etc.) on a large scale. The last decade has seen the rapid development of research on the topic of ecosystem services and, increasing awareness of the economic value of ecosystem goods and services among decision-makers. Remotely sensed data offers a unique opportunity to provide environmental information with complete coverage, at different spatial and temporal resolutions. A key advantage of remote sensing is the capability to perform synoptic, spatially continuous and frequent observations resulting in large data volumes and multiple datasets at varying spatial and temporal resolutions [
1].
Farming practices directly affect the provision of ecosystem services. Mapping these practices is thus a challenge both for researchers in agro-ecology and decision-makers. Tillage systems for instance drastically impact on greenhouse gas emission by agriculture [
2], and influence the development of classification method for mapping tillage practices at a regional scale [
3]. In the case of sugar cane, mulching crop residues at harvest, instead of burning it, significantly contributes to sustained land productivity, increased organic matter in soils [
4], and decrease in greenhouse-gas emission [
5,
6].
The sugarcane landscape in Kenya is composed of small-scale farmers (below five hectares (ha)) whose land is heavily fragmented and large-scale farmers (over 5 ha), with both sugarcane and food crops in respective agronomic fields and a diversified crop calendar. A baseline survey by KESREF [
7] revealed that the minimum agronomic field size for a small scale holder was 0.2 ha with more than three food crops in the farm associated with high levels of crop rotation. Variability in crop type introduces variations in crop residues and harvest type within the same landscape. Characterizing cropping practices (type of crop and harvest mode) in such heterogeneous fields is thus important to ensure that information on cropping practices at field level is identified for enhanced planning of sugarcane census, harvesting and transport operations [
8]. We proposed to identify suitable remote sensing indices to map cropping practices (crop type and harvest mode) in a complex agricultural landscape in Kenya, based on free remote sensing images. Mapping cropping practices for the sugarcane landscape in Kenya will be crucial in providing area under sugarcane crop. Area under sugarcane is a critical function of the yearly cane census [
9] which is a key input in the planning process. The sugar industry therefore requires spatially explicit tools to provide reliable and precise information on area under sugarcane and location of sugarcane fields to improve accuracy in monitoring sugarcane production and yield estimates [
8,
10]. Moreover, in a landscape where 85% of sugarcane is grown among other land uses, mapping of cropping practices is vital in ensuring improved planning and management of the diversified natural resources. Additionally, mapping of cropping practices in a landscape that is diverse in topography is difficult especially where the spatial and temporal information of these practices is required unless modern tools are utilized.
Until today, mapping sugarcane acreage in Kenya is undertaken using manual methods while utilizing the theodolites and tape measure. This method although acceptable by the geospatial fraternity, is tedious, usually associated with disadvantages that accrue from manual methods such as; gross error, high costs for retaining officers in the fields and long periods that the exercise takes to complete, which contribute to inaccuracy in real time representation [
11]. Physical methods for such mapping approaches only capture spatial extend for accessible areas [
12]. Except the crop suitability maps in existence today, there are no maps showing the sugarcane cropped fields and/or harvest mode in Kenya and this constrains planning for farm inputs such as fertilizer distribution by the government. Moreover, inaccessibility of certain areas in the landscape reduces accuracy in physical approaches. It is therefore important to present precise estimations of such acreage using modern tools that are able to capture both temporal and spatial variations in sugarcane area that accrue from crop cycles and expansion of this production area.
Remote sensing data is sensitive to the land surface components (water, vegetation, soil) which can be used to map land cover, subsequently describing surface type and conditions in every point of space, on regular basis [
13]. Remote sensing imagery, when converted into useful information and integrated with data from geographical information systems (GIS), can increase accuracy in monitoring spatial and temporal dynamics as well as crop development [
10], and provide area measurements. Recent studies have used remote sensing images to map sugarcane fields through automatic classification of Landsat images [
14], through a rule-based classifier applied to SPOT image time series [
15] or through an object based image analysis (OBIA) approach on high resolution image time series [
16] to categorize sugarcane fields into similar age units. In countries where sugarcane is distributed over large areas and in large fields, like in Brazil, Thenkabail
et al. [
17] suggested the need for automated methods to map sugarcane fields and other land uses using time series Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m images, coupled with Landsat 30 m images.
In Kenya where sugarcane fields are small, 30 m Landsat image can be useful in locating sugarcane fields of similar age. In the recent past, Landsat 30 m Normalized Difference Vegetation Index (NDVI) has facilitated exploration of the spatial variability of heterogeneous landscapes [
18]. However, the multiple planting and harvesting calendar [
19] in Kenya complicates land use mapping, necessitating the need for temporal series of satellite images for identification of which fields contain sugarcane [
13,
20]. The combination of varied spatial resolutions and temporal satellite images in mapping such fields minimizes inaccuracies in mapping disparate fields from a single image. To a large extend, although the Kenya sugar industry prohibits burnt cane harvesting because it depletes soil nutrients [
21], this practice is still rampant in sub humid agro-ecological zones and on small scales in humid areas, usually being attributed to the need to maximize on harvested stalk [
22]. Moreover, if burnt stalk is not harvested and milled within 48 h, the sucrose is usually destroyed, limiting the possibility to extract sugar that is already produced in deficit for the nation’s demand [
8]. Characterizing this harvest mode is therefore important for improved decision-making, planning for harvesting operations and enhanced sugarcane productivity. In Brazil, the MODIS images facilitated detection of sugarcane harvest and harvest mode using the Normalized Difference Vegetation Index (NDVI) [
23]. Lebourgeois
et al. [
24] showed that the signal measured in the Short Wave InfraRed band (SWIR) was a good indicator of the presence of sugarcane harvest residues on the ground. Production area in western Kenya, displays a heterogeneous landscape due to different crop types, different cropping calendars and sugarcane harvest mode. Because of its ability to distinguish fields with sugarcane from other crops through time and different cropping practices, we used a complete year time series of Landsat8 images to develop an original method for mapping the spatial and temporal dynamics in the landscape; and ground data for classification training and validation.
4. Conclusions and Perspectives
This research has investigated the spatial and temporal information contained in the satellite images in terms of cropping practices in a sugarcane-based cropping system.
The harvest mode map was obtained using an original method through a t-test, which found Landsat normalized difference water index (NDWI) values for green and burnt harvest significantly different. NDWI distinguishes bare soil from vegetation residue after harvest by segregating dry and humid surfaces that result from burnt and green harvest respectively. Detection of harvest mode using NDWI is therefore a new idea, which this study has developed to characterize harvest mode. The harvest map will be used to plan for sensitization forums on best management and environmental practices.
Moreover, Landsat NDVI has shown great potential for detecting crop type, crop conditions (harvested or growing) and mapping sugarcane cropped areas for medium sized farms over 1 ha in Kibos-Miwani. Farms that are less than 1 ha are however difficult to map at this image scale (15–30 m). The sugarcane map prepared in this study will be used as basis for precise acreages for increased accuracy in yield forecasting. To date, yield forecasting has been based on the Stack processed/planted Area regardless of where such field is located. The method developed in this study emphasizes on yield in a geographical zonation by remote sensing. Precise measurements will inform better planning decisions for the sugar industry operations [
20] towards environment-friendly management of production areas. Moreover, NDWI will be used in precise mapping of sugarcane harvest modes. In the past, efforts by the sugar Industry to dissuade farmers on burnt harvest mode in Kenya have been limited by lack of techniques in mapping the practice.
Recent Earth Observing satellite systems, such as Sentinel-2 (S2 ESA), with decametric spatial resolution, and a high visiting frequency (10 days in 2015, and 5 days in 2016), will give access to farm level information. This S2 ESA satellite mission will also benefit for sugarcane mapping that is presently done using Landsat time series, with a resolution that is able to capture boundaries of nucleus fields, but not for small growers.